public private partnerships in healthcare. evaluation of 10 years´ experience in spain

Ph.D. Program in Business Administration

27th October 2014

Public-Private Partnerships in Healthcare. Evaluation of 10 years’ experiencein Spain.

Doctoral ThesisAntonio Clemente

Directors:

David Vivas Maria CaballerISBN: 978-84-606-8865-5

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VALENCIA POLYTECHNIC UNIVERSITY

PhD in Business Administration

Public-Private Partnerships in Healthcare. Evaluation of 10 years’ experience in Spain

Author: Antonio Clemente

Directors: PhD. María Caballer

PhD. David Vivas

October, 2014

1

To my parents and brother for their constant encouragement and unconditional support to all my projects.

To my uncle Jose María for his charisma and particular way of seeing life.

To my Coach for his advice and, above all, for his trust in me.

To Jorge and my colleagues at Marina Salud because they gave me

the opportunity to develop the necessary tools to conduct this study.

Dénia has always been, is, and will, be my school in healthcare management.

To my directors María and David for their patience and devotion to this study.

2

INDEX

3

INDEX

INDEX 3

ABSTRACT 13

1. INTRODUCTION AND OBJECTIVES 18

1. Introduction 19

1.2 Research objectives 23

1.2.1 General objective 23

1.2.2 Specific objectives 23

2. BACKGROUND 25

2.1 Spanish healthcare context 26

2.2 Healthcare context in the Valencia region 302.2.1 The Alzira model 33

2.2.1.1 Basic concepts of the Alzira model 38

2.2.1.2 The beginnings of the Alzira model: La Ribera Hospital 43

2.3 A review of the literature on healthcare service assessments 45

2.3.1 Concept and measurement techniques of efficiency in the hospital industry 47

2.3.2 Efficiency analysis method 49

2.3.2.1 Multivariate methods 502.3.2.2 Non-stochastic methods 52

2.3.3 Hierarchical analysis. Clusters 57

2.3.4 Diagnosis related groups (DRGs) 592.3.4.1 Origin of the DRGs 60

4

INDEX

2.3.4.2 Development of the DRGs 61

2.3.4.3 The weightings of the DRG 61

2.3.4.4 The "product" that a hospital provides 64

2.3.5 Public-private collaboration experience in healthcare 66

3. ASSUMPTIONS AND INFORMATION SOURCES 73

3.1 Assumptions for the study 74

3.2 Information sources 74

3.2.1 Sources of economic information 77

3.2.2 Quality information sources 82

3.2.3 Information sources for the healthcare production 84

3.3 Indicators and variables used 87

3.3.1 Economic or cost variables 87

3.2.2 Quality variables 913.2.2.1 Quality indicators 92

3.2.2.2 Delay indicators 93

3.2.2.3 Qualitative economic indicators 97

3.2.2.4 Healthcare process indicators 97

3.2.2.5 Public health indicators 99

3.2.2.6 Safety indicators 100

3.2.3 Structural variables 102

3.2.4 Variables in healthcare activity 106

5

INDEX

3.3. Cost breakdown method 110

4. RESULTS 113

4.1 Cost analysis 114

4.1.1 Main healthcare indicators 114

4.1.2 Cluster analysis 118

4.1.3 Overall hospital costs in the Valencia region 120

4.1.4 Cost per equivalent patient and area 122

4.1.5 Healthcare production in equivalent patients by area 130

4.1.6 Assessment of the activity through an adjusted cost-production analysis 137

4.2 Analysis of the healthcare quality 143

4.2.1 Quality Analysis in Emergency Department 145

4.2.2 Quality analysis in the surgical area 148

4.2.3 Quality Analysis in the outpatient area. 151

4.3 Analysis of the healthcare activity 154

4.3.1 Overall healthcare production 155

4.3.2 Healthcare production in the medical area 159

4.3.3 Healthcare production in the surgical area 162

4.3.4 Healthcare production in outpatient services 166

4.3.5 Healthcare production in the emergency department 169

4.4 Study of the effect of the management model 172

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INDEX

4.4.1 Total differences 172

4.5 Assessment of the efficiency between the PPP and the directly managed hospitals 177

4.5.1 Overall efficiency 177

4.5.2 Efficiency in the medical area 182

4.5.3 Efficiency in the surgical area 186

4.5.4 Efficiency in the outpatient service area 190

4.5.5 Efficiency in the emergency department area 194

5. DISCUSSION 199

5.1 Contribution to knowledge and new lines of research 221

6. CONCLUSIONS 224

7. BIBLIOGRAPHY 234

8. ANNEXES 249

7

TABLE INDEX

Table 1. Global competitiveness index 27

Table 2. Total budget and spending in Spain (million euros) 28Table 3. Healthcare budget per capita in 2003-2012 31

Table 4. Population in the Valencia region 32Table 5. Overall budget for the Valencia region and budget for its Health

Department. 34Table 6. Main direct expense items for H10 90

Table 7. Equivalency of the processes to calculate the equivalent patients121

Table 8. Main hospital activity indicators in 2010 115Table 9. Main indicators of ambulatory production in 2010 117

Table 10. Relative position of the Management Agreements in 2010 144Table 11. Results of the overall analysis using the general linear regression

model. 158Table 12. Results of the general linear regression analysis in the medical

area 161Table 13. Results of the general linear regression analysis in the surgical

area 165Table 14. Results of the general linear regression analysis in the outpatient

service area 168Table 15. Results of the general linear regression analysis in the emergency

department area 171Table 16. Statistics for the mean difference 173

Table 17. Results of the Mann-Whitney test 174Table 18. Variables selected for the overall efficiency analysis 178

Table 19. Score overall efficiency 180Table 20. Overall efficiency score for cluster 1 181

Table 21. Overall efficiency score for cluster 2 182

8

TABLE INDEX

183

184185

186187

188189

190191

192193

194

195196

197

Table 22. The variables selected for analyzing the medical area

Table 23. Inpatient efficiency score

Table 24. Efficiency score in the inpatient area for Cluster 1

Table 25. Efficiency score in the inpatient area for cluster 2

Table 26. The variables selected for analyzing the surgical area

Table 27. Efficiency score in the surgical area

Table 28. Efficiency score in the surgical area for cluster 1


Table 30. The variables selected for analyzing the outpatient area

Table 31. Efficiency score in the outpatient service area

Table 32. Efficiency score in the outpatient area for cluster 1

Table 33. Efficiency score in the outpatient service area for cluster 2

Table 34. The variables selected for analyzing the emergency department

area

Table 35. Efficiency score in the emergency department area

Table 36. Efficiency score in the emergency area for cluster 1

Table 37. Efficiency score in the emergency department area for cluster 2

9

198

GRAPHIC INDEX

Graphic 1. Healthcare spending as a percentage of GDP in 2007 22

Graphic 2. Healthcare spending by source and funding in 2013 29

Graphic 3. Public healthcare spending breakdown in 2012 30

Graphic 4. Population pyramid in the Valencian region in 2010 33

Graphic 5. Population breakdown by healthcare district 36

Graphic 6 Healthcare concessions in Spain 37

Graphic 7. Main principles of the Alzira Model 39

Graphic 8. Characteristics and correction factors of the per capita funding system 41

Graphic 9. Dendogram showing the clusters 119

Graphic 10. Overall direct cost per hospital in 2010 (in millions of euros)121

Graphic 11. Cost per equivalent patient in inpatient care area in 2010123

Graphic 12. Cost per equivalent patient in the surgical area in 2010126

Graphic 13. Cost per equivalent patient in the outpatient service area in 2010127

Graphic 14. Cost per equivalent patient in the emergency department area in 2010 129

Graphic 15. Equivalent patients in the medical and surgical area en el área in Cluster 1 131

Graphic 16. Equivalent patients in the outpatient service area in cluster 1132

Graphic 17. Equivalent patients in the Emergency department in Cluster 1 133

Graphic 18. Equivalent patients in the medical and surgical area in Cluster 2 134

10

GRAPHIC INDEX

Graphic 19. Equivalent patients in the outpatient service area in Cluster 2 135

Graphic 20. Equivalent patients in the Emergency department in Cluster 2 136

Graphic 21. Cost of the surgical and medical area compared to the equivalent patients 138

Graphic 22. Inpatient cost compared with the equivalent patients 140

Graphic 23. Cost of operating rooms compared with the equivalent patients142

Graphic 24. Quality analysis in the emergency department 146

Graphic 25. Quality analysis in the surgical area 149

Graphic 26. Quality analysis in the outpatient area 152

Graphic 27. Linear regression analysis of the cost and overall equivalent patients 156

Graphic 28. Linear regression of the cost and equivalent patients in the medical area 160

Graphic 29. Linear regression of the cost and equivalent patients in the surgical area 163

Graphic 30. Linear regression of the cost and equivalent patients in the outpatients area 167

Graphic 31. Linear regression of the cost and equivalent patients in the emergency department area 170

Graphic 32. Adjusted cost per capita in the Valencia region 211

11

ABSTRACT

ABSTRACT

12

ABSTRACT

Public- Private Partnerships in Healthcare. Evaluation of 10 years’

experience in Spain

Health is one of the fundamental human rights, which is included in

the World Health Organization's Constitution of July 1946:

• The enjoyment of the highest attainable standard of health is one

of the fundamental rights of every human being.

• The right to health includes access to timely, acceptable, and

affordable health care of appropriate quality.

• The right to health means that every country must generate

conditions in which everyone can be as healthy as possible.

Healthcare is also one of the fundamental mainstays of the welfare

state in developed countries. Citizens' health is an essential objective

of each country, although it requires special attention and analysis

from an economic standpoint to ensure universal access and

sustainability.

In the last decade, healthcare management options different to the

conventional ones have been developed with the aim of ensuring

good healthcare quality and optimizing public resource utilization.

Among these options, one of the models that has attracted greater

interest in Spain is the administrative concession or Public Private

Partnership (from now on PPP) .

13

ABSTRACT

The first hospital in Spain to operate under the administrative

concession was in Alzira (Valencia), after the Valencia regional

government approved Law 15/1997 of 25 April, which enabled new

forms of management. Therefore, it is a public hospital that is

managed by a private company and provides specialist and primary

healthcare to a reference population through an economic

agreement, that establishes a fixed fee for each allocated patient,

undertaking to make the necessary investments in infrastructure and

equipment.

Control methods were jointly established with the Administration in

terms of compensation payments regarding the patients treated

outside the concession and those who are cared for without belonging

to it.

This model was later extended to a total of five districts in the

Valencia region and implemented in other regions under the name of

the "Alzira Model”.

Objec&ve

This PhD dissertation is aimed at analyzing the influence of the

health management model (direct management or PPP) in terms of

economic efficiency and healthcare quality.

14

ABSTRACT

Methodology

The data for the analyses were obtained from the information sources

of the Valencia Health Department for public hospitals. The

economic data for the PPPs (Alzira, Dénia, Torrevieja, Elche and

Manises) were provided directly by the hospitals themselves. In both

cases, the data refers to 2009 and 2010.

The variables included in the analysis are as follows: costs per

procedure, quality indicators, activity indicators and structural

indicators.

To compare efficiency and the factors influencing it, we performed

multivariate and non-stochastic analyses.

We also performed a hierarchical cluster analysis to group and

classify the hospitals in the Valencia region in a standardized way.

We used the data envelopment analysis (DEA) to divide hospitals into

efficient and inefficient in terms of management (direct or PPP),

overall and by specific unit in the surgical, inpatient, outpatient and

emergency department areas as well as by cluster. This was combined

with the quality and activity indicators and outputs.

15

ABSTRACT

Results

We obtained the following results in the study:

- The fact of being a hospital run by a PPP implies that they have a

lower cost than the rest of the other hospitals in the sample.

- The cost analysis by patient, adjusted for the case mix, shows that

the PPPs have lower than average costs in the surgical and outpatient

service areas. In the inpatient area, the PPPs have higher than

average costs, but all of them were considered efficient in the DEA

when quality indicators where included.

- In the emergency department area, one of the analyzed PPPs has

higher than average costs. These results were significant in the

regression analysis.

- The PPPs scored better in the quality indicators that were analyzed.

- In the overall DEA, two of the three PPPs obtained maximum

efficiency. Nine of the nineteen directly managed hospitals that were

analyzed achieved this level.

Conclusions

The performance and efficiency analyses show that the group of

PPPs outperformed the average for the directly managed hospitals,

but they were not always better.

16

ABSTRACT

The results of this dissertation will provide a sound basis for the

future research of economic assessments for healthcare management.

Nevertheless, new studies should be conducted that include a larger

number of hospitals which use the public-private collaboration

model.

17

1. INTRODUCTION

AND OBJECTIVES

18

CHAPTER 1 INTRODUCTION AND OBJECTIVES

1. Introduction

Healthcare is one of the most complex and dynamic industries in our

society. Its function is to look after individuals' health in an

increasingly complex situation as a result of users' growing demands,

social pressure, high-cost technology and scientific advances, plus

highly qualified professionals who constantly refresh and update

themselves. Healthcare accounts for 8% of the world GDP (Spanish

Health Ministry, 2008).

In the European Union, the public healthcare systems are

characterized by the following:

- Universal coverage for the population through social security systems.

- Funding through the taxes accrued based on income.

- Coverage of hospital and pharmacy benefits through prescriptions.

- Control tools to maintain the system's sustainability.

Healthcare is one of the most complex and dynamic industries in our

society since it looks after our most valuable possession: our health.

Two forms of funding coexist in Europe: the Bismarck model (funded

with the social security system) and the Beveridge models funded by

taxes, as in the case of the Spanish National Health Service (Freire,

2006).

19


As a result of financial austerity and an ever-increasing demand for

resources, the world's healthcare organizations and systems are

currently facing a major challenge (Walshe and Smith, 2011).

Evans (2005) drafted three basic fundamental questions which should

lead to healthcare reform and, therefore, to a possible change in the

management model.

1. Who funds healthcare and how much does it cost?

2. Who receives healthcare, what type of healthcare is received, when

should it be received and who is responsible for providing it?

3. Who gets paid for providing the service and how much?

Evans suggests that the conflict between the healthcare stakeholders

is usually because of their different views on how to answer such

questions.

Healthcare users, regardless of how they are called (patients,

consumers or customers), cannot be compared to consumers of other

public services or to clients of a service provided by a private

company:

Firstly, because the existing information between patients and

healthcare providers is asymmetrical; very few patients can contradict

20


a doctor's recommendation or treatment, no matter how qualified or

informed they are.

Secondly, patients are generally emotionally vulnerable, so they are

unlikely to act independently or assertively, which is usually the case

in other public or private services.

Therefore, the healthcare organizations and their managers have an

additional responsibility to offset the unequal situation of the patients

being cared for in the health system since, in the end, all the

hospitals, whether they are public, private or administrative

concessions, share the same concern about their wellbeing.

These are clearly changing times in the world healthcare scenario,

where it will be necessary to innovate and implement new resource

management methods (Drucker, 2006). In some countries, the

governments have recently increased their control, as in the case of

Mexico and Colombia, which have implemented a social security

system (Guerrero et al., 2011).

21


Graphic 1. Healthcare spending as a percentage of GDP in 2007

Source: Spanish Healthcare Ministry, 2008

Graphic 1 shows the breakdown of healthcare spending as a

percentage of GDP in the OECD countries in 2007, based on

whether it is funded by the public or private system. The main

feature is the weighting of public funding in all the countries, even in

the United States.

22


1.2 Research objectives

1.2.1 General objective

The main objective of this PhD dissertation is to analyze and

compare the influence of the public or direct healthcare

management model with the public-private partnerships from the

standpoint of healthcare quality and economic efficiency.

1.2.2 Specific objectives

1. Analyze the existing literature in Spain and abroad with the aim of

identifying the main specific variables for benchmarking.

2. Select the most significant variables for constructing indicators

with the aim of measuring the efficiency and quality of healthcare

organizations.

3. Allocate a standard measurement with the aim of comparing the

hospitals and the cost breakdown.

4. Group the hospitals based on their structural resources and

healthcare production capacity.

5. Find the variables to explain the cost for each hospital area by

using a regression analysis.

23


6. Rank the relative efficiency of the hospitals and management

models based on this study.

24


2. BACKGROUND

25

CHAPTER 2 BACKGROUND

2.1 Spanish healthcare context

Spain is currently in a critical economic context due to record

unemployment rates. As a result of this, plus an increase in life

expectancy (boosting the number of pensioners) and the excessively

large amount of civil servants (inherited from past economic booms),

the Administrations' current expenses are substantially higher than

their revenues (Arenas, 2013).

These factors have created a considerable budgetary tension and, as a

result, the regional policies prioritize the spending allocated to

maintaining the services rather than investing in infrastructure. As a

direct consequence, Spain has fallen considerably in the global

competitiveness index drafted by the World Economic Forum (2013),

as shown in table 1.

This chapter reviews the healthcare's economic aggregates within the

nationwide and regional context in Spain. It also summarizes the main

characteristics of the public-private partnership agreements. Lastly, it shows the

main Spanish and foreign contributions to healthcare service assessments.

26


Table 1. Global competitiveness index

Source: Schwab in the World Economic Forum, 2013.

As can be seen, Spain dropped 20 places in this index. In 2002, it was

ranked 22nd, higher than countries like France, while in 2010, it fell to

42nd.

This loss of competitiveness could jeopardize some of the

cornerstones of the welfare state: pensions, education, social services

and healthcare (Ochando, 2009).

Therefore, it is logical that one of the main debates right now is how

to cope with the growing healthcare spending (Table 2) while

establishing the necessary measures to control resource utilization,

which is somehow not being materialized but cannot be deferred any

longer.

Country/

Year2002 2007 2009 2010

USA 1 1 2 4

UK 11 12 12 12

Germany 14 7 7 5

Spain 22 29 29 42

France 30 16 16 15

Italy 39 49 49 48

27


Table 2. Total budget and spending in Spain (million euros)

Source: the author, based on Arenas et al. (2013) and Informe Nacional de Salud 2012

Nevertheless, despite the economic situation, the Spanish healthcare

system is still considered to be one of the best in the world. This is

evidenced in the studies by Gay et al. (2011), which analyze avoidable

mortality, where Spain is ranked among the top. It is considered to be

the reference indicator for healthcare quality (Nolte and McKee,

2008).

Therefore, we believe that it would be interesting to quantify (broadly

speaking) healthcare in Spain firstly and then in the Valencia region,

so that we are aware of the magnitude and need to implement

measures to improve the system's management and efficiency.

Year Budget Total spending

Absolute deficit

Deficit (%)

2007 52,383 64,339 11,956 22.82

2008 56,559 71,170 14,611 25.74

2009 58,960 75,395 16,435 27.87

2010 59,738 74,732 14,956 25.10

Total 227,640 285,636 57,996 25.48

28


In 2010, the Spanish Federation of Associations for the Defense of

Public Health highlighted the difficulty in maintaining annual

increases of 10% in the government healthcare budgets (Federation

of Associations for the Defense of Public Healthcare, 2010).

According to García et al. (2010), healthcare spending is funded

mainly by government taxes, as can be seen in the healthcare

spending breakdown by source in 2013 (Graphic 2), where 71% is

used mainly for the headings stated in the preceding paragraph and

for medicines.

Graphic 2. Healthcare spending by source and funding in 2013

Source: Spanish Ministry of Health, Equality and Social Affairs,2013. .

Other9%

Pharmacy20%

Primary Care16%

Specialized care55%

Copayment23%

Insurance6%

Taxes72%

29


2.2 Healthcare context in the Valencia

region

Graphic 3 shows the healthcare spending (5.49 billion euros)

breakdown by source in 2012, where the main headings are staff and

pharmacy expenses, in terms of both hospitals and prescriptions.

Graphic 3. Public healthcare spending breakdown in 2012

Source: the author, based on the Valencia Health Department's budget for 2012..

8%8%

7%

15%

4%11%

47%

Staff PPP ProstheticsHospital pharmacy Pharmacy prescriptions Healthcare materialsGeneral expenses

30


As can be seen in table 3, all the regions increased their healthcare

budget per capita in the 2003-2012 period, although there are

differences among them.

In 2012, the Valencia region had the lowest healthcare budget per

capita (1,110 euros) in Spain, i.e. 52% lower than Extremadura, the

region with the highest (1,692 euros).

Table 3. Healthcare budget per capita in 2003-2012

Source: the author, based on the Spanish Ministry of Healthcare, Equality and Social Services, 2012

Some factors had a strong impact on the increase in healthcare

spending in the Valencia region:

• The 25.52% growth in the registered population between

1999 and 2013 (Table 4).

31


Table 4. Population in the Valencia region

Source: the author, based on the NaKonal StaKsKcs InsKtute, 2013.

• A change in the demand structure: the ageing of the Spanish

and registered foreign population (graphic 4) plus an increase

in chronic diseases have had a direct impact on healthcare

spending. Also, as a result of the access to healthcare

information, patients now demand more from their doctors

than ever before.

32


Graphic 4. Population pyramid in the Valencia region in 2010

Source: Valencia Region Healthcare Plan through the PopulaKon InformaKon System (SIP) data, 2013

In 2010, healthcare spending amounted to 5.72 billion euros in the

Valencia regional government's budget (39.7% of the total). Despite

the overall reduction in the regional government's budget, the

percentage for healthcare has remained stable at around 40% since

2007 (Table 5).

Spanish males Spanish females Foreign femalesForeign males

33


Table 5. Overall budget for the Valencia region and budget for its Health Department.

Source: Valencia Regional Budget Act, 2012

The current funding system has not yet implemented any changes to

offset this situation: users maintain their status without contributing

their part to the service provided, and demand is higher than if it

were regulated by market forces. As a result of the imbalance

between supply and demand, there is a delay in the medical

treatment given to users.

Most of the public healthcare services are supplied by the institutions,

whose political responsibility depends on the elections, so the

decisions are usually very biased.

With the aim of improving the healthcare services' economic

efficiency without jeopardizing their quality, an innovative public-

2007 2008 2009 2010 2011 2012

Valencian Regional Budget

12.893 13.828 14.286 14.392 13.713 12.784

Healthcare Budget 5.089 5.454 5.659 5.720 5.515 5.492

Healthcare as a % of the total regional budget

39,4% 39,5% 39,6% 39,7% 40,2% 39,9%

34


private partnership plan was implemented in 1997 both in the

Valencia region and in the rest of Spain.

2.2.1 The Alzira model

The Abril Report (Abril Martorell, 1991) analyzes the National

Health System's challenges and proposes ways in which to make the

system more viable and efficient in the future. The report provides a

novel concept: it separates healthcare funding from the service

provision.

