Challenges for HIS

Learning objectives

• Know about a main challenge for HIS: lack of access

• Know about the reasons for this• Know how this influence data quality• Know about some data quality issues

The goal of the HIS

• “is to produce relevant information that health system stakeholders can use for making transparent and evidence-based decisions for health system interventions” (HMN)

• The challenges here are many:– You need access to data– You need quality data– You need to know what to do with it

This is not the usual case…

Picture: HMN

Picture: HMN

The lack of access to health information

Why?

Multileveled fragmentation

• Health programs• Health information domains• Public/private• Many electronic formats (and paper still very

common)

Fragmentation of health programs

• One information stream for Malaria program• One information stream for TB program• One information stream for… etc etc etc

• Surveys

• Data not available for comparison. Double counting, low data quality

• Country X: three national figures of HIV+ rate. All different…

Dental unit 1PAWC

City HealthClinic 1

54 private medical pract.

GeriatricServices

MOU(Midwife&

obstetric unit)PAWC

23 private dental pract.

12 private pharmacies

Private hospital:31 medical specialists

Day HospitalDNHPD

UWC OralHealth Centre

City HealthClinic 2

City HealthClinic 3

City HealthClinic 4

City HealthClinic 5

Dental unit 2PAWC

Dental unit 3PAWC

12-15 NGOs

SchoolHealth

DNHDPPretoria

Groote SchuurHospital

PAWC

DNHDPWestern Cape City Health

MITCHELL’S PLAIN

Environmentaloffice

MandalayMobile clinic

RSCYouth

Health Services

PsyciatrichospitalPAWC

RSC

Outsidehospitals

BirthsDeathsNotifiable diseases

New /emergingflow of information

Apartheid legacy: a fragmented and top down health structureno local governance & control of information

Example: South Africa in mid 90s

Why program fragmentation?

• Health services inherently fragmented due to high level of specialization

• Donors (both from necessity and ignorance)• WHO is highly fragmented itself• Interests and ownership• Leads to lack of transparency, some people

thrive on that (corruption)

Many official actors: risk of fragmentation• Ministry of Health is not alone…

– Central Statistics office (census)– Ministry of Local Government (run the clinics)– Ministry of Education (school health programs)– Ministry of Defence (military clinics)– Ministry of Justice (civil registration)– Special units on for example HIV

• In Norway?

Health Statistics

District - DHT

Facility 1 Facility 2 Facility n

IDSR – NotifiableDiseases

PMTCT

EPI

STD

Home Based Care

Nutrition Nutrition

ARV

MCH

Family Planning

HIV/AIDS

TBSchool Health

Mental HealthAnd more …

Facility 3

Botswana: Pre-intervention – Fragmentation – No shared IST resources “converging” at district level - Fragmentation at central level

/ HISP

Health information domain fragmentation• Various subsystems deal with different types

of data– Patient data: name, address etc– HR data: name, diplomas, employment history– Logistics: drug batch No., expiry date

• Has (naturally) led to different systems• But the link between them has been

neglected

A possible example: different information domains.

Others

Statistics

Patient data Human Resource data

No linkage!

Public/Private fragmentation

Why public/private fragmentation?

• Taxation reasons• Business ”secrets”• Lack of capacity at MOH to follow up

– Not one private sector, or umbrella organization– Private clinics, traditional medicine, religious

organizations, NGOs

• No incentives for private sector to share• Private sector often not very formal• Lack of policies and legal frameworks

How does fragmentation influence data quality?

Fragmentation linked to data quality

• Vicious cycle:1. Low data quality

2. Do not trust it

3. Build a new system for your own needs

4. Duplication, and higher workload for those collection data (nurses)

5. Leads to low quality data

• Lack of access is poor quality itself: missing data (as in example of Western Area above) affects indicators

limited capacity to manage or analyse data

Using evidence not perceived as a winning strategy

A vicious cycle A vicious cycle

Data not trusted

Weak demand

Weak HIS

Poor data quality

Limited investment in HIS

Decisions not evidence-basedDonors get their own

Fragmentation

Data Quality

• Is the data complete?• Is the data on time?• Is the data correct?• (are we collecting the right data?)

• Surprisingly often the answer is no…

A few reasons why data quality is low

• Fragmentation, which together with excessive amounts being collected leads to– Less time, less interest, in collection process

• Many manual steps• Unclear definitions• Lack of use: no incentive to improve quality• More?

Correct? A real example

• Data is produced at the service level. That usually means the nurse.

• For each step of manual aggregation and counting, there is a possibility for human errors

• There are 4 steps before data is ”safe” in the database:– Nurse ticking off slots in a tally sheet– These ticks counted into a total– This total written on the MMRCS Facility

Summary form– The data recorded into DHIS

Two steps of data exchangeFrom Facility Tally Sheet Total, to MMRCS Summary form, to DHIS

ANC 1 Bednets given ANC 2 ANC 3

Tally Sheet

Sum.Form

DHIS Tally Sheet

Sum.Form

DHIS Tally Sheet

Sum.Form

DHIS Tally Sheet

Sum.Form

DHIS

Jan 26 20 20 26 20 20 10 8 8 7 7 7Feb 40 40 40 40 40 40 12 12 12 10 10 12Mar 12 12 12 0 0 0 15 15 20 4 4 10Apr 24 24 24 0 0 8 8 8 13 13 13 2May 31 30 30 0 0 0 11 10 10 6 6 6June 15 15 15 0 0 0 6 6 6 5 5 5July 12 13 13 0 0 0 6 