The Valencia regional government culminated the legal reform

process that began with Law 15/1997 of 25 April on new forms of

management, which "opens up the healthcare services with any legal

form allowed by law". As a result of this law, the first hospital was

created under an administrative concession in Spain in 1999: La

Ribera Hospital.

At present, five healthcare districts are partially operated by private

insurance companies under the so-called "administrative concession",

which handles approximately 20% of the Valencia region's

population, as can be seen in graphic 5, which shows the number of

reference patients by healthcare district.

35


Graphic 5. Population breakdown by healthcare district

Source: Healthcare Plan through the PopulaKon InformaKon System (SIP), 2013

In the administrative concession agreements, most of the district has

public funding but is managed by a private company. The

agreements are awarded via government tenders (Caballer et al.,

2009).

The public-private partnership model, which includes managing the

healthcare staff, is present in both the Valencia and Madrid regions.

Graphic 6 shows a breakdown of the hospital bed numbers and the

year in which the hospitals were opened.

36


Graphic 6. Healthcare concessions in Spain

Source: Spanish InsKtute for Healthcare Development and IntegraKon, 2013

Therefore, the five healthcare districts operated under an

administrative concession in the Valencia region are: Alzira,

Torrevieja, Dénia, Manises and Elche-Crevillente.

An administrative concession is an agreement that manages the

healthcare service of the reference population. Its purpose is to

provide comprehensive primary and specialist healthcare to the

population and it is funded by a premium per capita; the movements

of the protected population are invoiced, as well as the possible

patients from outside the district (graphics 7 and 8). The agreement

Number beds

Number beds

Number beds

Inauguration

Inauguration

Inauguration

Hospitals Others

Inauguration

37


lasts for 15 years and can be extended to 20 years. The activity is

supervised by the Administration through the Valencia Health

Department's Commissioner (De Rosa and Marín, 2007).

This management model includes the basic principle of separating

the funding made by the public sector from the service provided by

the private sector, as set out in the Abril Report. In this case, the joint

venture that was awarded the concession agreement is responsible for

providing the service. The public sector owns, funds and controls the

healthcare service while the private sector provides the service itself,

respecting the principles that it must be a free, quality, efficient and

equitable service. One of the advantages of the model for the

regional administration is that it provides the healthcare network with

a quality public service without having to make any initial

investments and where the future costs are known and can be

planned (Tarazona et al., 2005).

2.2.1.1 Basic concepts of the Alzira model

The model is named after the Valencian town of Alzira in the La

Ribera area, where the first hospital of this type was built.

This health management model is based on the following principles

(graphic 7):

38


Graphic 7. Main principles of the Alzira model

Source: the author, based on De Rosa and Marín, 2007

The point of view of the stakeholders in the public-private

partnership agreements (the Public Administration and the awarded

company) is summarized as follows:

The Public Administration

The following main points are deduced from the agreements'

specifications::

q The investment cost is borne by the awarded company.

39


q If there is staff that belongs to the public administration

(statutory civil servants), their cost must be borne by the

concession company. Such services are compensated between

the concession company and the public administration,

including the social security costs.

q If the healthcare services required by the citizens allocated to

the district are not available, so they need to be taken

somewhere else or rerouted, the Law on Rates (Ley de Tasas)

are applied since it indicates the cost of such treatments, as in

the case of transplants, which are very complex treatments that

must be applied at the reference hospitals.

In both cases, the statutory civil servants and the rerouting costs form

part of the services that must be compensated between the

concession company and the Administration during the concession

period.

The awarded company

a) Funding

q Per capita: this is the amount that the company receives for

each citizen allocated to the district. This premium is updated

annually and the increase cannot exceed the average increase

for the other regions and it must be at least the consumer price

40


index (CPI). In 2014, the per capita funding was 660 euros per

citizen in each district.

Graphic 8. Characteristics and correction factors of the per capita funding system

Source: the author

q Census: the PPP shares the population information system

(SIP) with the Administration; this system determines the

number of people and their basic contact data. Thanks to this

system, the district and the Health Department can monitor

the patients and invoice the account of the patients treated at

other hospitals. It applies the number of people at September

30, even if this figure fluctuates during the year.

41


q Other revenue sources: the concession company can invoice

the services provided to patients outside the protected

population. The price for providing the service has a discount

with respect to the Law on Rates: 20% in the case of Alzira

and 15% in the other concessions (see graphic 8).

The concession company can provide services to patients who belong

to insurance companies and work-related mutual societies,

establishing the rates with the company in question or applying the

rates for traffic accidents.

The specifications state that the concession company will not be

compensated for the patients covered by the Valencia Health Agency

dealt with in the primary healthcare centers who do not form part of

the reference population.

b) Investments

At the end of the PPP agreement, the concession company

undertakes to deliver all the used assets to the Administration.

c) Maximum profitability

The PPP cannot earn more than a 7.5% profitability. If this occurs,

the concession company is obligated to return that surplus to the

Administration by investing it in healthcare.

42


2.2.1.2 The beginnings of the Alzira model: La Ribera

Hospital

The first hospital under an administrative concession began at the

start of 1999. This significant event took place in La Ribera, the

former healthcare district number 10.

The model was faced with specific determining factors in terms of

both the social and economic conditions, including the following:

1. The additional problem of being the first hospital.

2. The establishment of a stable link between the Health

Department and the PPP.

3. The controversy in the public health system of a new

healthcare management formula.

4. The pressure from the media, political establishment and

trade unions.

The preceding factors were present in the social context and can be

classified as threats.

On the other hand, the model was faced with the following

challenges:

1. It would be difficult to manage only specialist healthcare.

43


2. Since it was the latest user to enter the system, it was

expected to reach optimal quality levels right from the start.

3. The economic variables were adjusted for the premium per

capita and for a short concession period (10 years) in

principle.

4. The industrial relations needed to be particularly analyzed

because of their importance in each concession agreement

under the Alzira model. Specifically, La Ribera Hospital had

to face the following factors:

✓ For the first time in the Spanish health system, the concession

company's staff had to work with the statutory civil servants at

the same hospital.

✓ A variable economic supplement was established based on

objectives.

The initial objectives of La Ribera Hospital were conditioned by

some "needs": firstly, the need to meet the population's demands; and

secondly, the need to prove that the new healthcare management

system was viable.

Therefore, La Ribera Hospital's strategy had to include concepts like

implementing competitive differentiation factors, providing added

44


value to its patients, creating the smallest possible conflict and taking

advantage of the private management tools.

2.3 A review of the literature on

healthcare service assessments

To make more reliable decisions, tools should also be used in the

healthcare industry that facilitate management and, in turn, provide

greater knowledge of the process efficiency.

Nevertheless, there are impressive studies such as the one conducted

by Holmberg and Rothstein (2011) which conclude that, after

analyzing the data in 120 countries, efficiency takes place when

public resources are well managed, i.e. when commissions in

developed countries and bribes in poor countries are eradicated and

when there is transparency in information and management based

on rational and not arbitrary decisions.

It has also been shown that the healthcare indicators do not have a

direct correlation to healthcare spending. In other words, higher

spending does not necessarily mean better results in health (a higher

life expectancy or a lower death rate) based on a threshold (The

National Academies, 2013).

45


One of the usual ways to assess performance is using an indicator-

based instrument, i.e. a Balanced Scorecard (BSC), and a non-

stochastic method to assess efficiency, i.e. the data envelopment

analysis (DEA) (Amado et al., 2012). The DEA has been widely used

in the healthcare sector, where Hollingsworth's (2008) review of the

literature stands out. Another very interesting contribution is the

assessment using the methodology proposed by Ballestero and

Maldonado (2004).

We also researched the multiple objective programming methods to

determine the efficient frontiers that combine the achievements in

quality and costs, as proposed by Romero (2004) in other fields.

It was not until 1988, with the work of López-Casasnovas and

Wagstaff, that the efficiency of Spanish hospitals began to be

measured, although it was not until three years later, with the work of

Ley (1991), that the DEA was first applied to assessing a sample of

Spanish hospitals. For example, at regional level, the efficiency of the

hospitals in Galicia was analyzed by Seijas and Iglesias (2009), who

analyzed the hospitals belonging to the Galician Health Service

between 2001 and 2006.

Outside Spain, the first ones to apply the DEA to the hospital

industry were in the United States: Sherman (1984); Banker et al.

(1986); and Grosskopf and Valdmanis, (1987).

46


The work by Puig Junoy and Dalmau (2000) and Cabasés et al. (2003

and 2007) provided evidence in this area since they made a thorough

review of the literature on the efficiency of hospital organizations in

Spain. Rodríguez-López and Sánchez Macías (2004) also made their

contribution by assessing the efficiency of the specialist healthcare

system in Spain.

The reference work regarding the stochastic efficiency frontier in

healthcare organizations was conducted by O'Neill et al. (2008), who

reviewed 79 studies using this technique, and Hollingsworth (2008),

who completed the study by reviewing 317 articles based on

measuring productivity and efficiency at hospitals, while also using

the frontier techniques.

2.3.1 Concept and measurement techniques of

efficiency in the hospital industry

"Hospitals or hospital areas must be oriented towards reaching

optimal results with a determined resource level". The first author to

introduce this concept in the literature was Debreu (1951).

Authors of economic theory take into consideration different options

when focusing on the hospitals to be assessed that will be used for

measuring efficiency. The two most usual functions are healthcare

costs and production, and these two variables are the usual ones that

47


are used to determine and define efficiency. The production frontier

determines the maximum output that can be made based on a

certain input level. In terms of costs, it represents the minimum

economic cost with which a certain output can be produced.

When measuring efficiency in economic and objective terms, we are

referring to the overall or economic efficiency which, in turn, are

divided into overall and allocative technical efficiency. In the former,

it measures the relationship between optimal inputs and outputs; in

the case of allocative efficiency, they are the output combinations at

the price level.

The starting point for measuring overall efficiency is the methodology

presented by Debreu and Farrell (1951), which is still used at present

to assess the efficiency of hospitals and other production units.

Broadly speaking, we can make a distinction between non-frontier

methods, econometric models and other models where an optimal

reference needs to be established, and frontier methods where non-

parametric and parametric models need to be differentiated.

One of the most common methods used at healthcare organizations

is the Data Envelopment Analysis (DEA), which is the reference non-

parametric technique. The calculations are made using linear

programming since it is not necessary to establish a reference unit in

the frontier that determines the optimal level; instead, this frontier

48


will be determined by the behavior of the other units in the sample.

One of the main features of the DEA is its deterministic nature, so

the deviations between the assessed units and the optimal frontier are

considered to be a technical inefficiency.

In the group of parametric techniques based on econometric

methodologies, the random ones based on a certain form of

production stand out (stochastic frontier). The difference with the

preceding ones (deterministic) is that, in this case, the deviations

include, apart from the technical inefficiency, external factors that do

not depend on the company management but on the context.

The methodology used in this dissertation is the Data Envelopment

Analysis (DEA) which, given its flexibility with respect to the initial

assumptions and to the lower demand in the observations, will enable

us to assess the efficiency of hospital organizations based on their

type of management.

2.3.2 Efficiency analysis method

To analyze efficiency, we will use two types of models: the

generalized linear regression models and the non-stochastic methods

using the Data Envelopment Analysis (DEA).

49


2.3.2.1 Multivariate methods

Regression analysis (or econometric models): this is the usual

type of analysis when assessing hospital efficiency. By using the data

from all the hospitals or a particular healthcare service, we establish a

production function with several inputs as independent variables that

influence the result and a single dependent variable that determines

the performance, the procedural effectiveness or the cost efficiency.

We then estimate different regressions, assessing and determining

how the independent variables influence the dependent or

performance variable individually.

Therefore, each regression becomes the prediction of a situation. For

example, for a number of operating rooms or doctors (input), we can

determine the result, i.e. the number of hospital stays or the average

complexity in the process (output). Such predictions are obtained

using the average for the other hospitals' results. Therefore, the

difference between a hospital's performance results and the sample

average will be determined by the regression remainder.

The regression remainder will be positive in the hospitals that obtain

better results than expected. The best result for a specific hospital will

be the one that obtains the largest remainder.

50


Our study also includes the generalized linear regression model, to

identify the variables that have a greater effect on the composition of

the overall costs, and the costs by area.

To see the effect of the management model on the several variables

addressed by our study, we analyzed the difference in the averages by

applying the T-test to the variables that meet the normality

assumptions and the Mann–Whitney U test (1947) to the variables

that do not meet Kolmogorov's normality test.

The regression analysis has certain limits when identifying the best

praxis since the efficiency information that it provides is limited. A

method such as the stochastic frontier regression (SFR) enables us to

model the error term in two parts: the first one shows the deviations

with respect to an optimal frontier, and the second one determines

the conventional statistical noise (Chirikos et al., 2000). The

stochastic frontier regression breaks down the error term and

determines the overall efficiency level based on the sample of possible

suppliers, and it subsequently calculates their deviations based on

their distance with respect to the efficiency frontier. The

aforementioned authors state that there is a need to conduct more

comparative studies of the results obtained using the DEA and SFR,

whose main characteristics are detailed in the next section.

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2.3.2.2 Non-stochastic methods

Data Envelopment Analysis (DEA): the DEA has become a very

valuable tool for making comparative efficiency analyses, especially in

the public sector. Efficiency studies are now conducted on hospitals to

assess their behavior based on the basic principles of microeconomic

theory, such as maximizing profits.

Using the DEA technique, efficiency is calculated by solving the

multiple linear programming problems for each hospital, calling them

decision making units (DMU), with the aim of determining their

overall efficiency level so their inputs are weighted to maximize the

weighted results between both, taking into account the restriction that

all hospitals using this weighting obtain the maximum result,

represented by the value 1, or lower than this value if they are

inefficient with respect to the others.

In this way, different ratios are obtained with the most beneficial

weightings for each hospital; such ratios and the radial efficiency

concept were established for the first time by Farrell in 1957, which is

why they are also called efficiency indexes.

The frontier is established by the healthcare units considered to be

efficient since they have an optimal reference index (1) and any linear

combination thereof; in this way, one point in the frontier dominates

or equals, in production terms, the maximum vector of outputs given

52


certain inputs or the minimum inputs given a vector output, or any

other feasible production place or unit observed. If we want to assess

a group of "N" production units and each unit consumes "K" inputs

(x1,….,xk) and produces "M" outputs (y1,….,ym), the efficiency of

DMU 1 will be assessed by solving the following problem:

subject to:

where:

ys0 = quantity of output s per DMU,

us = weighting corresponding to output s,

xm0 = quantity of input m per DMU,

vm = weighting corresponding to input m, and where n is the

observation of the various decision making units (j = 1, 2,..., n) that

use p inputs (inputs = 1, 2,..., p) to produce v outputs (v = 1, 2,..., s),

where the variables to be weighted of both the inputs and outputs are

vi and ur, respectively, and the inputs and outputs observed are those

of the assessed unit xij0 and yrj0.

53


The DEA model is also known as CCR because of the surnames of

its authors, Charnes, et al. (1978). They developed it with three

restrictions on technology: specifically, they referred to constant

returns to scale, convexity of the set of feasible input-output

combinations and strong disposability of inputs and outputs.

Banker et al. (1984) also contributed to the CCR model, developing it

from the original, by taking into account the fact that underlying

technology could provide a different and variable performance. This

model is called BCC and it is the methodology that we applied to

determine the efficiency of the hospitals in the Valencia region based

on their management model. The hospitals are compared with other

similar sized ones, which is why we grouped the hospitals into clusters

in our sample, so that the analyses have significant conclusions and

the efficiency indexes can determine the result of a hospital with

respect to the one with the highest productivity and efficiency, which

we will use in chapter four below.

Both the BCC and CRR models were used in their input-oriented

versions, relating the necessary inputs to reach the efficiency frontier

in a certain output. One characteristic when making the analyses is

that the hospital cannot influence the output level since they have to

care for the patients who go to the hospitals randomly and

exogenously. This is why we believe that it is more appropriate to

analyze their behavior from the point of view of the minimum use of

54


resources to meet the healthcare demand and not the other way

around.

The efficiency results of the CCR and BCC models show the inputs'

maximum proportional reduction to reach the efficiency frontier.

Since the hospitals use different production factors (inputs) at the

same time to produce different outputs, it is necessary to use tools

that analyze both factors (input and output). In other words, it would

be interesting to know not only if the hospitals have chosen the

production level that maximizes profit but also if that production

level has been achieved with the lowest quantity of inputs or by

minimizing the production cost.

The main advantage of using the DEA is the flexibility that it gives

when analyzing the information. The inputs can be continuous

variables, ordinals or categories grouped into variables. They can also

be represented in different measurement units depending on the

analysis to be made (case mix, beds in use, delays, etc.). In the same

way, the output term can be analyzed from a much broader

perspective, including quality and performance results.

The most widely accepted advantages of using the DEA when

establishing comparative analyses in the service sector are as follows:

‣ The DEA mathematically establishes the optimal weighting for

each input and product considered. Since the DEA is a non-

55


parametric technique, there is no need to allocate a weighting to each

variable; the DEA's methodology itself allocates a weighting to each

input and output.

‣ The DEA can make simultaneous comparative analyses of multiple

dependent performance variables (cost efficiency and results, quality

and results) and provide a scale based on the best practices. In this

way, each hospital can be compared with a similar sample and

measured from two standpoints: allocative and technical efficiency.

‣ Once the suppliers that form the efficiency frontier are determined,

the DEA can estimate the quantity of idle resources or the additional

quantity of results, quality or production that can be made by an

inefficient DMU, or hospital in our case.

The main limitations of the DEA are as follows:

‣ Since it is a non-parametric technique, it does not have any

statistical indicators to measure the error term (noise) as in the case

of regressions. This is why it is not the best technique for making

assumptions.

‣ Another technical consideration which could limit the scope of the

analysis depending on each case is the number of DMUs to be

considered. Although there are no studies or fixed rules, many

authors suggest that the number of variables should be between 4

56


and 15 observations for each independent variable included in a


‣ The same occurs with the number of input and output variables to

be included. Too many variables are considered to be

methodologically wrong, so our study does not include more than

four.

‣ When performing the DEA, the result determines the suppliers

considered to be efficient; nevertheless, the DEA does not

discriminate the relative differences between the various DMUs.

As a result of such considerations, most authors use at least two of

the preceding tools in a supplementary way with the aim of obtaining

different perspectives of the relative efficiency results, particularly in

the case of healthcare service suppliers.

2.3.3 Hierarchical analysis. Clusters

To see the different performance of the hospitals, it is fundamental to

take into account the structural and activity characteristics of each

one.

The need to compare hospitals as a way of sharing improvement was

a concept implemented in the public sector by Marshall et al. (2000),

in which standard groups or clusters were used.

57


The cluster analysis is a group of techniques used for classifying

objects or cases into standard groups called clusters with respect to a

predetermined selection criterion (Anderberg, 1973).

The purpose of the cluster analysis is to group the observations so

that the data are very similar within the same groups (minimum

variance) and these groups are as different as possible between them

(maximum variance). In other words, if the classification is optimal,

the objects within each cluster will be similar to each other and the

different clusters will be very different from each other. In this way,

we obtain the classification of the multivariate data with the aim of

having a better understanding and the population to where they

belong. We can make a cluster analysis of cases or variables or by

blocks if variables and cases are grouped.

After selecting the variables and calculating the similarities, we began

the grouping process. Firstly, we selected the grouping's algorithm to

form the groups (clusters) and, subsequently, we determined the

number of groups to be formed. These two procedures will depend

on the results obtained and on the interpretation arising therefrom.

There are two types of grouping procedures: hierarchical and non-

hierarchical. The hierarchical cluster is characterized by a tree

hierarchy or structure (dendrogram). In that way, clusters are formed

only by the union of existing groups; therefore, any member in a

58


cluster can trace its relationship in an unbreakable path that starts

with a simple connection.

2.3.4 Diagnosis related groups (DRGs)

In the healthcare context, we remember that no two episodes are the

same, even if the same disease is treated. Nevertheless, thanks to the

DRGs, we can group patients with a similar resource utilization level

to obtain the hospital case mix (Guadalajara, 1994).

These cases are the episodes that are treated, i.e. the patients cared

for at the hospital; therefore, when referring to the case mix, we mean

the different types of patients treated.

For professional doctors, the case mix complexity entails a clinical

complexity; in this case, a greater complexity will entail a worse

situation for the patient. Therefore, a higher case mix indicates a

worse prognosis and greater need for healthcare resources. For the

hospital managers and in the role played by the heads of department

of the administrations, a higher case mix implies greater resource

utilization which, therefore, entails higher costs.

However, the purpose of the DRGs is to relate the hospital case mix

to the demand for resources and the costs incurred by the hospital so,

from the standpoint of the DRGs, a greater case mix complexity

means that patients will need more hospital resources.

59


As a result of using the DRG system to measure hospital complexity

in the last few years, the isolated management based on the clinical

service has evolved into a cross-sectional process management based

on the product. This has led to a style of hospital, complexity or case

mix management in which the organization's management is based

on the hospital processes.

2.3.4.1 Origin of the DRGs

The DRGs were designed and developed at the end of the 1960s at

Yale University (United States). The initial reason for developing

them was to analyze healthcare quality and the use of the hospital

services. The work, which was commissioned by the Health Care

Financing Administration, lasted just over a decade. The research

was carried out by a multidisciplinary technical team directed by

professor Robert Fetter. Specifically, the initial study focused on Yale-

New Haven Hospital (Fetter and Freeman, 1986).

The system was first implemented at a large scale at the end of the

1970s in New Jersey (US). In this case, the DRGs were used for a

specific fixed payment system based on each patient treated in

accordance with his own DRG, which determined the average cost of

treating this disease (Hsiao et al., 1986).

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2.3.4.2 Development of the DRGs

During the DRG development process, it was considered that the

patient classification system should have the following characteristics

so that it would be as practical and logical as possible:

‣ The information about the patient characteristics used for defining

the DRGs would had to be usually summarized in the hospital

reports. The DRGs had to be based on easily available information.

‣ The grouping had to include all the hospital patients with a

manageable number of DRGs, limiting the amount of groups to

ensure their practical use.

‣ The patients within each DRG had to have a similar resource

utilization level, implying a similar treatment cost. Even though there

could be variations in the resource volume used by the patients of a

certain group, they would be known and predictable.

‣ The patients within each DRG had to be similar from a clinical

standpoint, i.e. there had to be a clinical coherence.

2.3.4.3 The weightings of the DRG

The concept of weighting refers to the resource level that may be

needed to treat a case in a specific DRG. The weighting is calculated

based on relativizing the average cost of each patient group, i.e. it is

61


obtained by comparing the individual costs of the various DRGs with

the average cost per patient. Therefore, the relative weighting

associated with each DRG represents the foreseeable cost of that

patient type with respect to the average cost of all the patients.

If the relative weighting of the DRG is equal to 1, this means that the

cost of treating such patients is equivalent to the average cost of the

inpatient (standard). However, if this value is higher or lower than 1,

this means that the specific cost of this DRG is higher or lower,

respectively, than the cost of the standard patient.

In Spain, the weighting of the DRGs based on the calculations made

in the United States was used until 1997 since there were no specific

studies. In 1997, the Spanish Ministry of Health and Consumption

began a study called "Analysis and development of the DRGs in the

National Health System", which was coordinated by Rivero (1997).

The various Spanish regions participated in that work since the

healthcare powers had been devolved to them. As a result, national

weightings were implemented and it provided a way of standardizing

the Spanish system of allocating costs at hospitals. The study marked

a turning point in this area and, since then, the national DRG

weightings are reviewed annually. They are calculated by using the

hospital cost information, obtained by the analytical accounting

systems, of a sample of patient discharges representing all the

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Spanish hospitals. At present, of all the different versions, the system

uses version 23 of the AP-GRD, which includes a total of 676 DRGs.

The combination of the DRG categories with the Spanish weightings

is a very important instrument thanks to their multiple uses. The

DRGs are mainly used as the basis for the healthcare funding,

internal management and quality improvement systems.

In the healthcare industry, the DRG weightings and costs are widely

used as a budget tool. In Spain, most of the hospitals partially or fully

fund their activity based on the DRGs.

In the Valencia region, the funding of the districts under concession

is per capita and the invoicing between hospitals is calculated based

on DRG-assessed processes.

The DRGs relate the patient type in a hospital to the costs that

should be incurred by the hospital for treating such patients;

therefore, from the management standpoint, estimating the average

costs can be used for controlling the service use and facilitating

hospital management in relation to the resource utilization.

Moreover, this provides a double reading: firstly, the hospitals have a

nationwide reference; and secondly, using the standardized

measurement, they can be compared with each other thanks to the

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DRGs by detecting areas for improvement at the hospitals themselves

and facilitating the decision-making of the management teams.

In the context of quality improvement in healthcare, hospitals can

use them as standard indicators, using the DRGs as a healthcare

quality management tool.

Healthcare quality improvement is based on analyzing the deviation

from the rule. When the deviation is significant, the managers must

determine the reason for this. A usual example is to use the average

stay of patients per DRG with the aim of detecting possible

complications in the procedures with hospital admission.

Therefore, a DRG is a group of patients with a certain illness that

need similar treatments and use similar resource levels (Fetter et al.,

1980). The cases that belong to the same category have similar costs,

so we know the average cost of treating the patients within the same

DRG and, therefore, the average total cost of that clinical service

area.

2.3.4.4 The "product" that a hospital provides

Healthcare organizations are currently considered to be service

companies as part of the business network. They are companies that

combine human and physical factors (real estate or supplies) in

clinical processes with the aim of optimizing the health and wellbeing

64


of their patients. Having defined this concept, we will know talk

about the "product" that a hospital provides and how it is measured.

In 2000, a study by Brignall and Modell divided the "product" and its

performance into three sections: financial results, quality indicators

based on the performance of the organization's professionals and

resource utilization. The importance of each group is determined by

its setting and by the hospital strategy.

The hospital organizations have very diverse procedures due mainly

to the unique characteristics of the patients they care for. Therefore,

hospitals provide both tangible products (blood test or X-ray results

or prosthetics) and intangible results (the perceived service, the

clinical diagnosis, etc.). However, the "product" is defined as the care

of the patient to whom the doctor has applied a clinical treatment

(Fresneda, 1998). Even if two patients have the same disease, there

may be different underlying factors that determine the procedure and

make them different.

Therefore, a healthcare organization has both the "products" and the

patients that they care for and it is difficult to standardize them as in

other industrialized sectors. This difficulty is determined by the

differences in, and amounts of, patients cared for and the hospital

procedures.

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2.3.5 Public-private collaboration experience in

healthcare

Regarding the public-private collaboration level, the amount of

infrastructure for providing public services with private funding has

quadrupled in recent years (Abadie, 2008), especially in Europe (La

Forgia and Harding, 2009).

A widely used public-private collaboration model is when a private

company rebuilds a hospital whose infrastructure has become

obsolete (Gomez-Ibañez, 2003). This model is called PFI (Project

Finance Initiative) and was developed in the United Kingdom in the

early 1990s by the Labour and Conservative governments, becoming

a reference and test bed for the other European countries (Nieto,

2004).

In the United Kingdom alone, there are more than 100 projects of

this nature, with an estimated value of 25.8 trillion dollars, which

vary from hospitals for isolated communities, with a budget of

around 15 million dollars, to more than 2 trillion dollars such as the

refurbishment of the Royal London and St. Bartholomew's hospitals

in London (Barlow et al., 2013).

Regarding previous experience in comparing the models, a study was

conducted in Brazil in 2009 (La Forgia et al.), where a PPP (Public

66


Private Partnership) was implemented to care for the low-income

population living in the periphery of São Paulo state.

That study compared 12 directly managed hospitals with 12 under

concession. Both groups were considered to be standard in terms of

size, cost per bed and complexity of the population cared for.

The increase in the use of the public-private collaboration models in

healthcare is conditioned by the current economic recession and the

tax restrictions. We will see greater development in post-Soviet

Europe, where the hospitals do not meet the patients' service demand

and manage their resources inefficiently (Coelho et al., 2009).

The European Commission has recently published an assessment

study on the public-private partnerships in Europe (EXPH, 2014).

One of the main conclusions is that there is insufficient information

to assess the PPP model compared with direct management, so this

dissertation undoubtedly provides scientific value and knowledge to

this area. In the same line, in 2012 the Spanish Society of Public

Health and Healthcare Administration (SESPAS) stated that there

was no evidence of there being any advantages in implementing

PPPs or needing to conduct studies to demonstrate this (Palomo et

al., 2012).

The study by the Commission's expert panel compares the most

frequently used PFI models in Europe. Nevertheless, hospitals in

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Western Europe, with more modern facilities, should be redefined by

the change in the hospital model trend towards more ambulatory

processes and management of chronic cases, thus reducing the need

for beds for acute processes (Rechel et al., 2009).

There are 19 public-private collaboration projects in the healthcare

industry in Spain (James et al., 2010), which are worth 2.3 trillion US

dollars, considerably below other countries, where these types of

contracts are used much more widely, as in the case of the United

Kingdom (stated above) and Italy, where there are 71 projects worth

5.7 trillion dollars; although considerably higher than countries such

as France, which has 16 projects worth 1.6 trillion dollars.

As stated at the start of this chapter, the PFI collaborations, apart

from funding the construction work, also have a service provision

contract to maintain the building or the central non-medical services,

such as restaurants and coffee shops, laboratories, sterilization

services, waste collection and surveillance services.

A limit to the model with respect to the market is that it does not

enable free competition due to the transaction costs for both the

establishment and maintenance.

In Spain, there is a benchmark study in the healthcare industry

conducted by Catalan company IASIST, which issued a report in

2011 based on the Minimum Basic Data Set (MBDS), whose

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database is one of the sources we used for our dissertation. IASIST

compares the directly managed hospitals with other forms of

management, such as foundations, consortiums, PFI and PPPs.

Arenas (2013) conducted a comparative study in the Valencia region.

The analysis compared the cost of the reference patients (per capita)

of the concession districts with those of the directly managed ones:

the cost was 31.77% lower in the case of the PPPs.

When comparing the models, there are also unfavorable opinions

about the use of the public-private collaboration model. The

SESPAS report (Sanchez-Martínez et al., 2014) states that the public

or private ownership of hospitals does not determine their results.

Likewise, it states that the discussion should be abandoned since there

are no factors that can assess the performance of both options.

In the healthcare area, there is a study which shows that quality does

not differ depending on the legal form, but it does acknowledge that

there are better results in ambulatory major surgery rates and greater

clinical effectiveness due to the concession companies' technological

equipment (Coduras et al., 2008).

A study by Salvador Peiró in 2012 comparing the efficiency of the

hospitals under concession and those directly managed shows that the

lower cost of admissions at the PPPs seems to be related to a larger

69


number of admissions at them, so the fixed costs are spread out, thus

reducing this value.

Peiró (2013) added to the preceding study that private management

does not guarantee higher healthcare quality than direct

management or vice versa.

Outside Spain, Masson et al. (2010) state that the hospitals managed

under public-private collaboration treat less complex patients than

the other hospitals belonging to the United Kingdom's National

Health Service. Likewise, those private hospitals have a lower coding

level than the public ones.

Another comparative study of the PFI models was conducted in Italy

(Vecchi et al., 2010), which concludes that the return obtained on this

model by the investors is considerably larger than that expected in a

competitive environment.

In Germany, Herr (2008) compared the healthcare results obtained at

the hospitals using the PFI model with those directly managed, where

the average stay at public hospitals was 3.52 days less than at the PFI

ones. Likewise, the study states that only 59% of the PFI hospitals

have an ambulatory unit which, therefore, leads to greater pressure

on hospital admissions.

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In France, Dormon and Milcent (2012) studied a sample of 1,604

hospitals, i.e. 95% of the specialist healthcare supply in the country,

for a period of 5 years (1998-2003). To make the comparisons, they

divided those hospitals into three groups based on the number of

discharges and they weighted the stays of the DRGs based on the

equivalence tables called ISAs (Indice synthétique d´activité). Using

this indicator per hospital bed, the private hospitals are 70.6% more

efficient. This is because there are more unoccupied beds at the

public hospitals. In the same way, the French public hospitals focus

more on longer stays, while the private hospitals have a larger rate of

operations. This is particularly significant when analyzing the small

hospitals where the stay is 9.3 days at public ones and 3.8 at private

ones. Therefore, it is not surprising that there is more healthcare staff

(7.6 vs. 1.7 and 3.7 vs. 1.9) at small and medium hospitals,

respectively.

Since there is a lack of studies in Spain, we need to review the

literature from other countries that have traditionally used public-

private collaboration (PPC) but with PFI models (Barlow et al., 2013).

Based on the comparison of such PFI models in the United

Kingdom, the data show that there may be higher costs with respect

to public sector borrowing due to the higher financial costs of the

private operators and their economic margins (Hellowell and Pollock,

2007 and 2009).

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When comparing the results from the PFI models with those of direct

management, some authors state that the public-private collaboration

in building a hospital implies higher costs. Likewise, the authors state

that this could also mean a lower quality service in managing the

general services (McKee et al., 2006). Such authors also highlight the

studies which show the complex nature of managing a long-term

healthcare concession.

Another controversial point is the need to have control mechanisms

between the parties involved (Brown and Potosky, 2004).

There are also studies that warn about the underlying risk in public-

private collaboration beyond the PFI model.

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3. ASSUMPTIONS AND

INFORMATION

SOURCES

73

CHAPTER 3 ASSUMPTIONS AND INFORMATION SOURCES

3.1 Assumptions for the study

In the preceding chapter, we provided the various contributions made

to the comparisons between the public-private collaboration and the

traditional public healthcare or direct management model.

The main assumption for this PhD dissertation, which analyzes the

healthcare quality and efficiency of the economic model based on the

type of management, is that the PPP model is more efficient in terms

of healthcare quality and economics than direct management (public

sector), although this efficiency is nuanced or influenced by the

hospital area in question and will have a different result depending on

the analyzed indicators.

3.2 Information sources

We can group the various information sources used for this

dissertation based on the nature of their indicators:

Having reviewed the scientific contributions, this section will now detail the

main assumptions used for the study and show the information sources used

with their corresponding indicators, which will determine the field study and its

conclusions.

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-Economic information/costs: we obtained this data from the

Valencia Health Department's Economic Information system (SIE)

regarding the hospitals that are directly managed. Since the

concession companies are not included in that information system,

we asked them to complete a form with the costs based on their

source or cost pool. Once that information was collected, we adapted

the data to the SIE to standardize this.

- Quality: we obtained the quality indicator results based on the

assessment carried out by the Valencia Health Department (2010) in

the Management Agreements, where the hospitals that are directly

managed and those under concession have been assessed.

- Healthcare production: to obtain the hospital revenues, we used

the Minimum Basic Data Set (MBDS) for all the hospital discharges.

Nevertheless, to have an overall view of the specialist healthcare, we

obtained the information from the SISAL (Healthcare Activity

Information System) for the outpatient and emergency areas. Both

databases were provided by the Valencia Health Department.

- Structure: to obtain the structural aggregates of the hospitals

managed directly and those under concession, we used the

information from the SISAL system.

- Period: all the assessed data refer to 2009 and 2010.

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- Hospitals analyzed:

✓ Directly managed hospitals:

La Plana Hospital (Castellón)

University General Hospital (Valencia)

Castellón General Hospital

Castellón Provincial Hospital

Arnau de Vilanova Hospital (Valencia)

Requena Hospital (Valencia)

Sagunto Hospital

Vega Baja Hospital (Orihuela)

Vilajoyosa Hospital (Alicante)

San Juan Hospital (Alicante)

Clinical University Hospital (Valencia)

Malvarrosa Hospital (Valencia)

Alicante General Hospital

Verge dels Liris Hospital (Alcoi)

San Francisco de Borja Hospital (Gandía)

Dr Peset Hospital (Valencia)

Onteniente Hospital

Lluís Alcañiz Hospital (Xátiva)

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Elda General Hospital

Elche General Hospital

Vinaroz Hospital

La Fe Hospital

✓ Hospitals under an administrative concession (Valencia):

La Ribera Hospital (Alzira)

Elche-Vinalopó Hospital

Dénia Hospital

Torrevieja Hospital

Manises Hospital

3.2.1 Sources of economic information

"SIE is an information system that collects and analyzes the data on

activity and costs in hospitals and provides standardized indicators for

costs, activities and cost per activity which provide a comparison

between hospitals, making it a tool to help decision-making. It is used

as a healthcare management instrument, helping to improve the

effective and efficient use of the public healthcare system's

resources" (Valencia Health Department, 2002).

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It is an analytical accounting system based on an the activity-based

costing (ABC) model. The system comprises two large subsystems of

data collection and analysis: one for the total costs incurred by the

hospitals and the other for the activities generated by the resource

utilization. It shows the cost of each intermediate product identified

in the healthcare process, based on the actual cost of the pool and on

the activity performed in the same period.

The data with which the SIE works is obtained from other

information systems on utilization and activity that form part of the

healthcare system, such as salaries, supplies and storage, specialist

healthcare management indicators, pharmacy, prosthetics, concerted

agreements and procedure catalogues. The SIE is a flexible system,

i.e. it can adapt to the different information availabilities of each

hospital and its specific organizational features.

SIE was implemented in 1992 to improve the economic information

of the Health Department's healthcare districts, helping to make

better decisions by combining the two aspects of healthcare activity:

production and resource utilization.

Since the beginning, SIE has evolved with the aim of adapting as far

as possible to the complex healthcare structure. As a result of

standardizing the activity that is measured at hospitals through the

SIE, part of the results are used for drafting the healthcare tariffs

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included in the Valencia regional government's Law on Rates,

through which the compensation between districts is made.

SIE has been implemented at all the hospitals in the Valencia region's

public network and, at present, it has a single database for all the

centers.

Nuances to the economic data:

As stated previously, the data included in the SIE cover the hospitals

that are directly managed by the public sector. This means that it

does not have information about the districts managed under PPPs

(Manises, La Ribera, Dénia, Elx-Crevillent and Torrevieja). Since the

economic efficiency analysis was made for 2010, we have included

only three PPPs, Alcira (La Ribera Hospital), Dénia and Torrevieja,

as we did not have the costs for the others.

Likewise, we must remember that our data collection date is very

close to the date on which Dénia hospital was opened (2009), so there

may be inefficiencies due to the opening itself, aggravated by the fact

that it was a move and not a new opening.

Also, we did not include information about medium- and long-stay

hospitals since they do not have a Minimum Basic Data Set (MBDS/

CMBD in Spanish) and the cost per DRG cannot be calculated.

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We did not include the DRGs classified as indeterminate in the

MBDS (type 0) since we cannot classify such processes as either

medical or surgical.

The scope of the information is at hospital level in both cases, i.e.

direct management and under concession. This means that we did

not include the activity carried out at the outpatient services

(specialist centers, integrated hospitals, etc.) or in the primary care

units.

We grouped the information recorded at the cost and cost pools

(hereinafter, CACs) into the four large healthcare activity areas of a

hospital: outpatient, emergency, inpatient and surgical; and we

allocated their direct costs.

We did not take into account the allocation of the structural and

logistics costs such as the support units (admissions, patient care, etc.)

and secondary structural costs, or the primary structural costs (water,

electricity, maintenance, etc.).

Another limit to the information is that we did not include the costs

from the central healthcare CACs (the diagnosis and treatment

CACs: the hematology, biochemical, microbiology, radiology,

rehabilitation, lithotripsy, electrophysiology and cardio stimulation,

hemodynamics and interventional cardiology laboratories, etc.) or the

surgical area. In fact, the costs of those CACs should be passed on to

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the end CACs but SIE does not allocate such costs; that is the

criterion we used when allocating the information about the

concession costs.

Therefore, the costs of the end inpatient or ambulatory CACs do not

include the cost of the diagnosis and therapeutic tests made on the

inpatients or the patients dealt with in the outpatient services and

emergency units. They do include the cost of the specialization's

diagnosis and therapeutic procedures (neurology, digestive medicine

or the clinical service in question) carried out in the inpatient rooms

during the admission period.

We excluded the following ambulatory activities: the day hospital and

the home care programs.

Regarding the surgical area, we included the costs of the surgical

group and of the associated, local and general anesthesia acts which

are included separately in the SIE. Major ambulatory surgery is

considered to be a different CAC in the SIE but we included it to

cover all the surgical group's expenses.

We also excluded the CACs that are not allocated from the CAC list.

Such costs are those borne by the hospital but do not correspond to

their own activity. The pharmaceutical products dispensed to

ambulatory patients stand out since they account for a large volume

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of the cost, and include dispensing pharmaceutical products for

hospital use to ambulatory patients, medicines to dialysis clubs, etc.

3.2.2 Quality information sources

Management Agreements (Acuerdos de Gestión)

The Management Agreements were implemented in the Valencia

Health Agency hospitals through an agreement with the Valencia

Government's Council in 2004, which establishes a variable bonus to

motivate and differentiate the remuneration of the healthcare staff.

Meeting such targets depends on the results obtained in the quality

and efficiency indicators, which are previously agreed by the Valencia

Health Department and the management of each healthcare district.

Because of the importance of human resources in the healthcare

industry, the Strategic Plan confirms the need to develop them so that

the system can work correctly. This means that there is a need to

implement mechanisms that acknowledge and compensate the

professionals' performance, based on their participation in the Health

Department's targets.

Therefore, the Management Agreements show the strategy which

must be carried out by the hospitals that belong to the Valencia

Health Department: the healthcare districts, the public hospitals, the

chronic and long-term care hospitals, the inspection bodies, etc.

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Each hospital has KPI (Key Performance Indicators) with a goal and

a weighting. Through the Strategic Plan Office, the Health

Department monitors the indicators every month and makes a final

assessment at December 31, and subsequently publishes the results,

comparing the hospitals by standardized group.

The main characteristics of the Valencia Health Department's

Management Agreements are as follows:

a) There is a link between the public hospitals and all their

professionals with the strategy previously set out by the Health

Department.

b) Because of the possible differences between the hospitals and the

professionals, it is necessary to be impartial when establishing the

targets, so the requirements must be associated with each

organization's resources.

c) Teamwork will be necessary for assessing the professionals'

individual actions in their own service. Regardless of their category,

all the professionals are assessed for their individual contribution to

the results of the unit where they work.

d) The professionals and their representatives must participate in

establishing the targets.

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e) The economic compensation must be in proportion to meeting the

targets.

f) The process must be transparent, through publicity and control

throughout the year.

g) All the process of establishing and assessing the indicators must be

objective.

The fundamental objective of the Management Agreements is to

achieve greater efficiency in the public healthcare service provided by

the healthcare district regarding both the population allocated to it

and that from the other hospitals requiring its services.

Through the Management Agreements, the Valencia Health

Department analyzes and ranks the hospitals; from 2013, this was

carried out by dividing the hospitals into standardized groups

regarding to the management model (direct or PPP).

3.2.3 Information sources for the healthcare

production

The Minimum Basic Data Set (MBDS/CMBD in

Spanish)

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The basic data necessary for allocating a patient to a certain DRG

comes from the hospital discharge report. Such data is included in

the MBDS, defined as the Minimum Basic Data Set for hospital

discharges. The MBDS is a source of standardized data that contains

21 variables with administrative, demographic and clinical-

epidemiological information about the morbidity treated of the

inpatients and of the patients at the ambulatory surgical services

(Fusté et al., 2002).

After its approval by the Interregional Council, the MBDS entered

into force in Spain in 1987. Nevertheless, it was not until 1992 when

it became mandatory to include in the MBDS the data of the

patients cared for and those who have had at least one hospital stay

(see section 3.2.4) in the Valencia region.

The variables used (included in the MBDS) to classify the patients

through the DRGs are as follows:

1. Main diagnosis

2. Procedures

3. Age

4. Situation upon discharge

5. Secondary diagnoses

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The main diagnosis is defined as the diagnosis which, after the

patient is examined by the doctor, is established as the reason for

being admitted to a hospital. The secondary diagnoses are the

diagnoses which refer to complications or comorbidities (Nanda,

2005) which increases the complexity of the care and, therefore,

influence the duration of the patient's stay at the hospital or of the

administered treatment.

The complications are the disease processes arising during the

hospital stay, while the comorbidities are the patients' health

problems or illnesses before being admitted to hospital.

The diagnoses and procedures are coded through the International

Classification of Diseases, Ninth Revision, Clinical Modification

(ICD-9-CM). The ICD-9-CM is divided into two groups:

✓ The diseases are classified in 17 chapters.

✓ The procedures are classified in 16 chapters.

Since the MBDS does not have any information about the

ambulatory healthcare production (outpatient services and the

emergency department), we obtained this from SISAL information

system. Although the ambulatory surgical processes are not counted

as a inpatient admission, they are included in the MBDS.

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3.3 Indicators and variables used

3.3.1 Economic or cost variables

For this PhD dissertation, and as stated in the preceding section, we

will analyze the costs that are directly allocated, divided as follows:

A. Human resources expenses

‣ Medical staff: this group includes specialist doctors, regardless of

their activity center, pharmacist.

‣ Non-medical healthcare staff: this group includes the staff that

carries out healthcare functions but does not have a degree. They

have a nursing diploma or are midwives, assistants, optometrists,

physiotherapists or specialists.

‣ Non-healthcare staff: this group includes the other administrative

staff and the management team, regardless of their training (medical,

care and non-care personnel).

The amount that will be used refers to all the concept included in

chapter I of the budget:

- The employee salary plus the corresponding social security

contribution.

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- The contribution for professional training or unemployment.

- The recovery for temporary disability.

The percentage of dedication by the hospital staff, based on the

corresponding activity center, must be updated with the aim of

processing this together with the data sent by the calculation center. It

is fundamental to maintain this since the staff makes up most of the

total cost of a CAC and, furthermore, the conversion of the staff

dedication into the full-time equivalent (FTE) is used as a breakdown

criterion for many secondary structural costs and, in some cases, for

the ambulatory logistics costs.

The staff costs will be allocated to their corresponding CACs every

month.

For the medical healthcare staff, we will take the following into

account:

The cost of the continued care by the medical healthcare staff will be

allocated to the various hospital areas: inpatient, outpatient, surgical

and emergency services.

In the ordinary activity, the medical healthcare staff provides its

services to the inpatient services, ambulatory care, operating rooms

and emergency departments or in laboratories, so its cost must be

divided based on their dedication to each CAC, in accordance with

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the chief of the service. The cost of the non-medical healthcare staff

and non-healthcare staff will be allocated to the CAC where they

provide their services, and their cost must be allocated, if they carry

out activities in different CACs, in proportion to the time devoted to

each one.

The pension bonuses for the non-medical healthcare staff and the

service commissions (for the staff remunerated by the specialist care

center but which carries out its activities in another center) will be

included in group 9 of the CAC which is not allocated. The cost of

the residents will be allocated in the following way:

- The cost of the residents in family and community medicine is not

allocated to the SIE in specialist care since this is included in the

budget of the primary care cost pools. If they provide continuous

care at the hospital, the cost of the shifts will be allocated to the

corresponding CAC.

- The cost of residents in other specialties will be included in group 9

of the CAC which is not allocated, in the first three years, unless the

chief of the unit believes that they are carrying out activities for that

service, in which case the head of department must determine the

percentage of dedication to the corresponding CAC. If they provide

continuous care at the hospital, their cost will be allocated to the

corresponding CAC.

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B. Supply utilization

✓ Utilization of healthcare material

✓ Utilization of pharmaceutical products

The cost of the pharmaceutical products include those acquired from

third parties and those produced by the hospital's pharmacy service.

The CACs of which we know the product utilization with accuracy

will be allocated directly. The logistics centers corresponding to each

product line will be used for allocating the utilization costs that

cannot be differentiated and, subsequently, for breaking them down

among the corresponding inpatient area and ambulatory care centers

using an objective criterion.

As a result, we have the information per hospital with a similar

structure to that shown in table 6, where we see the cost breakdown

by area for Hospital H10.

Table 6. Main direct expense items for H10

Source: the author

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3.2.2 Quality variables

Based on the results of the Management Agreements, twelve

indicators were chosen for this study out of a total of 95, grouped

into six variables and provided by the Valencia Health Department:

a) Quality indicators

b) Delay indicators

c) Economic indicators

d) Healthcare process indicators

e) Public health indicators

f) Safety indicators

Likewise, we included the overall score indicator in the Management

Agreements through which the Health Department makes its annual

rankings for the hospitals. Those 13 indicators were also present in

the previous year (2009), so that we could study their evolution.

On one hand, as we said, we included the overall score of the

Management Agreements as the overall indicator, which groups both

the variables chosen for this dissertation and the others, maintaining

the targets for each indicator established by the Health Department

to obtain the hospital ranking.

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We will now provide details of each of the 12 indicators chosen and

of the overall score indicator which determines the final rankings, as

well as the formula used to calculate this, arranged per perspective.

3.2.2.1 Quality indicators

Synthetic satisfaction index

OBJECTIVE: To improve the satisfaction perceived by the patients

in relation to how the healthcare service works.

DEFINITION: The synthetic index obtained in the patient

satisfaction surveys conducted by the Directorate General for Quality

and Patient Care.

This is measured by combining five indexes of the quality perceived

by the patients, obtained from the results of the patient opinion

surveys. The perceived quality indexes are calculated for both

ambulatory care and hospital care, and are as follows:

1. "Satisfaction assessment" index

2. "Perceived improvement" index

3. "Information quality" index

4. "Accessibility" index

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5. "Comfort" index

We added the assessment of the compliance with the objectives

defined for each district and with the actions for improving patient

satisfaction to those indicators.

SOURCE: Directorate General for Quality and Patient Care.

PURPOSE: To maximize this.

NOT APPLICABLE TO: Healthcare districts or hospitals with a

critical mass of insufficient surveys.

3.2.2.2 Delay indicators

Delay in the first visit to a specialist doctor

OBJECTIVE: To reduce the patients' waiting period to visit the

specialist doctor for the first time.

DEFINITION: Average waiting time to visit the specialist doctor.

where:

D: The delay stated in days.

SOURCE: The Healthcare Information System Analysis Service.

DCE = D

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PURPOSE: To minimize this.

NOTE: Since this indicator depends greatly on the healthcare

districts transferring the information in the appropriate format and

characteristics, if the necessary files for assessing this indicator are not

received within an acceptable time period and in due form so that the

healthcare district can assess it, this is considered to be total non-

compliance (0 points).

Percentage of patients with surgery delayed longer than 180

days

OBJECTIVE: To ensure operating compliance with the surgery

guarantee deadlines, even when the choice is not legally required.

DEFINITION: Number of patients with surgery delayed longer than

180 days, divided by the total number of patients on the waiting list

obtained from the LEQ (surgery waiting list) system in the June and

December cutoffs.


The LEQ system.


The average surgery delay (DMI)

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OBJECTIVE: To reduce the average time, stated in days, that

patients have to wait until surgery. The waiting time is defined as the

difference in days between the registered date and the cutoff date.

DEFINITION:

where:

FC: the cutoff date or the date on which the indicator is obtained.

FRSi: the date on which patient i is included in the waiting list.

N: the number of patients waiting for surgery on the cutoff date.

The sum includes the N patients who are waiting for surgery on the

cutoff date.


The LEQ system.


Emergency department waiting times

OBJECTIVE: To reduce the patients' wait times in the emergency

department until receiving medical treatment.

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DEFINITION: The average waiting time (in hours) for medical

treatment in terms of a standardized emergency department (ED)

triage scale.

SOURCE: SASIS. The SIDO22 emergency information system.


NOT APPLICABLE TO: The healthcare districts in which the

hospitals have not implemented the SIDO22 emergency information

system.

Weeks elapsed until the start of treatment once breast

cancer is suspected after a mammogram

OBJECTIVE: To guarantee effective treatment after a breast cancer

screening.

DEFINITION: The 75th percentile of the distribution of the

number of weeks elapsed until the start of treatment.

SOURCE: The Directorate General for Public Health. The Cancer

Office.


NOTE: At least 75% of the women should start the treatment within

8 weeks.

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3.2.2.3 Qualitative economic indicators

Absenteeism index due to non-work-related temporary

disability (IT)

OBJECTIVE: To reduce the impact of non-work-related

absenteeism.

DEFINITION:

where:

ITi: the number of days with temporary disability due to a common

illness or non-work-related accident of worker i in one year.

Ti: the number of days in the contract of worker i in one year.

SOURCE: The Strategic Plan Office.


3.2.2.4 Healthcare process indicators

Ambulatory replacement rates

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OBJECTIVE: To know about the major ambulatory surgery

performed at the hospitals with respect to those that could potentially

be made ambulatory.

DEFINITION: The percentage of operations that could potentially

be part of the major ambulatory surgery.

where:

TSA: the ambulatory replacement rate

Icma: the number of operations made as part of the major

ambulatory surgery of the DRGs that could potentially be made

ambulatory.

NIPA: the total number of operations made of the DRGs that could

potentially be made ambulatory.

SOURCE: The Minimum Basic Data Set (MBDS) and SASIS.


Vaginal delivery rates with epidural anesthesia

DEFINITION:

PAE/V

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where:

PAE: the number of vaginal deliveries with epidural anesthesia

during the period calculated for the indicator.

V: the total number of vaginal deliveries during the period calculated

for the indicator.


The Minimum Basic Data Set (MBDS).


NOT APPLICABLE TO: Healthcare district 6.

3.2.2.5 Public health indicators

Diabetes screening indicator

OBJECTIVE: To increase the diabetes diagnoses with the aim of

reducing known diabetes.

DEFINITION:

where:

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DR: the number of patients aged over 45 with blood glucose

recorded in the previous three years.

DT: the number of patients aged over 45 allocated to consultation.

SOURCE: The Abucasis Office. The SIA.


NOTE: The hospitals that have implemented Abucasis II less than 12

months are excluded.

3.2.2.6 Safety indicators

Rate of hip fracture surgery with a delay longer than 2 days

(DQFC)

OBJECTIVE: To reduce the delay in hip fracture surgery since this is

associated with poorer treatment results and greater complications.

DEFINITION:

where:

FC48: the number of operations made over 48 hours after the

emergency admission during the period calculated for the indicator.

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N: the total number of hip fracture operations during the period

calculated for the indicator.




OBSERVATIONS: This is calculated using Clinos.CalTM.

The denominator is calculated based on the surgical discharges with

code 820 in any diagnosis position. The numerator is obtained from

the discharges of hip fracture surgery whose pre-operative stay

(surgery date - admission date) is longer than two days.

Three-day readmission rate (RI3)

OBJECTIVE: To monitor and reduce hospital readmissions due to

insufficient treatment in previous episodes.

DEFINITION:

where:

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R3: the number of discharges which are readmitted within three days

for whatever reason, excluding Friday discharges that are readmitted

on Sunday during the period calculated for the indicator.

N: the total number of discharges during the period calculated for

the indicator.




NOTE: We excluded the weekend discharges since this could provide

a substantial bias at some hospitals. Although this could lead to

under-reporting, it would take place with approximately the same

degree in all the healthcare districts, so it should not affect the

comparison. The DRG and the diagnosis are not considered because

they depend on factors related to the coding, which may not detect

the readmissions which are readmissions. In this case, there could be

over-reporting which, as in the previous one, would also affect all the

areas equally.

3.2.3 Structural variables

INSTALLED BEDS: The beds that form part of the hospital's

equipment which may or may not be in service for different reasons,

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such as lower healthcare demand, construction work and lack of staff.

The beds can be as follows:

- Available beds: those which can be occupied.

- Non-available beds: those which are not in service at a specific time,

such as during the holidays or for the reasons stated in the first

paragraph.

- Extraordinary beds: those which, due to the hospital's needs, exceed

the supply available at the hospital.

- Working beds: the total beds installed and available for use,

including, where applicable, the extraordinary beds.

Therefore, the working beds include:

The available ones, the pediatric cots and the fixed incubators, as well

as the beds in the short-term units and the extraordinary beds. The

working beds do not include: the cots of newborn babies without

complications, the beds used for the emergency departments or the

armchairs or beds for the day hospital, pre-anesthesia, operating

rooms and dialysis.

OPERATING ROOMS: the rooms where the operations are made,

regardless of the type of surgery, with admission, ambulatory or

minor, excluding delivery rooms.

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DELIVERY ROOMS: the operating rooms which are devoted solely

to obstetrics.

OUTPATIENT BOXES: the outpatient unit in a hospital devoted to

outpatients.

EXAM ROOMS IN THE EMERGENCY DEPARTMENT: the

exam rooms where the emergency healthcare treatment is given and

an assessment is made before any other process (surgery or

admission), if necessary.

TREATMENT AND PLASTER ROOMS IN THE EMERGENCY

DEPARTMENT: the rooms in the Emergency Department where

treatment is provided in the first case and plaster is placed to

immobilize the injured body part in the second case.

OBSERVATION BEDS IN THE EMERGENCY DEPARTMENT:

the rooms in wards with permanent nursing staff and constant

patient monitoring.

INCUBATORS: the devices used in neonatology for premature

babies where there is an optimal temperature and humidity for the

growth of newborn babies. This closed chamber limits the exposure

of newborns to germs. They monitor heart rates, breathing rates and

brain activity.

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INTERVENTIONAL ROOMS: the rooms where surgical

procedures are carried out using images. Their purpose is to perform

minimally invasive processes, mainly by using needles, tubes and

catheters.

COMPUTERIZED AXIAL TOMOGRAPHY EQUIPMENT: the

equipment itself and the tests made with it are commonly known as

CT scans. It is an imaging procedure that uses X-rays to obtain cuts

or sections of anatomical objects. While conventional radiology

provides one image, the CT scan provides multiple images since the

source of the rays rotates around the body.

MAGNETIC RESONANCE EQUIPMENT: this equipment

performs a non-invasive procedure that uses nuclear magnetic

resonance to obtain information about the structure and composition

of the body to be analyzed. This information is processed in

computers and transformed into images to observe the alterations in

the tissues. They are frequently used for detecting cancer.

RENAL LITHOTRIPSY EQUIPMENT: this equipment provides a

non-invasive treatment for bladders or ureters, breaking up kidney

stones.

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3.2.4 Variables in healthcare activity

Healthcare production

HOSPITAL EMERGENCIES: patients cared for by the hospital's

emergency service, even if they are not admitted to hospital.

FIRST VISITS: the ambulatory healthcare received by patients from a

specialist doctor for the first time due to a specific disease.

The following are also considered to be first visits:

- The visits to the primary healthcare unit rerouted to the hospital.

- The visits from another specialist unit.

- When a hospital admission requests this for a specialist other than

the one for which it was admitted.

SUBSEQUENT VISITS: the ambulatory healthcare received by

patients for the same disease within the specialist unit. We distinguish

the following:

- Subsequent visits for results: these are the subsequent visits in which

the supplementary tests or examinations requested in the first visit are

assessed.

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- Ordinary subsequent/checkup visits: these are the subsequent visits

not included in the preceding definition.

HOSPITAL STAY: the unit that measures how long the patients stay in

hospital occupying a bed in a time interval (Vargas González and

Hernández Barrios, 2007).

This is usually measured at 0:00 hours. The minimum stay is

considered to be when a patient stays one night and receives one

main meal (lunch or supper) at the hospital; if the patient stays less

than this amount, it is not considered to be a stay. The following are

not considered to be a stay: observation in the emergency

department, the hemodialysis units, the day hospital or the intensive

care unit.

To calculate hospital stays, we simply subtract the date and time of

the admission from the date and time of the administrative discharge.

HOSPITAL DISCHARGE: this is understood as the moment in which

the patients admitted to hospital vacate the bed in which they stayed.

The reasons for a discharge are as follows:

- Death

- Voluntary discharge

- Medical discharge

107


- Transfer to another hospital

If patients have to be transferred to another hospital, this is not

considered to be a discharge since the bed will not be free until their

return, once the tests or formalities have been made in the other

centre.

If this happens, the patients' stay at the other hospital continues to be

calculated, provided that they do not stay overnight at the other

center.

EQUIVALENT PATIENTS: this is the variable we use to explain the

healthcare activity since talking about medical or surgical acts or

visits is very broad due to the case mix that can take place in each

hospital episode. We used a standardized measurement to explain the

resource utilization. The higher the equivalent patient in a centre, the

higher the resource utilization should be in principle. This variable

can be for the whole hospital or for one area: inpatient, surgical,

outpatient and emergency services; in this way, we can obtain a more

in-depth analysis of the healthcare activity.

To calculate the equivalent patients based on the activity area, we

used the following equivalency table. For the medical and surgical

procedures, we calculated it by using the Minimum Basic Data Set

(MBDS) of each hospital. For the visits and the emergency

department, we used SISAL's results.

108


Such equivalencies are a commonly used standard in the healthcare

industry (IASIST, 2009).

Table 7. Equivalency of the processes to calculate the equivalent patients

Source: IASIST

CASE MIX: a hospital's case mix is determined by the complexity of

the diseases treated at the hospital.

The term "case mix complexity" refers to the specific features of the

patients treated at the hospital, including the need for resource

utilization, the diagnosis and the severity of the illness.

Based on the relative weighting of the diagnosis-related groups

(DRGs) which we will explain in the next section, we can measure the

case mix complexity of a hospital by calculating the average for the

weighting, weighted by the number of discharges in each DRG. The

average complexity measurement of the patients cared for is included

Area Equivalency

Surgical procedures The complexity of the DRGs

Medical procedures The complexity of the DRGs

First outpatient visits 0,033

Subsequent outpatient visits 0,02

Emergency Room 0,04

109


in this case mix index. This index enables us to compare hospitals in

relation to the type of patients treated. In turn, we can divide this

index into medical or surgical, or as a whole.

3.3. Cost breakdown method

We used the following cost breakdown method:

1. Using a hospital's Minimum Basic Data Set (MBDS) as the

baseline for a specific period (2009 and 2010), we divided it into two

files: one file with the surgical procedures that include major

ambulatory surgery (MAS) and the other which groups the medical

procedures.

2. After dividing this, we calculated the overall sum of each DRG so

that we could have all the cases and their total weighting for each

one. To do this, we weighted the total weighting of the DRG divided

by the number of cases to obtain the relative weight in equivalent

patients. With that data, we obtained the percentage of complexity of

that DRG with respect to the total equivalent patients of a hospital's

surgical or medical area in a specific period.

3. The next step was to break down the total cost of each source

(staff, healthcare material and pharmacy) based on the adjusted

110


percentage of this procedure with respect to the total, in line with the

following function:

[(sum of the weighting of the DRG) / (equivalent patients)] x total €

allocated to the area.

4. This adjusted percentage was used for the breakdown of each cost

based on its source. In this way, we obtained the following subdivision

for each DRG per year and centre, grouping or ungrouping it as

needed:

- Cost of the medical staff

- Cost of the non-medical healthcare staff

- Cost of the non-healthcare staff

- Cost of the pharmacy

- Cost of the healthcare material

Using this information, we added the total cost per DRG and divided

it into the number of cases to obtain the average cost.

One variable that we used in the chapter on results was the COST

PER EQUIVALENT PATIENT.

This value is determined by the following expression:

111


Cost of the hospital or area to be analyzed / Equivalent patients of

hospital or area to be analyzed.

112


4. RESULTS

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CHAPTER 4 RESULTS

4.1 Cost analysis

This section analyzes the cost differences by using the graphs of the

various hospitals and comparing the results from the directly

managed hospitals (H) with the Concessions (C).

4.1.1 Main healthcare indicators

Table 8 analyzes the main healthcare indicators in procedures with

hospital admission.

This chapter details the results from the efficiency assessment made on the PPPs

compared to the directly managed hospitals. Firstly, we grouped the hospitals

into clusters, a criterion that will be maintained when comparing the equivalent

patients by using comparative graphs and regression models. Lastly and to

assess the efficiency using the DEA, we added the quality indicators to the

economic and structural ones

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CHAPTER 4 RESULTS

Table 8. Main hospital activity indicators in 2010

Source the author.

Tables 8 and 9 show the hospitals from the largest to smallest case

mix. In this case, the mix shows the complexity of the surgical and

medical procedures with respect to the total discharges. We excluded

the ambulatory activity in visits to the doctor and the emergency

department.

We calculated the average for the total of the case mix and average

stay since they are more representative than the total sum.

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CHAPTER 4 RESULTS

The discharges at the PPPs represent a 19% of the total in the

Valencia region, with a higher percentage in surgical than medical

discharges (21% and 17%, respectively).

The number of stays at the hospitals under concession account for

13% of the total in the Valencia region. We believe that this average

stay is an interesting indicator because of its variability. In principle,

the larger the case mix, the longer the hospital stay because of the

larger complexity in the hospital procedure. We can see that the

hospitals with the largest case mix were those with a longer average

stay (H22 and H8). Nevertheless, for example, C4, which has the

fourth largest case mix in terms of discharges, has one of the shortest

average stays in the Valencia region, i.e. 4.22 days, a minimum

difference of 3 days with respect to the hospitals that deal with

greater complexity in their procedures.

The average hospital stay in the Valencia region is 6.84 days,

although all the concessions are below this value. C2, with an average

stay of 4 days, has the shortest stay among the concessions.

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CHAPTER 4 RESULTS

Table 9. Main indicators of ambulatory production in 2010

Source: The author

The outpatient services at the PPPs represent a 22% of the total in

the Valencia region, excluding concession C5. The reason for this is

that its ratio of their subsequent to first visits does not seem coherent

in table 9. We believe that this indicator is interesting since the PPPs

are more efficient in managing their ambulatory activity in outpatient

services. The average ratio for subsequent visits to the first visit in the

Valencia region is 2.55: this indicator is 1.95 among the PPPs and

2.81 among the directly managed hospitals, i.e. 44% higher.

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CHAPTER 4 RESULTS

The emergency services at the PPPs account for 16% of the total in

the Valencia region.

4.1.2 Cluster analysis

To calculate the clusters, we took into account the activity and

structural equipment variables. We specifically considered the

following: total equivalent patients, operating rooms, emergency

treatment rooms, interventional rooms, recovery rooms in the

emergency departments, emergency plaster rooms, renal lithotripsy

equipment, magnetic resonance equipment, CT scan equipment,

incubators, consulting rooms, delivery rooms, installed beds and

observation beds in the emergency departments.

The dendrogram in graphic 9 shows the logical representation in a

tree diagram of the formation process of the hospital groups and the

resulting three groups that are formed.

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CHAPTER 4 RESULTS

Graphic 9. Dendrogram showing the clusters

Dendrogram that uses an average link (between groups) Rescaled distance cluster combine

Source: the author.In this way, we classified the hospitals into three groups. Four

concessions were included in group 1 (C2, C3, C4 and C5) and one

(C1) in group 2:

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CHAPTER 4 RESULTS

Group 1: H6, H18, H7, H16, H4, C5, H12, H15, H19, H13, H21,

C2, C4, H5, H17, C3 and H14.

Group 2: H1, H20, H2, H11, C1, H8 and H10.

Group 3: H22.

Group 1 is formed by small and mid-sized area and district hospitals;

group 2 by reference hospitals with a larger structural equipment and

a broad service portfolio; and group 3 by the benchmark hospital for

the Valencia region since it is the largest and has the greatest

complexity in the procedures that it deals with.

4.1.3 Overall hospital costs in the Valencia region

Graphic 10 shows the annual direct spending in specialist healthcare

in the Valencia region in 2010.

Excluding concessions C4 and C5 since we do not have their

information, the overall cost exceeded 1.06 billion euros; the

concession with the highest cost was C1, with nearly 56 million euros.

We must remember that these data do not include the total hospital

cost because of the limitations stated in chapter 3.

The benchmark hospital H22 has a cost of 180 million euros, which

is more than double the second-largest in terms of the budget (H11).

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CHAPTER 4 RESULTS

G

raph

ic 1

0. O

vera

ll di

rect

cos

ts p

er h

ospi

tal i

n 20

10 (

in m

illio

ns o

f eur

os)

Sour

ce: t

he a

utho

r.

Millions

121

CHAPTER 4 RESULTS

Graphic 2: Coste Global directo por hospital

4.1.4 Cost per equivalent patient and area

In this section, we will show the cost per equivalent patient (PEQ) for

each of the hospital's main activity areas:

‣ Inpatient care (medical procedures).

‣ Surgery (surgical procedures with or without admission).

‣ Outpatient services.

‣ Emergency department.

122

CHAPTER 4 RESULTS

Gra

phic

11.

Cos

t pe

r eq

uiva

lent

pat

ient

in t

he in

patie

nt c

are

area

in 2

010

Sour

ce: t

he a

utho

r

Cos

t per

Equ

ival

ent P

atie

nt (i

npat

ient

are

a)Av

erag

e

123

CHAPTER 4 RESULTS

Graphic 3: Coste por paciente equivalente en hospitalizaciónIn graphic 11, the x-axis shows the hospitals arranged from biggest to

smallest cost: hospital H6 has the biggest cost, with 2,546 euros per

equivalent patient, and H3 the smallest, with 242.1 euros. Therefore,

there is a wide variability in the medical area's costs.

This may be due partly to the fact that the surgical procedures with

admission have a part of their DRG cost allocated to the inpatient

care area.

The average cost for the hospitals in the Valencia region is 976.15

euros, with a standard deviation of 548.45 euros.

The PPPs are in 5th, 9th and 11th place in terms of the hospitals with

the highest cost per equivalent patient in the medical area; C1 and

C3 are higher than the average in the Valencia region and C2 is

practically the same as the average, although slightly higher at 979.76

euros.

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CHAPTER 4 RESULTS

GIn graphic 12, the x-axis shows the hospitals arranged from biggest to

smallest cost per equivalent patient in the surgical area: hospital H21

has the biggest cost, with 1,490 euros per equivalent patient, and

concession C2 the smallest, with 640.35 euros.

The average cost in this case is 1,056 euros, with a standard deviation

of 231.62 euros, so the data variability is lower than in the case of the

inpatient care area.

We can see that all the surgical areas that exceed the average are

directly managed hospitals. The three PPPs are among the six

hospitals with the lowest cost per equivalent patient.

Likewise, the two hospitals with the lowest cost per equivalent patient

are concessions C2 and C1, with 706 euros and 640 euros,

respectively.

.

125

CHAPTER 4 RESULTS

Gra

phic

12.

Cos

t pe

r eq

uiva

lent

pat

ient

in t

he s

urgi

cal a

rea

in 2

010

Sour

ce: t

he a

utho

r

Aver

age

Cos

t per

sur

gica

l equ

ival

ent p

atie

nt

126

CHAPTER 4 RESULTS

Gra

phic

13.

Cos

t pe

r eq

uiva

lent

pat

ient

in

the

outp

atie

nt s

ervi

ce a

rea

in 2

010

Sour

ce: t

he a

utho

r

Aver

age

Cos

t per

equ

ival

ent p

atie

nt (o

utpa

tient

are

a)

127

CHAPTER 4 RESULTS

In graphic 13, the x-axis shows the hospitals arranged from biggest to

smallest cost per equivalent patient in the outpatient services.

As stated in chapter 3, to calculate the equivalent patients in the

outpatient services, we took into account the first and subsequent

visits, weighting the complexity of both to match their weighting in

the DRGs (table 7).

Hospital H22 had the biggest cost, with 3,393.50 euros per

equivalent patient, and hospital H18 had the lowest, with 847.67

euros.

The only concession that exceeds the average of all the hospitals is

C3, with 1,658 euros, slightly higher than the average cost of the

outpatient services, which is 1,633 euros per equivalent patient. In

the outpatient services, there is considerable variability since there is

a standard deviation of 609.2 euros, or 400.54 euros if we take out

the two outlier hospitals from the analysis (H22 and H18).

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CHAPTER 4 RESULTS

Graphic 6 Coste por paciente equivalente en Urgencias

Gra

phic

14.

Cos

t pe

r eq

uiva

lent

pat

ient

in t

he e

mer

genc

y de

part

men

t ar

ea in

201

0 Sour

ce: t

he a

utho

r

Aver

age

Cos

t per

equ

ival

ent p

atie

nt in

Em

erge

ncy

Roo

m

129

CHAPTER 4 RESULTS

In graphic 14, the x-axis shows the hospitals arranged from biggest to

smallest cost per equivalent patient in the emergency department

area.

We weighted the activity in the emergency department in accordance

with table 7 to adjust the activity to the equivalent patients, as we did

with the outpatient services.

In this case, hospital H6 had the biggest cost, with 4,242 euros per

patient, and H18 the lowest (like the outpatient services), with 1,559

euros.

The only concession that exceeds the average of all the hospitals is

C3, with 2,998 euros; it has the fourth largest cost per equivalent

patient. The other two concessions are below the average of 2,370

euros and the standard deviation is 640.37 euros.

4.1.5 Healthcare production in equivalent patients by

area

When analyzing the hospitals' activities and comparing them, we

decided to make an adjusted analysis by area in terms of equivalent

patients by taking advantage of the cluster shown in section 4.1.2.

We used three levels:

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CHAPTER 4 RESULTS

- Equivalent patients with medical and surgical admission, plus

the major ambulatory surgery episodes.

- Equivalent patients in the outpatient services.

- Equivalent patients in the emergency department.

CLUSTER 1

Graphic 15. Equivalent patients in the medical and surgical area in cluster 1

#

Source: the author.

In graphic 15, the x-axis shows the hospitals in the cluster, arranged

from the highest to lowest number of equivalent patients in group 1,

in the inpatient and surgical areas.

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CHAPTER 4 RESULTS

We can see that the first in the group is concession C2, with 33,234

equivalent patients, followed by four directly managed hospitals

(H14, H17, H13 and H5, respectively). The other PPP in the group is

in sixth place out of a total of 15 hospitals analyzed. The average for

this variable is 19,312 equivalent patients, and both PPPs exceed this

value.

Graphic 16. Equivalent patients in the outpatient service area in cluster 1

#

Source the author

In graphic 16, the x-axis shows the hospitals in cluster 1, arranged

from the highest to lowest number of equivalent patients in the

outpatient service area.

Once again, we can see that concession C2 heads this group with the

largest number of equivalent patients, and concession C3 is the third

132

CHAPTER 4 RESULTS

one in the group of 15 hospitals, with 5,040 and 4,160 equivalent

patients, respectively.

On the other hand, the two hospitals with the lowest number of

patients are H6 and H18, with 2,337 and 2,318, respectively.

Graphic 17. Equivalent patients in the emergency department area in cluster 1

#

Source :the author



emergency department area.

In this case, the ranking has changed completely with respect to the

preceding ones since the first concession that appears is C2 in sixth

place and then C3 in eleventh.

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CHAPTER 4 RESULTS

CLUSTER 2

Graphic 18. Equivalent patients in the medical and surgical area in cluster 2

Source: the author.


from the highest to lowest number of equivalent patients in group 2,

in the inpatient and surgical areas.

The first three in this comparison are directly managed hospitals and

the fourth one is concession C1, whose number of equivalent patients

is 43,809, slightly higher than the group average of 42,300.

The hospitals that head this analysis are H11, H8 and H10.

134

CHAPTER 4 RESULTS

Graphic19. Equivalent Patients outpatient area in Cluster 2.

��Source: the author

In graphic 19, the x-axis shows the hospitals in cluster 2, arranged


outpatient service area.

In this case, the breakdown changes with respect to graphic 18 which

shows the inpatient and surgical areas. Hospital H10 has the highest

value, followed by concession C1, with 8,753 and 8,097 equivalent

patients, respectively.

135

CHAPTER 4 RESULTS

Graphic 20. Equivalent patients in the emergency department area in cluster 2

Source: the author.



emergency department area.

As stated for graphic 20, the ranking changes substantially: C1 is in

fifth place, behind H8, H11, H10 and H1, respectively.

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CHAPTER 4 RESULTS

4.1.6 Assessment of the activity through an adjusted

cost-production analysis

In this section, we analyze each area from two standpoints: on one

hand, the direct cost allocated to each area and, on the other, the

healthcare production in equivalent patients in their respective area.

In this way, we can analyze the hospitals' efficiency insofar as these

aggregates are more separate in the graphic since, the lower the cost,

the higher the volume of patients or, failing this, the more complex

the volume of patients.

The y-axis shows the cost, arranging the hospitals based on this

criterion from a larger to a lesser extent.

Graphic 21 shows the cost in the inpatient and surgical areas

compared with the equivalent patients with admission and major

ambulatory surgery, which we then disaggregate to see each one's

specific features:

• Inpatient area cost - equivalent patients

• Surgical area cost - equivalent patients

137

CHAPTER 4 RESULTS

Graphic 13: Coste quirófano y hospitalización

Gra

phic

21.

Cos

t of

the

sur

gica

l and

med

ical

are

a co

mpa

red

with

the

equ

ival

ent

patie

nts

Sour

ce: t

he a

utho

r.

Surg

ical

and

inpa

tient

cos

tEq

uiva

lent

pat

ient

s (s

urgi

cal a

nd in

patie

nt a

reas

)

Millions

Equi

vale

nt

Patie

nts

138

CHAPTER 4 RESULTS

comparado con los

Graphic 21 shows the cost of the surgical and medical area of the

patients admitted to hospital plus those operated in an ambulatory

way.

We can see that H21 and H26 are inefficient compared with the rest

of the sample since their curve of equivalent patients is below the

cost.

Regarding the concessions, the distance between the cost and

equivalent patients of C1 and C2 is greater than the rest, although

C3 is closer and, therefore, less efficient.

Hospital H10 provides the best result because of the distance

between its cost and its curve of equivalent patients.

s

139

CHAPTER 4 RESULTS

E n l a Graphic 14 representamos la distribución del

Gra

phic

22.

Inpa

tient

cos

t co

mpa

red

with

the

equ

ival

ent

patie

nts

Sour

ce :

the

auth

or

Tota

l cos

t (in

patie

nt a

rea)

Equi

vale

nt p

atie

nts

(inpa

tient

are

a)

Millions

140

CHAPTER 4 RESULTS

coste y de los

Graphic 22 shows the breakdown of the cost and equivalent patients

in the inpatient care area, with a less linear distribution than in

graphic 21.

The directly managed hospitals H6 and H21 continue to be

inefficient compared with the rest of the group. We can also see a big

difference between the various hospitals analyzed; the most efficient

ones in the comparison are H17, H14, H10 and H11.

If we break down the preceding graph into two, the PPPs are

approximately in the same efficiency range although the hospitals

with a similar cost (H8 and H10) have a larger number of equivalent

patients in the inpatient care area, although they do not stand out

with respect to the aforementioned ones.

141

CHAPTER 4 RESULTS

Gra

phic

23.

Cos

t of

ope

ratin

g ro

oms

com

pare

d w

ith t

he e

quiv

alen

t pa

tient

s

Equi

vale

nt p

atie

nts

(sur

gica

l are

a)To

tal c

ost (

surg

ical

are

a)

Millions

Sour

ce :

the

auth

or

Equi

vale

ntPa

tient

s

142

CHAPTER 4 RESULTS

Graphic 23 shows the breakdown of the cost in the surgical area with

respect to its equivalent patients.

In this comparison, few hospitals are efficient; specifically, the directly

managed hospitals H11, H10 and H8, and the concessions C1 and

C2. This efficiency is even more evident if we compare them with the

hospitals of a similar cost (H20 and H14).

The most inefficient hospitals in this analysis are H1, H20, H14 and

H2.

4.2 Analysis of the healthcare quality

Because of the importance of this factor in the healthcare industry,

we believe that it should be included in a separate section in the

chapter on results.

Firstly, we will show the position obtained by the healthcare districts

in the Management Agreements in 2010 after their corresponding 95

indicators were measured. Secondly, we will focus on the hospital

indicators that we think are most important, so that we can analyze

them together with the cost or healthcare production variables, thus

establishing whether there are significant differences depending on

the type of management.

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CHAPTER 4 RESULTS

Table 10. Relative position of the Management Agreements in 2010

Source: Valencia Health Department .

Table 10 shows the overall score and relative position of each

healthcare district in 2010.

The top scorer is a directly managed district, H4, followed by

concession C2. In the analysis made by the Valencia Health

Department every year, four PPPs are among the top seven.

Concession C4 has the lowest score, with 73.6%, so it is 15th among

144

CHAPTER 4 RESULTS

the 25 hospitals. Nevertheless, all the PPPs are higher than the

average for the Valencia Health Department (71.69%).

We established three cost comparisons per equivalent patient with the

quality indicator of the area obtained in the Management

Agreements.

When analyzing the perception of healthcare quality, we focused on

the delay indicators by area since patients and managers consider

that a delay in a diagnosis or treatment is one of the key performance

indicators.

Specifically, we included the following:

‣ Wait time in the emergency department.

‣ Average delay for surgical operations.

‣ Delay in the first visit to the specialist doctor.

In all three cases, we excluded the hospitals that do not have the

results of both variables, i.e. the cost per equivalent patient and the

corresponding quality indicator of the area.

To highlight the qualitative results in this subsection, we arranged the

hospitals from longest to shortest delay.

4.2.1 Quality Analysis in Emergency Department

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CHAPTER 4 RESULTS

Graphic 16: Análisis de la calidad en el área de Urgencias

Gra

phic

24.

Qua

lity

anal

ysis

in t

he e

mer

genc

y de

part

men

t

Aver

age

wai

ting

time

(min

utes

)

Sour

ce: t

he a

utho

r

Cos

t per

equ

ival

ent p

atie

nt

Min

utes

Cos

t pe

r EP

146

CHAPTER 4 RESULTS

Graphic 24 shows the wait time in the emergency department in bars

and the cost per equivalent patient in the trend line, so that we can

analyze the relationship between the following three variables: cost -

healthcare production weighted by complexity (cost per equivalent

patient) - wait time.

Hospital H10 has the longest wait time in the emergency

department, specifically an average of 12 hours, considerably

exceeding the average for the hospitals of the Valencia Health

Department which, excluding H10, is under 4 hours. We believe that

this is not a coherent result since it may be due to internal and

administrative patient discharge procedures in the emergency

department. Nevertheless, the cost per equivalent patient at H10 is

1,692 euros, which is actually below the average for the rest of the

sample.

Hospitals H10 to H17, inclusive, have a wait time of over 4 hours,

including concession C2.

Concession C2 stands out because, with a wait time similar to that of

H19 and H8, its cost per equivalent patient is 330 euros higher.

Hospital H6 also stands out because, with a delay of 192 days, it

requires a considerably higher economic effort than the other

hospitals and its cost per equivalent patient is 4,242 euros.

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CHAPTER 4 RESULTS

Concession C1 is below the average wait time, with 223 minutes, and

its cost per equivalent patient is among the top seven in this indicator.

4.2.2 Quality analysis in the surgical area

Graphic 17 Análisis de la calidad en el área quirúrgica

148

CHAPTER 4 RESULTS

Gra

phic

25.

Qua

lity

anal

ysis

in t

he s

urgi

cal a

rea

Sour

ce: t

he a

utho

r

Cos

t per

equ

ival

ent s

urgi

cal

Aver

age

surg

ical

del

ay (d

ays)

Days

Cos

t per

EP

149

CHAPTER 4 RESULTS

Graphic 25 analyzes the average delay for operations at the various

hospitals with respect to the cost per surgical equivalent patient.

This value goes from 26 days at H7 to 81 days at H22, which is the

hospital with the lowest cost per equivalent patient with respect to the

delay.

Concession C2 has the second shortest delay, with 29 days,

considerably lower than the average for the Valencia Health

Department in the sample, with 41.5 days. C2 also has the lowest cost

per equivalent patient of all the hospitals, with 640 euros.

Concession C1 has the second lowest cost per equivalent patient,

with 706 euros, but the delay is 46 days. If we compare the hospitals

with a similar delay to that of C1, we can see that its cost per

equivalent patient is higher, as in the case of H1, which is 1,108

euros, and H5, which is 987 euros.

Concession C3 has a shorter delay and a lower cost per equivalent

patient than the average but, compared with the other hospitals that

have a similar delay, such as H10, it has a higher cost per equivalent

patient.

Hospital H12 stands out because it is very inefficient in its surgical

area since, apart from having a high cost per equivalent patient, it

also has the third longest delay for operations, with 51 days.

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CHAPTER 4 RESULTS

4.2.3 Quality Analysis in the outpatient area.

151

CHAPTER 4 RESULTS

Gra

phic

26.

Qua

lity

anal

ysis

in t

he o

utpa

tient

are

a

Sour

ce: t

he a

utho

r.

Cos

t per

EP

Days

Aver

age

dela

y in

firs

t spe

cial

ized

vis

it (d

ays)

Cos

t per

equ

ival

ent p

atie

nt

152

CHAPTER 4 RESULTS

Graphic18: Análisis calidad en consultas externasGraphic 26 shows the delay in the first visits to the hospital with

respect to the cost per equivalent patient. This cost includes and

weights the first and subsequent visits.

We can see that the delay goes from 21 days at H7 to 136 days at

H18. The hospital with the longest delay has the lowest cost per

equivalent patient in the sample.

Conversely, H22 is in 12th place since it has the highest cost per

equivalent patient, with 3,393 euros.

The concession with the shortest delay in the first visits is C2, with 32

days and 1,206 euros in the cost per equivalent patient. The other

two hospitals with the shortest delays are H16 and H7 but their cost

per equivalent patient is considerably higher than C2, with 1,947

euros and 1,921 euros, respectively.

If we analyze C1 with respect to the hospitals with a similar delay, we

can see that it is 40 days and its cost per equivalent patient is 1,092

euros, i.e. below that of H4, which has a delay of 40.24 days and a

cost per equivalent patient of 1,421 euros, and below that of H10,

whose delay is 5 days shorter than C1 but has a higher cost per

equivalent patient, with 1,619 euros.

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CHAPTER 4 RESULTS

4.3 Analysis of the healthcare activity

The sections below are structured in the following way: firstly, we

made a linear regression analysis, taking the overall cost as the

dependent variable and the equivalent patients as the explanatory

variable since this could be one of the variables that has the greatest

effect on the cost variable. The relationship between these variables is

set out in graphic form so that we can see where each hospital is

(graphic 27).

Subsequently, we made a general linear regression analysis, where the

dependent variable is still the cost and the variables described in

sections 3.2.3 and 3.2.4 have been included as the explanatory

variables.

The structure used for this analysis was firstly based on the overall

data and we subsequently repeated this specifically in each area:

medical, surgical, outpatient services and the emergency department.

The general linear regression analysis for the hospital areas takes into

account the importance of each variable in the actual activity of each

area so that it can be included in the model.

To analyze both the overall costs and the cost by area, we excluded

hospitals C4 and C5 because we did not have the data.

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CHAPTER 4 RESULTS

4.3.1 Overall healthcare production

Graphic 27 shows the total costs of each hospital with respect to its

activity, i.e. with respect to the equivalent patients cared for, which

provides a very clear view of a hospital's tasks. The hospitals under

direct management are in green and PPPs are in orange.

Although the breakdown conforms quite well to the average (we

obtained a correlation coefficient of R2 = 0.926), hospital H22 has

higher total costs than the average with respect to the equivalent

patients that it cares for.

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CHAPTER 4 RESULTS

Graphic 27. Linear regression analysis of the cost and overall equivalent patients

However, the comparison between hospitals must be made by taking

into account their structural characteristics, so the groups calculated

in section 4.1.2 should be taken as the reference.

Equivalent patients

Tota

l cos

t

Source: the author

156

CHAPTER 4 RESULTS

To obtain the equation that predicts the total cost based on the

number of equivalent patients cared for, we made a regression

analysis with the following results:

Y = -6,624,721.493 +1,394.027 X

where:

Y = total costs

X = Equivalent patients

As stated previously, this model explains 92.6% of the data variability.

In other words, to obtain the expected cost of a hospital, we would

simply replace its equivalent patients in the equation obtained:

TOTAL COST = -6,624,721 + 1,394 (equivalent patients)

Since the coefficient is negative, this equation is valid from the

number of equivalent patients in which the line crosses the abscissa

axis.

Subsequently, we made a general linear regression analysis to explain

the total costs, except the quality indicators. In other words, to

explain the variables defined in sections 3.2.3 and 3.2.4.

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CHAPTER 4 RESULTS

Table 11 shows the three models obtained. In model 1, we included

the equivalent patients by area (surgical, emergency, medical and

outpatient) and the stays (medical and surgical). The most significant

variables were the equivalent patients in the surgical area and the

type of management. In model 2, we included the aforementioned

variables but we considered the total equivalent patients and the total

stays, and not by area as in model 1. This means that the equivalent

patients in the surgical area have a larger weighting when

determining the total cost. In model 3, we excluded the type of

management since it was the least significant variable.

We used the stepwise method to include the significant variables.

Table 11. Results of the overall analysis using the general linear regression model.

Source: the author.

VARIABLES Model 1 Model 2 Model 3Coefficients Coefficients Coefficients

Constant α 3.876.981,767 -‐9.070.831,10

Total equivalent pa&ents β1 1.255,501 1.211,854

Equivalent pa&ents in the surgical area β2

2.681,385

Type of Management β3 -‐19.818.259,95 -‐12.127.095,94No of interven&onal rooms β4 2.159.343,130 2.758.203,034R2 ,924 ,933 ,916Adjusted R2 ,917 ,923 ,91

p < 0,05 0 ,000 ,000

F 133,645 97,07 97,07

N (number of hospitals) 24

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CHAPTER 4 RESULTS

The most explanatory is model 2, where the total cost is explained by

the "total equivalent patients", "type of management" and

"interventional rooms", as can be seen in table 11. The coefficient of

the "type of management" variable is negative, so being a PPP has a

negative effect on the total costs.

However, if we see the coefficients shown in model 2 (Bequivalent patients

= 0.811, Btype of management = -0.135 and Binterventional rooms = 0.180), the

"equivalent patients" variable is more explanatory. It is the variable

that has a considerably greater effect on the explanation for the total

costs; it obviously has a positive sign since an increase in the number

of equivalent patients will lead to a rise in costs. Model 2 can explain

92.3% of the data variability, although models 1 and 3 have a similar

explanatory capacity. All the variables that can be seen in table 11 are

significant in the analysis; in other words, t > 0.05.

4.3.2 Healthcare production in the medical area

In this section, we repeated the structure used in the preceding

analysis, focusing on the medical area.

Therefore, we firstly made a simple regression analysis by taking the

equivalent patients of the medical area as the variable which explains

the total cost of the area.

Graphic 28 shows the hospitals based on these two variables.

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CHAPTER 4 RESULTS

Graphic 28. Linear regression of the cost and equivalent patients in the medical area

#

Source: the author.Graphic 28 shows the equation where the hospital complexity

explains the total costs of the medical area:

Y = -5,562,434.216 + 1,455.836 X

where:

Y = total hospital costs

X = Equivalent patients (medical area)

Equivalent patients (inpatient)

Inpa

tient

cos

t

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CHAPTER 4 RESULTS

This model explains 78.7% of the data variability.

We then made a general linear regression analysis following the

criteria and guidelines stated in section 4.3; in other words, we

included all the variables addressed by the study, except the quality

indicators and the variables that do not have a direct relationship to

the activity of the hospital area, with the aim of obtaining a model

with a higher explanatory capacity.

As can be seen in table 12 for the hospital activity, we obtained a

model in which the cost is explained by the "number of installed

beds" and the "type of management".

Table 12. Results of the general linear regression analysis in the medical area

Source:the author

Non standardized coefficient

Standardized coefficients

Sig

Constant α -‐6.073.784,53 ,007

Installed beds β1 48.917,28 ,955 ,000

Type of management β2 9.289.639,82 0,247

R2 0,872

Adjusted R2 ,860

p < 0,05 ,000

F 68,4

N 23

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CHAPTER 4 RESULTS

The following expression shows the equation of the line in which the

installed beds and type of management explain the costs of the

medical area.

Y = -6,073,784.53 + 48,917.28 (installed beds) + 9,289,639.81(type

of management).

In this case, the coefficient sign of the "type of management"

variable is positive, so the status of being an administrative

concession increases the costs in the medical area.

However, if we see the standardized coefficients, we can see that the

"installed beds" variable has a larger weighting to explain the medical

costs.

This model explains 86% of the data variability.

As in the preceding case, all the variables shown in table 12 are


4.3.3 Healthcare production in the surgical area

In this section, we made the same analyses as in section 4.3.1 but we

focused solely on the costs and activity of the surgical area.

Firstly, we obtained a scatter graph of the total surgery costs with

respect to the surgical activity. We can see that hospital H22 is

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CHAPTER 4 RESULTS

considerably higher than the expected value. Also, the PPPs' results

are higher than the average since they are all below the regression

line.

Graphic 29. Linear regression of the cost and equivalent patients in the surgical area

#

Source: the author

Secondly, we calculated the regression line that explains the surgical

costs based on the equivalent patients in surgery. The result is shown

in the following expression:

Equivalent patients (surgical area)

Cos

ts (s

urgi

cal a

rea)

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CHAPTER 4 RESULTS

Y = 20,060,063.157 + 800.455 X .

where:

Y = total surgery costs

X = equivalent patients in the surgical area

This model explains 95% of the data variability.

We will now show the results from the general linear regression

analysis, where we used the same steps and criteria as in section 4.2.1;

in other words, we included all the variables addressed by the study,

except the quality indicators and the variables that do not have a

direct relationship to the surgical activity.

Table 13 shows the results obtained, where all the variables are

significant in the analysis; in other words, t > 0.05:

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CHAPTER 4 RESULTS

Table 13. Results of the general linear regression analysis in the surgical area

Source: the author

We obtained a model in which the surgical costs are explained by the

"surgical complexity" and the number of "installed beds"; the

"surgical complexity" variable has the largest effect when explaining

the cost. This model explains 98% of the data variability.

In other words, we obtained the following function for the model : 1

Surgical costs = 492.42 (equivalent patient in the surgical area)+

18,003.44 (installed beds).

Variables Non standardized coefficient


Sig

Constant α -‐210.075,902 ,741

Equivalent pa&ents in the surgical area β1

492,418 0,598 ,000

Installed beds β2 18.003,441 0,419 ,000

R2 0,981

Adjusted R2 0,98

p < 0,05 ,000

F 529,35

N 23

We decided to use the sta:s:cal criterion of significance to include the variables in 1

the equa:on, which is why we did not include the term of the constant.

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CHAPTER 4 RESULTS

4.3.4 Healthcare production in outpatient services

To analyze the activity of the outpatient services, we excluded the

following hospitals from the study: H3, H9, H13, H16, H18, H21,

C4 and C5, because of the lack of data of some cost items in the

outpatient services.

Graphic 30 shows the hospitals based on the following variables:

"outpatient service cost" and "outpatient service in equivalent

patients".

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CHAPTER 4 RESULTS

Graphic 30. Linear regression of the cost and equivalent patients in the outpatient area

#

Source: the author.

Subsequently, we calculated the equation of the line in which the cost

of the outpatient services is explained by the complexity of the

patients in the outpatient services. The following expression shows

the result:

Y = -5,367,032.394 + 2,924.438 X .

where:

Equivalent patients (outpatients)

Cos

t out

patie

nt a

rea

167

CHAPTER 4 RESULTS

Y = cost of outpatient services

X = complexity of the patients in the outpatient services


To improve the model's explanatory capacity, we made a general

linear regression analysis. Table 14 shows the results. For this analysis,

we used the same criteria as in section 4.2.1; in other words, we

included all the variables addressed by the study, except the quality

indicators and the variables that do not have a direct relationship

with the outpatient services. As in the preceding cases, the significant

variables are those included in the table; in other words, t > 0.05.

Table 14. Results of the general linear regression analysis in the outpatient service area

Source: the author.



Sig

Constant α -4.539.005,403 ,039

Nº outpa&ent boxes 193.932,474 ,888 ,000

R2 ,788

Adjusted R2 ,776

p < 0,05 ,000

F 63,297

N 19

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CHAPTER 4 RESULTS

In this case, the results indicate that the variable that significantly

explains the costs in the outpatient services is the "number of

consulting rooms".

4.3.5 Healthcare production in the emergency

department

To analyze the activity in the emergency department, we excluded

the following hospitals from the study: H9, H10, H15, H19 and H21

as well as C4 and C5, since we did not have the necessary

information.

In line with the preceding sections, we firstly made a regression

analysis in which the "cost of the emergency department" is

explained by the "equivalent patients in the emergency department

area". This relationship is shown in graphic 31.

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CHAPTER 4 RESULTS

Graphic 31. Linear regression of the cost and equivalent patients in the emergency department area

#

Source: the author.

We obtained the equation for the line that explains the cost of the

emergency department due to the patient complexity in the

emergency department.

Y = -235,119.052 + 2,444.270 X

where:

Y = cost of the emergency department

Emergency department equivalent patients

Emer

genc

y de

partm

ent c

ost

170

CHAPTER 4 RESULTS

X = patient complexity in the emergency department


Subsequently, we made a general linear regression analysis using the

same criteria and guidelines as in the previous sections. In other

words, we included all the variables addressed by the study, except the

quality indicators and the variables that do not have a direct

relationship with the outpatient services.

The results are shown in table 15, where all the variables included are


Table 15. Results of the general linear regression analysis in the emergency department area

Source: the author.


Standardized Coefficients

Sig

Constant α -‐1.644.273,25 .13Equivalent pa&ents in the Emergency Depart. β1

2.168,2 0,80 ,000

R2 0,883

Adjusted R2 0,869p < 0,05 0F 60,51N 19

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CHAPTER 4 RESULTS

Once again, the standardized coefficients indicate that the variable

that has the most effect on explaining the costs in the emergency

department is the patient complexity or equivalent patients.

Therefore, we obtained a model that predicts the costs based on the

equivalent patients in the emergency department. Specifically, we

obtained the following equation:

Cost in the emergency department = 2,168.20 (equivalent patients in

the emergency department) + 1,781,866.08 (plaster room).

This model can explain 87% of the data variability, improving the

simple regression model that was obtained previously.

4.4 Study of the effect of the management

model

4.4.1 Total differences

Continuing with one of the specific objectives of this PhD

dissertation, we will identify the possible differences in terms of

performance and resource equipment between the hospitals under

direct management and those under administrative concession. We

used parametric and non-parametric models to analyze the mean

differences in the variables considered in this study. Firstly, we

172

CHAPTER 4 RESULTS

checked the normality of each variable by using the Kolmogorov-

Smirnov test.

- We applied a t-test for independent samples to the variables which

meet the assumption of normality. We also calculated the Levene test

(1960) to identify possible heteroscedasticity problems. The results are

shown in table 16:

Table 16. Statistics for the mean difference

Source: the author.

The preceding table shows the variables that have significant mean

differences of usefulness in healthcare management based on the

type of management.

Table 16 shows that the PPPs have better results in the "first visits",

"delay in first visits" and "ambulatory replacement rate".

Below we show the statistics, significance and size of the effect of

each variable. The size of effect r (in other words, the discrimination

Statistics of the groupType of

ManagementMean

Standard

devia&onFirst visits 0 Public 48.824,95 21.571,668

1 PPP 73.050,8 20.689,6Delays in the first visits (days) 0 52,67 33,48

1 14,52 20,06Ambulatory replacement rate 0 60,09% 0,149

1 80,67% 0,889348

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CHAPTER 4 RESULTS

capacity between these variables and the type of management) was

calculated using expression (1).

(1)

First visits t(25) = -2.281 , p < 0.05, r = 0.38; delay in first visits t(23)

= 2,417 , p < 0.05, r = 0.45 ; and ambulatory replacement rate t(23)

= -2.923 , p < 0.05, r = 0.52;

The biggest difference between these variables based on the type of

management is in the ambulatory replacement rate since it has the

greatest effect (r = 0.52).

- Subsequently, we analyzed the variables that do not meet the

normality criterion by applying the Mann-Whitney (1947) non-

parametric test. Table 17 shows the results of the variables that have

shown significant differences.

Table 17. Results of the Mann-Whitney test

Management Model Statys:calCost of the healthcare material in

Emergency Room

0 PublicMedian 248.565,34

1 PPPMedian 725.782,24

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CHAPTER 4 RESULTS

Source: the author

As can be seen in table 17, the directly managed hospitals have better

results in terms of the "cost of the healthcare material in the

emergency department" since the median is lower than at the PPPs.

Conversely, the median for the rate of hip fractures operated on with

a delay of over two days is higher in the directly managed hospitals;

the best result in this indicator is obtained at the PPPs.

Also, the PPPs show a better result in the MRI equipment since they

have more equipment and a larger overall indicator in the

Management Agreements.

MRI equipment0 Median ,00

1 Median 1,00

Overall score in Management

Agreements0 Median 73,52

1 Median 84,18

Rate of hip fractures operated on

with a delay of over 2 days0 Median 0,588

1 Median 0,169

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CHAPTER 4 RESULTS

The statistics, significance and size of the effect of the variables are

stated below. The size of the effect is calculated in the following

expression (2).

# (2)

Emergency department material U = 8, p < 0.05, r = -0.08; MR

equipment U = 5.5, p < 0.5, r = 0.11; overall score indicator of the

Management Agreements U = 6, p < 0.05, r = -0.087; rate of hip

fractures operated on with a delay of over two days U = 4, p < 0.05,

r = -0.095; and number of cataract operations U = 6 , p < 0,05, r =

-0.09.

If we see the results of the size of the effect, the most significant

differences are in the "cost of material in the emergency department"

variable, which is lower in the directly managed hospitals, and in the

"overall score in the Management Agreements", which has a better

result among the PPPs.

Following this analysis, we can see that the biggest differences in

terms of the type of management can be found in the quality

indicators. Specifically, the size of the most significant effect is in the

indicators where the directly managed hospitals stand out because

their production capacity prevails; in other words, they perform a

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CHAPTER 4 RESULTS

larger number of specific operations on average such as inguinal

hernias, uterus surgery and cholecystectomies. However, the PPP

provide moderately better results, for example, in the overall score

indicator in the Management Agreements.

As stated previously, these results must be treated with caution since

the sample size of the hospitals under administrative concession is

smaller than those under direct management.

4.5 Assessment of the efficiency between

the PPP and the directly managed

hospitals

4.5.1 Overall efficiency

To analyze the hospitals' overall efficiency, the variables we selected

are shown in table 18. For the inputs, we selected the human resource

cost separately because of its importance in healthcare. For the

"other costs", we used the other costs in the study, i.e. supply costs

(healthcare and pharmacy material), installed beds and operating

rooms. For the outputs, we considered the equivalent patients in the

medical, surgical and ambulatory (sum of the outpatient service and

emergency department) areas and the patients' overall satisfaction.

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CHAPTER 4 RESULTS

Table 18. Variables selected for the overall efficiency analysis

Source: the author

Table 19 shows the results of the DEA (data envelopment analysis) of

the variables. Two of the three PPPs of which we have data are

efficient, specifically C1 and C2; also, the following directly managed

hospitals are efficient: H2, H5, H6, H12, H13, H16, H17, H18 and

H19. Concession C3 is not efficient compared with the others but it is

very close to the efficiency frontier, with a score of 91.05%, together

with H4 (96.58%) and H15 (96.1%).

In the middle scores, apart from those stated in the preceding

paragraph, we also find hospitals H7 (88.52%) H14 (85.66%) H10

(85.45%) and H8 (80.97%). Because of their degree of complexity

and their size, these hospitals' inputs are quite high but their outputs

are not sufficient for approaching or reaching the efficiency frontier.

In the bottom scores, we find H11 (68.09%), H21 (67.9%) and H22

(37.05%), where there are clearly shortages; with these hospitals'

178

CHAPTER 4 RESULTS

inputs, they should generate a higher output to reach the efficiency

frontier. H3 and H19 are not shown because we did not have their

full data.

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CHAPTER 4 RESULTS

Table 19. Score overall efficiency

Source: the author, using the DEA SOLVER soXware

Nevertheless, to give a more approximate approach to the results, we

divided them into the two clusters which include the PPPs, so that the

comparison can be made with the standardized hospitals. The third

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CHAPTER 4 RESULTS

cluster, with H22, is not included since we cannot compare it with

any other hospital in the Valencia region.

Table 20. Overall efficiency score for cluster 1


In cluster 1, most of the group's hospitals are efficient. In this case,

concession C2 and the following directly managed hospitals are

efficient: H5, H6, H12, H13, H16, H17, H18 and H19.

181

CHAPTER 4 RESULTS

Table 21. Overall efficiency score for cluster 2


Conversely, in cluster 2 there are only two efficient hospitals in the

group: concession C1 and the directly managed hospital H2.

4.5.2 Efficiency in the medical area

To measure hospital efficiency, the variables we selected are shown in

table 22: the average stay for the hospital procedures, the rate of

readmission three days after the discharge, and the equivalent

patients in the inpatient care area.

For the inputs, we chose the variables that explain the cost such as the

cost in the pharmacy area and the cost of the healthcare staff

(doctors, nurses and assistants), as well as the installed beds.

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CHAPTER 4 RESULTS

Table 22. The variables selected for analyzing the medical area

Source: the author

The score obtained in the DEA for hospital efficiency is shown in

table 23. The hospitals that form the efficiency frontier are those

under direct management: H2, H6, H13, H17, H18, H4, H16 and

H10. In this case, all three concessions are efficient: C1, C2 and C3.

Hospital H19 does not appear because we did not have its full data.

183

CHAPTER 4 RESULTS

Table 23. Inpatient efficiency score

Source: the author, using the DEA SOLVER software

Dividing the results by the cluster, we obtain tables 24 and 25. Table

24 shows the score for cluster 1.

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CHAPTER 4 RESULTS

Table 24. Efficiency score in the inpatient area for Cluster 1


In cluster 1, most of the hospitals in the group are efficient; from a

medical standpoint, PPPs C2 and C3 and the following directly

managed hospitals are efficient: H6, H13, H17, H18, H4 and H16.

Conversely, the hospital that is furthest from the efficiency frontier is

H21, with a score of 69.37%.

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CHAPTER 4 RESULTS

Table 25. Efficiency score in the inpatient area for cluster 2


Cluster 2 is completely different than cluster 1 since there are more

inefficient than efficient hospitals.

4.5.3 Efficiency in the surgical area

To measure the efficiency in the surgical area, we selected the

variables shown in table 26. For the inputs, we considered the

structural indicators where the operations are performed (operating

rooms, interventional rooms and delivery rooms), as well as the cost

of the healthcare material since 75.3% of the total healthcare

material is used in the surgical area.

For the outputs, we selected the equivalent patients in the surgical

area and the average delay in operations stated in days.

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CHAPTER 4 RESULTS

Table 26. The variables selected for analyzing the surgical area

Source: the author.

Table 27 shows the score obtained after the DEA analysis for the

efficiency in the surgical area.

From the standpoint of surgical efficiency, the hospitals that form the

efficiency frontier are PPPs C1 and C2 and the directly managed

hospitals H6, H18, H19, H22, H8 and H10. Hospital H2 does not

appear because we did not have the necessary data.

Hospital H22 stands out because, although it is the most inefficient

from an overall standpoint, it is efficient from a surgical point of view.

The hospitals in the middle zone are those under direct management:

H2 (98.82%), H4 (97.77%), H11 (97.42%) and C3 (91.63%).

187

CHAPTER 4 RESULTS

Table 27. Efficiency score in the surgical area


The most inefficient hospitals are two under direct management,

H12 (56.60%) and H13 (65.03%)

If we divide this analysis into two clusters, we obtain the comparison

shown in tables 28 and 29.

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CHAPTER 4 RESULTS



If we analyze cluster 1, we see that only four hospitals are efficient:

those under direct management, H6, H18 and H19, and concession

C2. As in the preceding section, H4 (97.7%) and C3 (91,6%) are

close to the efficiency frontier.

Table 29 shows the score for cluster 2.

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CHAPTER 4 RESULTS



If we analyze cluster 2, we see that the only hospitals within the

efficiency frontier are those under management, H8 and 10, and

concession C1.

4.5.4 Efficiency in the outpatient service area

To measure the efficiency in the outpatient service area, we selected

the variables shown in table 30. For the inputs, we included the

consulting rooms since, as stated in section 4.3.3, this variable best

explains the cost in the outpatient service area, as well as the direct

cost allocated to the outpatient service area. For the outputs, we chose

the equivalent patients in outpatient area and the delay in the first

visits to the specialist healthcare.

190

CHAPTER 4 RESULTS

Table 30. The variables selected for analyzing the outpatient area

Source: the author.

Table 31 shows the score obtained after the DEA for the efficiency in

the outpatient service area.

191

CHAPTER 4 RESULTS

Table 31. Efficiency score in the outpatient service area


From the standpoint of efficiency in the outpatient service area, only

four of the 21 hospitals of which we have full information make up

the efficiency frontier, specifically those under direct management,

H7, H13 and H18, and concession C1.

In the middle zone of the efficiency in the outpatient service area, we

have the hospitals under direct management H15 (93.73%), H2

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CHAPTER 4 RESULTS

(85.65%), H5 (83.31%), H20 (83.18%), as well as concession C2

(86.23%).

The hospitals furthest from the efficiency frontier are: concession C3

(62.56%), plus H22 (60.09%) and H11 (48.82%).

As in the other areas, we divided the results into two clusters.

In cluster 1, the efficient hospitals are three directly managed ones,

H7, H13 and H18. Concession C2 is in the middle zone, together

with H15 and H5.

Table 32. Efficiency score in the outpatient area for cluster 1


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CHAPTER 4 RESULTS

In cluster 2, only one hospital is considered to be efficient, i.e.

concession C1. In the middle zone, we have the directly managed

hospitals H2 (85.65%) and H20 (83.18%). The most inefficient and

furthest from the efficiency frontier is H11 (48.82%).

Table 33. Efficiency score in the outpatient service area for cluster 2


4.5.5 Efficiency in the emergency department area

Using the same criterion as in the outpatient service area since both

are ambulatory procedures, we selected the variables which are

shown in table 34 because they are the most significant ones.

194

CHAPTER 4 RESULTS

Table 34. The variables selected for analyzing the emergency department area

Source: the author.

In the emergency department area, nine directly managed hospitals

form the efficiency frontier, specifically H2, H13, H20,H21, H11,

H7, H10, H8 and H1, but no concession-

The hospitals in the middle zone are H15, C1, H22 and H19. The

most inefficient one is concession C3, as can be seen in table 35.

195

CHAPTER 4 RESULTS

Table 35. Efficiency score in the emergency department area


Within the efficiency analysis, we divided the emergency department

area into the two clusters.

196

CHAPTER 4 RESULTS

Table 36. Efficiency score in the emergency area for cluster 1


In cluster 1, there are 3 directly managed hospitals considered

efficient H7, H13 and H7. In the middle zone we also find public

hospitals (H15, H22 and H19)

197

CHAPTER 4 RESULTS

Table 37. Efficiency score in the emergency department area for cluster 2


In cluster 2, the only hospital outside the efficiency frontier is

concession C1, although it is very close to the frontier. The directly

managed hospitals are all efficient: H2, H20, H11, H10, H8 and H1.

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CHAPTER 4 RESULTS

5. DISCUSSION

199

CHAPTER 5 DISCUSSION

Despite the controversy and constant debate in the media, there is no

extensive bibliography on the assessments made of the healthcare

management models or similar experiences in other countries

(Barlow et al., 2013), although we did find a large number of articles

comparing the PFI with direct management models.

Below we will compare the main conclusions in the literature and

those obtained in this dissertation, although they are outside the

scope of this comparative study of the PPP and directly managed

hospitals.

We will also focus on those which do establish a comparison between

the management models used in this paper.

At an international level, in the United Kingdom, Mason et al. (2010)

state that the hospitals managed under the public-private

collaboration model treat less complex patients than those of the

other hospitals that belong to the National Health Service. Likewise,

In this chapter, we will analyze two factors: the results obtained in other

assessments of the PPP vs. direct management model, comparing them with the

results from this dissertation; and the limits we found in our own study.

200


such privatized hospitals have a lower coding level than the public

ones.

This statement is a constant paradigm in the media, which state that

PPPs treat less complex patients than in the directly managed

hospitals.

Table 8 shows the case mix for all the hospitals. We can distinguish

the term "complexity" on two different levels: the patient case mix

and the gross hospital case mix. In the first case, the value is adjusted

for the patient; in the second case, a higher complexity can be due to

a larger number of patients, which may or may not be more

complex.

With respect to the first criterion and considering all the sample of

the analyzed hospitals, the PPPs have an average case mix of 1.84,

compared with 1.83 for the directly managed hospitals. Note that the

reference hospital is included in the group under direct management

and no PPPs treat diseases with that level of complexity, such as

transplants.

If we consider only the hospitals (graphic 9) that are included in the

clusters and, therefore, comparable with each other, the average case

mix for the directly managed hospitals is 1.81, compared with 1.84

for the PPPs, i.e. 2% more.

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When analyzing the complexity without adjusting for each case (in

our study of "equivalent patients”), we can add the following facts:

the PPPs treat 26% more total equivalent patients than the directly

managed hospitals. In the medical and surgical areas, this percentage

is 29%, in the outpatient service area, 34%, and in the emergency

department area, 7% less.

Therefore, based on our study's data, Mason's statement is not

correct since his study compares the PFI and direct management

models since, as we stated, the PPPs treat a higher complexity, even if

we consider the non-comparable hospitals, compared with the PPPs

based on the variables selected in the cluster analysis.

Likewise, in section 2.2.1, where we defined the main features of the

PPP contracts, one of the main factors is the compensation between

healthcare districts as a result of the patient rerouting. If it does take

place, the concession has to pay 100% of the DRG cost, compared

with the compensation that it receives (80%-85%) of the cost of the

patients outside its reference population (graphic 8).

In Germany, Herr (2008) compares the healthcare and efficiency

results between the private and public hospitals. His study shows that

the patients at the public hospitals have an average stay of 3.52 days

less than in the private hospitals. According to the authors, this may

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be due to the fact that the payment for each episode depends on the

duration of the stay.

Likewise, the study shows that only 59% of the PFI hospitals have an

ambulatory unit, thus generating greater pressure on hospital

admissions. This differs from the results in our regression analysis,

where we saw that the PPPs have a better ambulatory replacement

rate (table 16).

In our study, the patients have an average stay of 5.04 days at the

PPPs, compared with 7.17 days at the directly managed hospitals

(excluding the hospitals that do not belong to clusters 1 and 2); in

other words, 2.13 days less. All the PPPs have an ambulatory unit. In

fact, the model is tending towards a greater ambulatory service in

healthcare and operations, so the strategy of the German hospitals is

probably adjusting to the current healthcare demand.

In terms of efficiency, the study considers the costs arising from the

admissions and not from the ambulatory service, since they are

reimbursable by the insurance companies. Therefore, we can only

compare these areas.

The cost per bed is calculated by dividing the cost of supplies

(pharmacy and healthcare material) into the working beds, excluding

the staff cost. Considering these criteria, we compared Herr's results

with those obtained in our study. In the German case, the average

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cost per bed was 15,790 euros at the public hospitals and 17,780

euros at the private ones (12.60% higher). In our case, it was 21,160

euros at the directly managed hospitals and 34,800 euros at the PPPs

(46% higher).

In short, the cost per bed based on the cost of supplies is lower at the

public hospitals than the private ones in Germany and with respect to

the PPPs in our study. On the other hand, the conclusions of the

healthcare quantitative analysis are the opposite, both in the

management of the average stay and in the ambulatory service of the

procedures. As stated above, this is probably due to the fact that the

private sector receives a compensation from the German

Administration based on the duration of the stay when the necessary

data for the study were collected.

In France, there is a study by Dormon et al., published in 2012, with

a total sample of 1,604 hospitals, i.e. 95% of the specialist French

healthcare supply, conducted over a period of five years (1998-2003).

To make a comparison, those hospitals were divided into three

groups based on the number of discharges. The paper weights the

stays of the DRGs based on equivalency tables called ISAs (Indice

synthétique d´activité). By measuring this indicator per hospital bed,

the private hospitals are 70.6% more efficient. According to the

authors, this is because there are more unoccupied beds at the public

hospitals.

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In the French study, we can see that there is a significant difference in

the average stay between public and private hospitals. This is even

more evident if we divide the hospitals by group. For example, the

average stay at the small hospitals is 9.3 days compared with 3.8 days

at the private hospitals.

In our study, we also divided the hospitals into three groups, based on

variables which are more specific than the number of discharges

(section 4.1.2). This could provide a bias since it was the only

criterion when dividing the hospitals into groups but it was logical

that the authors simplified the classification since they had a sample

of such aggregates.

In the preceding comparison, we saw that the average stay at the

PPPs was 42.6% lower than at the directly managed hospitals, so we

agree with Dormon's conclusion.

Nevertheless, to see the relationship between this indicator (ISA/bed)

and our results, we divided the equivalent patients into the number

of beds: based on this criterion, the efficiency was 87% higher at the

PPPs than at the directly managed hospitals.

Therefore, it is not surprising that there is more healthcare staff in

the French public system than in the private one, 7.6 vs. 1.7, and 3.7

vs. 1.9 in the small and medium public hospitals, respectively.

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In the comparison between the PFI and direct management models,

McKee et al. (2006) state that the public-private collaboration to

build a hospital generates a higher cost.

We do not have any data in our study with respect to this statement,

but we do have evidence to compare such conclusions. In the PPP,

the extra cost in building a new hospital is passed on to the

concession company itself since the per capita payment includes this

item when new hospitals have to be built, so there is no setback for

the Administration and it is not very logical, even though we do not

have this information, as stated previously.

By limiting the scope of the studies to those which only provide a

comparison between the public hospitals and the PPP, we highlight

the following examples, with different opinions about implementing

the public-private collaboration model in healthcare.

The SESPAS report (Sanchez-Martínez et al., 2014) states that the

public or private ownership of a hospital does not determine its

results. It also recommends that we should abandon this debate since

there are no factors which can assess the performance of both

options. This conclusion is curious since the authors themselves

acknowledge that they do not have sufficient evident to assess this

statement.

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Conversely, we believe that, before implementing either option, both

should be assessed before the decision is made, even before

abandoning this debate. We suggest greater information transparency

so that studies like ours can be conducted. Also, by using

comparisons, we can find out about the best hospital practices which

can provide a greater efficiency to the system, regardless of the

management model, which has been suggested by the authors we

have named.

A PPP was implemented in Brazil (La Forgia et al., 2009) under the

name of “organizações sociais de saúde" to care for the low-income

population living in the periphery of São Paulo state.

The study compared 12 directly managed hospitals with 12 under

concession. Both groups were considered to be standard in terms of

size, cost per bed and complexity of the population cared for.

That criterion for grouping the hospitals was similar to the one we

used in this dissertation to group the hospitals into clusters so that the

conclusions could be significant (section 4.1.2).

The results of this study focus mainly on healthcare efficiency: the

data are shown in tables 8 and 9.

We see that the average stay at the directly managed hospitals in

Brazil is 5.4 days, compared with 4.2 at the PPPs. In our sample, the

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average stay at the directly managed hospitals is 7.25 days (including

the reference hospital), compared with 5.04 at the PPPs, i.e. 43.8%

lower. Therefore, there is greater efficiency in hospital bed utilization.

The costs upon discharge are 48% lower at the Brazilian PPPs

compared with the directly managed hospitals, without adjusting for

the equivalent patients as in our research. In the chapter on results,

using a regression analysis for the equivalent patients compared with

the cost, we showed that the PPPs were more efficient in the surgical

area (graphic 29) and only one was more efficient in the medical area

(graphic 28) with respect to the average.

With respect to the quality indicators in the Brazilian analysis, the

authors simply limited themselves to the morbidity level in the

various areas, where the PPPs obtained better results. Table 10 shows

that the PPPs are among the top positions in the Management

Agreements, which include 95 quality and patient security indicators.

One of the lessons that we can apply is that the per capita payment

in São Paulo partly conforms to the quality results obtained by the

PPPs. In the same way, we believe that this payment should conform,

apart from the quality and patient security results, to the weighting by

the morbidity of the patients cared for or the clinical risk groups

(CRG) (Vivas-Consuelo et al, 2014), since it is wrong to consider that

all the healthcare districts care for the same type of population.

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The study by the European Commission's expert panel (2014) focuses

on analyzing the cost effectiveness of the model based on the

payment per capita received by the concession company with respect

to the cost per capita of the traditional model. Note that, when

calculating the per capita of the PPPs in the Valencia region, major

items such as the following ones are excluded from expenses: the

pharmacy cost for prescriptions, prosthesis, healthcare transport and

oxygen therapy. This is due mainly to the fact that the prescriptions

are centralized among the professional associations, as well as the

tenders for prosthesis, healthcare transport and oxygen therapy.

Therefore, we believe that a comparison should not be made in terms

of budget per capita but based on a standardized measurement such

as the cost per equivalent patient.

Another interesting point stated in the report is the compensation

that is made between the districts when a complex episode is rerouted

to a reference hospital and the possible lack of invoicing control

among the hospitals.

One of the disadvantages and needs stated by the study, which is

stated constantly throughout the report, is the lack of comparative

studies for this model, such as the one we have conducted in this

dissertation. We agree with the expert panel that there is a need to

make the model more transparent by exchanging information and

knowledge among the managers. It should be mandatory for

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managers to share practices with the aim of supporting policy

decisions regarding the suitability of using either healthcare model.

Based on this need, there is a study by Bloom et al. (2010), which

states that including competition variables at the hospitals leads to an

increase in healthcare quality and higher productivity. Specifically,

this competition led to a 10.7% reduction in mortality due to heart

attacks. Such results could be more evident if we compare publicly

owned hospitals (Cooper et al, 2012).

In line with this criticism regarding the need to compare models

based on the cost per capita, there is a clarifying study by Arenas

(2013), which compares the direct management model with the

private-public one using a calculation adjusted for the per capita

expense.

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Graphic 32. Adjusted cost per capita in the Valencia region

Source: Carlos Arenas, with data from the Valencia Health Department, 2013

The methodology used for making the aggregates comparable was to

deduct the centralized expenses of each healthcare district, so the

average cost in the 2004-2010 period was 31.77% lower for the PPPs.

The study eliminated the districts with reference hospitals because

they are not considered to be comparable with the group of PPPs, as

well as one small area hospital since it had a scant reference

population.

By comparing the hospitals with a similar service portfolio and

eliminating the outliers, the study concludes that the PPPs are 157.68

euros cheaper per capita than the directly managed hospitals.

We believe that our study provides a good standpoint to the subject

since it also makes a comparison in the Valencia region, although it

PPPsPublic Departments

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could be completed by stratifying the clusters in section 4.1.2 and

grouping the hospitals by case mix, together with the structural

variables.

In the healthcare area, a study by Coduras et al. (2008) states that the

quality does not differ due to the legal form. It does, however,

acknowledge that there is a greater result in the rate of major

ambulatory surgery and greater clinical effectiveness due to the PPPs'

technological equipment. In our study, we saw that the management

model variable does have a significant effect on the results in the

Management Agreements, which measure 95 indicators regarding

mainly healthcare quality and patient security. Likewise, our study

agrees with the author's conclusion when comparing the magnetic

resonance equipment between the PPPs and directly managed

hospitals, where it is fairly higher among the PPPs (table 17). This

also occurs when analyzing the ambulatory replacement rate, which

is also higher among the PPPs, compared with the directly managed

hospitals (table 16).

A study (Peiró, 2012) on the efficiency comparison between the PPPs

and the directly managed hospitals suggests that the lower cost per

admission among the PPPs seems to be related to a larger number of

admissions at them. Therefore, this value is reduced since the fixed

costs are shared. We agree with the author that including the

structural, fixed or variable costs can distort the results when

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comparing the cost per admission. That is why our study allocates the

directly attributable cost by activity area to avoid this.

According to that study, the cost per capita of the hospital population

is 7.5% higher in the districts that are directly managed than at the

hospitals under concession.

We do not believe that the hospital population variable used in the

study is the most representative for assessing the management

models, although we do not have population variables in our study to

compare the results. We used the sources of our dissertation to

calculate the adjusted cost per admission of equivalent patients. The

result is that the cost at the directly managed hospitals of equivalent

patients is 950 euros, compared with 901.3 euros at the PPPs, i.e.

5.39% lower.

We will now compare the conclusions of a study conducted by

IASIST using the Minimum Basic Data Set (MBDS) for 78 Spanish

hospitals. This paper is considered to be the benchmark in the

healthcare industry. The hospitals were divided into two categories:

directly managed hospitals and "other forms of management", which

include foundations, consortiums, PFI hospitals and PPPs.

The main conclusions of that study between the hospitals with other

forms of management and those directly managed, which we will

compare with the results of our own dissertation, are as follows:

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1. They are smaller hospitals in terms of size and staff and they treat patients of

a similar age and complexity. In our study, we saw that the PPPs'

complexity is similar to those hospitals directly managed in the

analysis on equivalent patients at the these levels: overall, by area and

cluster, and by case mix. See table 8 and graphics 15 and 18.

2. They make a more efficient bed utilization and are more intensive in the use of

ambulatory options. We also agree with this point since, in our study, the

average stay is lower at the PPPs than at the directly managed

hospitals, so they are more efficient in bed utilization. In the same

way, in the analysis of the variables that provide different significant

averages, they have a greater ambulatory replacement rate. In other

words, the percentage of potentially ambulatory operations that were

finally made without admission was higher than at the directly

managed hospitals.

3. Their staff, which has a similar structure, generates 37% more adjusted

activity units. In our dissertation, we did not quantify the number of

full time equivalent workers. Nevertheless, we calculated the total

staff cost per equivalent patient, and the average was 14% lower for

the PPPs.

4. They are more efficient since their cost per production unit is 30% lower. We

partly agree with this conclusion since the PPPs are below the

average cost per production unit (cost per equivalent patient in our

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study) in the surgical area and outpatient service (visits to the doctor

and emergency department), but higher than the average for the

hospital sample in the medical area (graphics 11 to 14).

5. They obtain better results in terms of scientific and technical quality, measured

with the following indicators (mortality, complications and readmissions adjusted

for risk), than the directly managed hospitals. In the quality indicator for our

dissertation, we used the assessment of the 2010 Management

Agreements, which include the indicators that IASIST classifies as

scientific and technical quality ones. We saw that the PPPs obtained a

significantly better result than the directly managed hospitals (table

10).

6. Greater efficiency does not determine the scientific and technical quality. The

hospitals with other forms of management have the same or higher scientific and

technical quality results than those under direct administrative management. Our

study agrees with this conclusion since these indicators are based on

the MBDS discharges, i.e. on the medical and surgical episodes.

Nevertheless, in the emergency department area, we saw that the

PPPs were less efficient than the directly managed hospitals; this part

is not included in the MBDS nor, therefore, in the IASIST study.

This fact, which is stated as an inefficiency in principle, could be due

to better primary healthcare resource management. The frequency

of emergency department visits should be as low as possible since,

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according to studies by the Spanish Health Ministry published in

2010, "the frequency rate of emergency department visits in Spain is

considerably higher than in the United Kingdom or United States. In

Spain, it is estimated that the inappropriate use of the emergency

department varies between 24% and 79%."

That same study also states that 80% of the patients visit the hospital

emergency unit on their own account, without being referred by

other services. "Approximately 80% of the emergencies attended are

discharged to go home."

Table 17 shows the difference with respect to the conclusion that the

hospitals with other forms of management obtain the same or better

results than the directly managed hospitals. It also shows that the rate

of hip fractures operated on with a delay of over two days is fairly

lower at the PPPs than at the directly managed hospitals.

7. There are no significant differences between the two management models in

terms of the age and complexity of the inpatients. We agree with this

conclusion since, as we have seen, the average case mix is 1.83 for the

directly managed hospitals and 1.84 for the PPPs, taking into account

the hospitals in our database.

If we make this comparison between the hospitals included in the

clusters and, therefore, those comparable with each other, the case

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mix of the directly managed hospitals is 1.81 compared with the 1.84

stated above.

We do not have any information about the age of the patients treated

in outpatient or ER so we can not discuss this point.

Lastly, the study concludes by saying that "there are hospitals that use the

direct administrative management model which achieve notable efficiency results; it

is necessary to analyze what factors are behind such results which indicate a

certain improvement in the barriers for this management model and which can be

used as a guideline for the other hospitals of this type." As we see in the

chapter on conclusions, we agree with this statement but with

nuances adapted to our results.

In 2008, Jaume I University published a PhD dissertation by Manuel

Civera, which analyzes the patients' quality perception based on the

management model. Although this was not the main purpose of our

study, we believe that this view completes the comparison between

the healthcare management models by adding a fundamental

standpoint to our dissertation.

The methodology used to reach the conclusions detailed below was

by conducting 399 personal interviews at three hospitals with similar

aggregates and different types of management: direct management

(Sagunto Hospital), PPP (Alcira Hospital) and private hospital (9 de

Octubre Hospital). The questionnaires assessed, among others, the

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following factors: the healthcare staff's professionalism, the patients'

trust, the treatment received, the professional's knowledge of the

patients and the information received by the staff.

Some of the main significant differences in the patients' answers were

in the concepts and items that were assessed:

1. The staff's professionalism

a. They know how to do their work.

b. Their knowledge is updated.

c. They do not make any mistakes.

Alcira Hospital obtained a significantly better result in all three items

than the other two hospitals in terms of the medical and nursing

staff. There were no differences between 9 de Octubre Hospital and

Sagunto Hospital.

2. Trust

a. I felt completely comfortable telling them about anything.

b. I felt comfortable telling them about my doubts.

c. I trust the care that they are providing to me.

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In terms of the medical staff, Alcira Hospital obtained a significantly

better result in the first item; and with respect to the nursing staff, it

had a better result than the other two hospitals in all three items.

3. Treatment received

a. They gave me customized care.

b. The way they treated me made me feel comfortable.

Alcira Hospital obtained a significantly better result than Sagunto

Hospital.

4. Information received by the staff

There were no significant differences between Alcira Hospital and

Sagunto Hospital. There were, however, significant differences

between Alcira Hospital and 9 de Octubre Hospital, favoring the

former.

Therefore, there are significant differences in the factors analyzed in

favor of Alcira Hospital. Consequently, according to this study, the

conclusion is that the quality perceived by the patients cared for

under the concession model is better than the directly managed and

private hospitals, with the proviso of extrapolating the results of the

three hospitals with those of the others in the Valencia region.

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Despite the comparative analysis of the different standpoints between

the management models, we see that the decisions about the

suitability of applying either model have a stronger political

component than technical, as a result of the controversy among the

population. In Spain, we recently witnessed the mobilization in

Madrid against the conversion of 6 hospitals using the PFI model to

the Alzira model, which was finally stopped through legal rulings

(Afem, 2013).

We encountered limits to the scope of our study, so the conclusions

must be taken with caution.

It was very expensive to collect the information and standardize it

due mainly to two factors:

Firstly, the time that it took the Valencia Health Department's central

services to validate it, making this useless for making decisions as a

result of the current lag.

Secondly, the information is considered to be at a hospital and not

district level, leaving out all the primary healthcare, which we believe

plays a critical role in sustaining the healthcare system.

Although the Valencia region has a sample of five PPPs which, at the

date we started this research, were not all in operation or had started

recently, this can affect the efficiency results, more so if we consider

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the case of a transfer from a fully running hospital to a new one, with

the readjustments that this could mean.

5.1 Contribution to knowledge and new

lines of research

This dissertation provides sound research to the subject-matter stated

in the state of the art since there is greater development and tradition

of the public-private collaboration model in the healthcare industry

in the Valencia region. Several institutions and other regional and

international governments have visited the hospitals that use the

Alzira model and are interested in the results obtained by them in

collaboration with the Valencia Health Department. This

corroborates our first statement since the Valencia region is clearly

the most suitable place to make a comparison of these factors.

Likewise, for the first time, the results were treated objectively and in

a standardized way among the hospitals with the aim of obtaining

significant conclusions overall and for each area.

Based on the efficiency and performance results for the PPPs and the

directly managed hospitals shown in the dissertation, the hospital

concession model must continue to be an option to consider when

planning healthcare, more importantly than the messages based on

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opinions which, in many cases, are from detractors or supporters of

either management model. This study should be used by healthcare

managers as the basis for making decisions about the suitability of

developing the Alzira model in Spain and other countries.

Implementing the model in Spain is conditioned by the political

criteria which can jeopardize its development at present. However,

this should not limit its growth at international level, where the

model's future is being considered for the short and medium term. As

a matter of fact, two hospitals were opened in Peru which use the

Alzira model: specifically, Villa María del Triunfo and Callao

hospitals in Lima.

As a result of the foregoing, we believe that an interesting line of

research is being opened to assess the different healthcare

management models.

This study should be conducted with a larger number of hospitals

with the aim of obtaining more representative conclusions.

The future research arising from this dissertation should be

conducted by professionals of the various regions who provide

healthcare and efficiency indicators in a standardized way for each

hospital studied with the aim of obtaining conclusions that will lead

to a greater system efficiency and sustainability.

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This objective will simply be a dream unless all the healthcare

stakeholders get involved.

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6. CONCLUSIONS

224

CHAPTER 6 CONCLUSIONS #

The main conclusions from our PhD dissertation are as follows:

1) The performance and efficiency analyses show that the results of

the group of PPPs were higher than the average for the directly

managed hospitals, but not always better.

2) The highlights of the first analysis made of the main healthcare

indicators are as follows:

a) The PPPs have a better ambulatory rate in surgical

procedures, i.e. 20%.

b) At the hospital level, the average stay at the PPPs is 5.04 days,

compared with 7.25 days at the directly managed hospitals,

i.e. 43.8% less. This shorter stay in the group of PPPs is not

due, as could initially be thought, to an increase in

readmissions for the same diagnostic category, as was seen in

table 16.

This chapter is divided into two: firstly, we will provide the main conclusions

obtained in this work; and secondly, we will determine whether or not the

objectives set out in chapter 2 were met.

225

CHAPTER 6 CONCLUSIONS

c) In the ambulatory area, the directly managed hospitals deal

with an average of 2.81 subsequent visits for every first visit,

compared with 1.95 at the PPPs, i.e. 44% less.

3) The main conclusions from the analysis of the cost per equivalent

patient shows that:

a) The cost of the PPPs in the surgical area is lower than the

average for the sample of the hospitals in the study.

b) The cost of the PPPs in the medical area is higher than the

average.

c) In the ambulatory area (outpatient services and emergency

departments), the cost of two of the PPPs is lower than the

average with respect to the equivalent patients treated. The

cost of the other concession is slightly higher than the average

in the outpatient services.

4) The PPPs treat more complex processes than the directly managed

hospitals in terms of the case mix and the equivalent patients per

area.

5) The highlights of the cost/quality comparison using the cost per

equivalent patient and the delay per area are as follows:

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a) In the surgical area, two of the PPPs have a shorter delay

compared with the hospitals that have a similar cost per

equivalent patient.

b) In the outpatient service area, the same occurs: two of the

three PPPs have a shorter delay compared with the hospitals

that have a similar cost per equivalent patient

The directly managed hospitals have a considerably longer

delay compared with the PPPs that have a lower cost per

equivalent patient.

c) In the emergency department area, the PPPs have a higher

cost per equivalent patient compared with the hospitals that

have a similar delay.

6) The conclusions from the regression analyses which compare the

equivalent patients and cost are as follows:

a) The fact of being a hospital run by a PPP implies that they

have a lower cost than the other hospitals in the sample.

b) The variables that best explain the cost vary depending on the

area, and they are as follows:

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- In the medical area, the installed beds are the main variable

since they determine the number of admissions and, therefore,

the stays.

- In the surgical area, the most significant variables are the

installed beds once again and the equivalent patients.

- In the outpatient service area, the variable that best explains

the cost is the consulting room. In the emergency department

area, the equivalent patients are the main variable.

c) In the scatter diagram based on the overall regression analysis, we

can see that two of the three PPPs are on the lower left side. In other

words, there is a good cost/equivalent patient ratio with respect to

the average.

d) In the medical area, only one of the three PPPs has a good result

with respect to the average.

e) In the surgical area, the three PPPs are on the lower left side,

indicating that there is a better cost/patient ratio than the sample.

f) In the outpatient service area, two of the three PPPs have a better

cost/equivalent patient ratio with respect to the average for the

hospitals.

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g) In the emergency department area, only once concession has a

better ratio among the variables described in the other points with

respect to the average for the sample.

This can be explained because the PPPs are hospitals that focus on

the ambulatory procedures both in the surgical area and in the

outpatient service area with the aim of minimizing hospital

admissions and providing greater quality of life to patients.

7) When we included the quality and structural variables in the DEA,

we obtained the following conclusions per area:

a) The overall efficiency frontier of the hospitals in the Valencia

region is formed by 11 of the 23 that were studied, two of

which are PPPs and nine are directly managed hospitals.

b) In the hospital area, when we included the number of beds

and the healthcare quality variables such as the readmission

rate for the same diagnostic category and the average stay, the

three PPPs are within the efficiency frontier.

c) In the surgical area, when we included the structural variables

and the delay in operations as the quality variable in the same

way, eight of the hospitals are efficient, of which two are PPPs.

d) In the outpatient service area, when we included the

consulting rooms, the delay in the first visit, the cost and

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equivalent patients, only four hospitals are efficient, one of

which is a concession.

e) In the emergency department area, none of the PPPs are

considered to be efficient whereas the nine hospitals that form

the efficiency frontier are the directly managed ones (47% of

the hospitals in the study).

8) In the Valencia region, there are three clusters that should be used

for establishing comparisons in healthcare production and economic

efficiency results since they are determined by structural variables

and equivalent patients.

9) The PPPs obtained a better result in the Management Agreements

than the directly managed hospitals since they occupy four of the top

positions in the ranking made by the Valencia Health Department.

10) This PhD dissertation provides a starting point from which to

continue the research and comparisons between the healthcare

management models both in the Valencia region, a pioneer in

implementing this model, and other regions, so that we can obtain a

larger and, therefore, more representative sample.

OBJETIVES MET

It is interesting to see to what extent we met the objectives defined in

this research.

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We defined the main objective of this dissertation as follows:

"Analyze the influence of the healthcare management model (public or public-

private collaboration) used in the Valencia region from the point of view of

healthcare quality and economic efficiency."

As stated in the conclusions, we believe that this objective was met

but with the limits described in the discussion section from both

points of view: healthcare quality and economic efficiency. However,

the healthcare management model is determined excessively by

political decisions rather than by criteria related to objective

assessments.

The specific objectives of this dissertation were as follows, which we

will state whether or not they were met.

1. Analyze the existing literature in Spain and abroad with the aim of identifying

the main specific variables for benchmarking.

After reviewing the literature and bibliographical sources, we realized

that there were not many studies that compared both management

models. Nevertheless, we did see that the methodology used in the

dissertation and the variables we selected were the usual ones in the

healthcare industry. Therefore, we believe that this objective was met

satisfactorily.

231


2. Select the most significant variables for constructing indicators with the aim of

measuring the efficiency and quality of healthcare organizations.

We believe that this objective was met since, in chapter 3, "Assumptions

and information sources", we detailed the variables selected for forming

the necessary database for measuring the hospital efficiency and

quality indicators. The cost per equivalent patient and area is the

main indicator that we used to measure the economic efficiency. We

also selected the quality indicators from among the 95 available that

provided the best explanation overall and by area.

3. Allocate a standard measurement with the aim of comparing the hospitals and

the cost breakdown.

One of the biggest efforts in this dissertation was to standardize the

information and the indicators with the aim of providing a

comparison. Based on the results obtained in chapter four and the

detail provided to us by the Valencia Health Department's current

information systems, we believe that the cost per equivalent patient is

a representative indicator for comparing the hospitals.

4. Group the hospitals based on their structural resources and healthcare

production capacity.

232


This objective was met satisfactorily in section 4.1 and, as stated

above, it should be used for comparing hospitals, regardless of their

type of management model.

5. Find the variables to explain the cost for each hospital area by using a


In the chapter on results, we stated which variables best explain the

cost, firstly overall and subsequently in each hospital area: medical,

surgical, outpatient services and emergency department, so we can

conclude that this objective was met satisfactorily.

6. Rank the relative efficiency of the hospitals and management models based on

this study.

We drafted a relative efficiency ranking in the last section by using the

DEA. We selected the variables considered to be most representative

in terms of cost and quality and drafted our conclusions based on the

hospitals' position in terms of the efficiency frontier in the area

analyzed.

233

CAPÍTULO 6 CONCLUSIONES

7. BIBLIOGRAPHY

234

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248


8. ANNEXES249

CHAPTER 8 ANNEXES

ANNEX 1. CALCULATION OF THE COST PER PROCEDURE/DRG AT A HOSPITAL

IN ONE YEAR

IT CODE

DRG

Descrip&on of DRG Total cost (€)

Average cost (€)

StayAverag

e stay

Cost per stay (€)

H10 1 Craniotomy, age >17 with complica:ons

106,696.54

3,810.59

392 14.0 272.19H10 2 Craniotomy, age >17 without

complica:ons198,569.17

2,451.47

1065 13.1 186.45

H10 6 Carpal tunnel release50,068.

72 538.37 2 0.025,034.

36

H10 7

Opera:ons on peripheral nerves and skull and other opera:ons on the nerves with complica:ons

12,693.91

2,115.65 14 2.3 906.71

H10 8

Opera:ons on peripheral nerves and skull and other opera:ons on the nerves without complica:ons

88,160.11

1,241.69 70 1.0

1,259.43

H10 9 Spine injuries and disorders 3,586.41

1,195.47

17 5.7 210.97H10 10 Nervous system neoplasm

with complica:ons88,263.

551,961.

41405 9.0 217.93

H10 11 Nervous system neoplasm without complica:ons

36,629.96

1,046.57

355 10.1 103.18H10 12 Degenera:ve diseases of the

nervous system83,417.

661,191.

68685 9.8 121.78

H10 13 Mu l : p l e s c l e r o s i s a n d cerebellar ataxia

51,116.68

896.78 153 2.7 334.10H10 14 Ictus with infarct 372,81

7.791,515.

521990 8.1 187.35

H10 15Nonspecific cerebrovascular accident and precerebral occlusion with infarct

133,372.16

1,149.76 788 6.8 169.25

H10 16Nonspecific cerebrovascular disorders with complica:ons

12,928.63

1,436.51 85 9.4 152.10

H10 17 Nonspecific cerebrovascular disorders complica:ons

10,834.93

773.92 82 5.9 132.13H10 18 Peripheral and cranial nerve

disorders with complica:ons13,697.

481,141.

46161 13.4 85.08

H10 19 Peripheral and cranial nerve d i s o r d e r s w i t h o u t

32,619.58

741.35 196 4.5 166.43

H10 21 Viral meningi:s 3,368.18

673.64 26 5.2 129.55H10 22 Hypertensive encephalopathy 953.78 953.78 7 7.0 136.25H10 23 Nontrauma:c stupor and

coma2,046.3

0682.10 32 10.7 63.95

250

CHAPTER 8 ANNEXES #

ANNEX 2. CALCULATION OF THE COST PER PROCEDURE/DRG AT A HOSPITAL IN ONE YEAR

IT CODE

DRG Descrip&on of DRG

Weigh&ng of DRG

Cases

Sum of weighAng

Medical staff (€)

Non-‐medical healthca

re staff (€)

Non-‐healthcare staff (€)

H10 1

Craniotomy, age >17 with complica:ons 4.8838 28

136.7464

28,945

29,828.98

178.15

H10 2

Craniotomy, age >17 without complica:ons 3.1419 81

254.4939

53,868

55,513.67

331.55

H10

6 Carpal tunnel release 0.69 93 64.17

13,583

13,997.63

83.60

H10

7 Opera:ons on peripheral nerves and skull and other opera:ons on the nerves with complica:ons

2.7115 616.269

3,444 3,548.82 21.19

H10 8

Opera:ons on peripheral nerves and skull and other opera:ons on the nerves without complica:ons 1.5914 71

112.9894

23,916

24,646.78

147.20

H10 9 Spine injuries and disorders 1.4829 3

4.4487

1,471 1,612.49

161.66

H10 10

Nervous system neoplasm with complica:ons 2.433 45

109.485

36,211

39,684.34

3,978.56

H10 11

Nervous system neoplasm without complica:ons 1.2982 35

45.437

15,028

16,469.26

1,651.13

H10 12

Degenera:ve diseases of the nervous system 1.4782 70

103.474

34,223

37,505.57

3,760.12

H10

13 Mu l : p l e s c l e r o s i s a n d cerebellar ataxia

1.1124 57 63.4068

20,971

22,982.67

2,304.13

H10

14 Ictus with infarct 1.8799 246

462.4554

152,953

167,623.31

16,805.09

H10 15

Nonspecific cerebrovascular accident and precerebral occlusion with infarct 1.4262

116

165.4392

54,718

59,965.71

6,011.87

H10 16

Nonspecific cerebrovascular disorders with complica:ons 1.7819 9

16.0371

5,304 5,812.87

582.77

H10

17 Nonspecific cerebrovascular disorders complica:ons 0.96 14

13.44

4,445 4,871.51

488.39

H10 18

Peripheral and cranial nerve disorders with complica:ons 1.4159 12

16.9908

5,620 6,158.55

617.43

251

CHAPTER 8 ANNEXES #

ANNEX 3: COST PER Equivalent paKent AND AREA IT CODE

TYPE OF MANAGEMENT

CLUSTER

Cost per total

equivalent pa&ent

Cost per equivalent pa&ent in the medical

Cost per equivalent pa&ent in the surgical

Cost per equivalent pa&ent in the

outpa&en

Cost per equivalent pa&ent in the

emergencH1 0 2 1,426 1,368 1,108 2,073 1,966H2 0 2 1,248 1,061 1,293 1,215 1,882H3 0 0 1,261 242 1,383 1,136 2,840H4 0 1 1,127 718 960 1,421 3,058C1 1 2 997 1,177 707 1,093 1,968H5 0 1 1,018 580 987 1,471 2,574H6 0 1 1,895 2,546 1,201 852 4,242H7 0 1 1,458 1,571 952 1,922 2,238H8 0 2 1,309 1,267 754 2,995 1,982H9 0 0 1,261 571 1,429 1,092 0H10

0 2 984 806 780 1,619 1,691H11

0 2 1,215 1,229 827 2,194 2,333H12

0 1 1,009 453 1,247 1,570 2,356H13

0 1 970 767 1,127 952 1,649H14

0 1 1,220 691 1,219 1,950 2,598H15

0 1 967 617 1,034 1,519 1,751H16

0 1 1,311 765 1,268 1,947 3,243H17

0 1 852 361 888 1,366 2,240H18

0 1 845 502 988 848 1,559H19

0 1 1,034 373 1,242 1,701 1,985H20

0 2 1,105 791 1,103 1,664 1,668H21

0 1 1,790 1,869 1,490 1,974 2,691C2 1 1 948 980 640 1,207 2,325C4 1 0 0 0 0 0 0C3 1 1 1,317 1,369 883 1,658 2,998C5 1 0 0 0 0 0 0H22

0 3 1,567 1,730 911 3,394 3,053

252

CHAPTER 8 ANNEXES #

ANNEX 4: HEALTHCARE MATERIAL UTILIZATION GROUPED BY AREA

IT CODE

TYPE OF MANAGEMENT

CLUSTER

TOTAL MATERIAL

MATERIAL FOR THE OUTPATIE

NT SERVICE AREA

MATERIAL FOR THE

MEDICAL AREA

MATERIAL FOR THE

SURGICAL AREA

MATERIAL FOR THE EMERGEN

CY DEPARTMENT AREAH1 0 2 8,097,093.

56411,855.8

71,047,05

2.726,245,52

6.07392,658.9

0H2 0 2 3,443,100.69

150,829.35

427,246.23

2,580,922.09

284,103.02H3 0 0 787,224.6

379,212.22 0.00 639,705.

3068,307.11

H4 0 1 2,679,501.38

126,263.81

34,437.32

2,227,992.15

290,808.10C1 1 2 5,926,311.

90200,460.3

11,219,10

6.934,179,83

5.84326,908.8

1H5 0 1 2,025,004.86

198,097.99

1,295.39 1,631,055.17

194,556.31H6 0 1 1,719,787.

5933,968.14 1,276,84

3.73308,016.

37100,959.3

5H7 0 1 2,626,374.29

170,832.86

188,461.50

2,034,221.53

232,858.41H8 0 2 11,027,12

9.531,032,316

.302,057,59

0.547,407,85

7.20529,365.4

9H9 0 0 2,469,519.20

1,389.11 0.00 2,468,130.09H10 0 2 12,428,19

2.65568,768.6

41,349,04

6.139,495,04

5.101,015,332.

78H11 0 2 11,240,356.57

767,242.67

1,322.291.33

8,651,537.13

499,285.44H12 0 1 2,902,573.

3095,851.69 93,908.3

02,543,07

4.34169,738.9

7H13 0 1 3,570,309.12

121,218.70

77,260.96

3,122,029.21

249,800.25H14 0 1 6,153,757.

20332,649.2

7151.496.

065,220,79

9.24448,812.6

3H15 0 1 3,394,188.84

181,905.72

427,982.66

2,573,493.30

210,807.16H16 0 1 3,946,838.

2386,505.38 991,185.

642,621,81

6.77247,330.4

4H17 0 1 3,811,113.10

302,260.47

0.00 3,302,366.64

206,485.98H18 0 1 1,676,465.

21127,544.0

0131,341.

911,172,97

5.92244,603.3

8H19 0 1 1,977,312.55

142,812.67

71.78 1,592,520.83

241,907.27H20 0 2 8,262,901.

42348,797.6

61,054,18

8.626,556,74

6.02303,169.1

2H21 0 1 2,657,803.62

167,083.35

487,366.76

1,948,648.19

54,705.32C2 1 1 6,067,755.

71562,491.4

1579,791.

384,199,69

0.68725,782.2

5C4 1 0 0.00 0.00 0.00C3 1 1 5,024,544.

03326,723.8

8387,403.

883,490,56

2.59819,853.6

8C5 1 0 0.00 0.00 0.00H22 0 3 18,679,09

6.251,161,158

.613,192,76

5.2513,698,1

70.90627,001.4

9

253

CHAPTER 8 ANNEXES #

ANNEX 5: COSTS GROUPED BY SOURCE AND HOSPITAL

IT CODE

TYPE OF MANAGEMENT

CLUSTER

TOTAL COST

COST OF THE

OUTPATIENT

SERVICE AREA

COST OF THE EMERGENCY DEPARTMENT AREA

COST OF OPERATI

NG ROOM + HOSP.

COST OF OPERATI

NG ROOM

COST OF THE

INPATIENT CARE AREA

H1 0 2 71,280,962.69

12,210,327.55

11,482,893.23

47,587,741.91

20,180,951.31

27,406,790.61H2 0 2 33,855,2

91.828,152,43

2.175,254,950.55

20,447,909.10

9,792,319.94

10,655,589.16H3 0 0 9,257,98

6.96

2,174,103,242,106.80

3,841,771.01

3,398,515.56

443,255.45H4 0 1 26,961,5

01.663,410,86

0.527,431,826.82

16,118,814.32

9,578,305.13

6,540,509.19C1 1 2 55,892,7

11.738,848,32

7.228,154,466.40

38,889,918.12

19,040,085.07

19,849,833.05H5 0 1 29,548,8

43.315,818,34

5.566,993,991.92

16,736,505.83

9,132,385.56

7,604,120.27H6 0 1 17,876,5

07.891,991,82

6.834,506,345.04

11,378,336.02

3,552,748.29

7,825,587.73H7 0 1 30,913,3

17.674,492,42

0.766,021,148.32

20,399,748.59

7,712,485.37

12,687,263.23H8 0 2 85,236,4

24.1823,621,5

38.3312,765,080.46

48,849,805.39

22,827,152.90

26,022,652.49H9 0 0 9,010,24

4.86952,528.

510.00 8,057,71

6.357,455,10

4.70602,611.6

5H10

0 2 63,824,724.68

14,174,652.18

9,978,653.41

39,671,419.09

24,918,644.11

14,752,774.98H1

10 2 89,699,7

18.3214,545,1

26.5713,973,359.69

61,181,232.06

28,903,090.35

32,278,141.71H1

20 1 22,112,3

10.954,747,11

2.904,606,908.85

12,758,289.20

7,983,699.71

4,774,589.49H1

30 1 27,692,5

80.03

3,325,163,914,840.64

20,452,574.07

9,609,662.53

10,842,911.54H1

40 1 47,901,5

39.328,714,83

9.5910,803,167.23

28,383,532.50

16,659,545.89

11,723,986.61H1

50 1 26,163,2

42.544,468,05

4.734,857,260.26

16,837,927.56

9,121,805.06

7,716,122.50H1

60 1 31,211,6

05.764,576,80

7.578,409,063.19

18,225,735.00

9,575,458.40

8,650,276.60H1

70 1 29,463,9

70.055,588,53

9.467,689,528.10

16,185,902.49

10,813,154.07

5,372,748.42H1

80 1 10,040,4

31.031,965,09

8.062,155,471.35

5,919,861.62

3,688,715.91

2,231,145.71H1

90 1 23,003,5

07.825,170,70

0.374,890,319.11

12,942,488.34

9,582,337.18

3,360,151.16H2

00 2 48,177,8

26.129,378,31

6.916,370,705.66

32,428,803.55

19,152,104.33

13,276,699.22H2

10 1 22,607,5

78.747,044,65

2.281,430,074.03

14,132,852.43

7,113,311.54

7,019,540.89C2 1 1 38,833,1

54.576,082,86

7.566,222,285.41

26,528,001.60

11,384,260.65

15,143,740.95C4 1 0 0.00 0.00 0.00 0.00 0.00 0.00

C3 1 1 37,121,737.25

6,899,286.98

6,509,297.11

23,713,153.16

11,246,101.51

12,467,051.65C5 1 0 0.00 0.00 0.00 0.00 0.00 0.00

254

CHAPTER 8 ANNEXES #

ANNEX 6: DEA VALUES IN THE EMERGENCY DEPARTMENT AREA

DMU

Cost of the emergency department

{1}

Exam rooms in

the emergen

cy

department {1}

Observa&onal rooms in the emergency department

{1}

Wait &me {1}

Equivalent pa&ent in the emergency department

{1}

H1 11.482.893 11 34 279 5.840

H2 5.254.950 7 12 224 2.793

H4 7.431.826 13 14 170,5 2.431

C1 8.154.466 10 31 223 4.144

H5 6.993.991 9 19 268 2.717

H6 4.506.345 6 6 192 1.062

H7 6.021.148 6 13 184 2.691

H8 12.765.080 16 24 269 6.439

H10 9.978.653 23 18 770 5.900

H11 13.973.359 12 30 163 5990

H13 3.914.840 22 26 220 2373

H15 4.857.260 21 13 232 2773

H16 8.409.063 10 18 263 2593

H17 7.689.528 14 21 250 3433

C2 6.222.285 23 53 271 2676

H22 25.016.119 21 36 239 8193

C3 6.509.297 28 14 170 2171

255

CHAPTER 8 ANNEXES #

ANNEX 7: DEA VALUES IN THE SURGICAL AREA

DMUMaterial for opera&ng rooms {1}

Opera&ng rooms {1}

Interven&onal rooms {1}

Delivery rooms {1}

Average delay in opera&ons

{0}

Pa&ents in opera&ng rooms {0}

H1 6245526.07 18 3 2 47 18207

H2 2580922.09 6 1 2 35 7576

H4 2227992.15 9 1 2 36 9978

C1 4179835.84 14 2 4 46 26946

H6 308016.37 3 2 1 32 2959

H7 2034221.53 11 2 2 26 8104

H8 7407857.20 16 0 3 41 30264

H10 9495045.10 23 3 2 41 31937

H11 8651537.13 27 4 3 54 34968

H12 2543074.34 8 6 3 51 6402

H13 3122029.21 11 3 2 41 8530

H14 5220799.24 13 2 2 36 13671

H15 2573493.30 9 4 2 36 8820

H16 2621816.77 6 1 2 30 7553

H17 3302366.64 10 1 2 38 12180

C2 4199690.68 12 0 2 29 17778

C3 3490562.59 14 1 2 41 12737

H22 13698170.90 40 16 3 81 58958

256

CHAPTER 8 ANNEXES #

ANNEX 8: DEA VALUES IN THE OUTPATIENT SERVICE AREA

DMUCOST OF

OUTPATIENT SERVICE AREA

{1}

CONSULTING ROOMS {1}

PATIENTS EQUIVALENT IN VISITS {0}

Delay in first visits {0}

H1 12,210,327.5581

5,891 49.5137

H2 8,152,432.17 83 6,709 66.4975

H4 3,410,860.52 51 2,400 40.2676

C1 8,848,327.22 75 8,098 40

H5 5,818,345.56 60 3,954 124.0129

H7 4,492,420.76 22 2,338 21.4839

H8 23,621,538.33 117 7,886 51.1814

H10 14,174,652.18126

8,753 35.962

H11 14,545,126.57 134 6,628 60.571

H12 4,747,112.90 43 3,023 65.085

H13 3,325,165.32 45 3,493 74.121

H14 8,714,839.59 58 4,469 57.4019

H15 4,468,054.73 35 2,942 60.8242

H16 4,576,807.57 30 2,350 23.0694

H17 5,588,539.46 76 4,092 58.9436

H18 1,965,098.06 38 2,318 136.1291

H19 5,170,700.37 50 3,040 48.6499

H20 9,378,316.91 63 5,634 34

C2 6,082,867.56 62 5,041 32.6287

H22 38,306,312.72 174 11,288 45.759

C3 6,899,286.98 71 4,160 42.9

257

Ph.D. Program in Business Administration

27th October 2014

Public-Private Partnerships in Healthcare. Evaluation of 10 years’ experiencein Spain.

Doctoral ThesisAntonio Clemente

Directors:

David Vivas Maria CaballerISBN: 978-84-606-8865-5

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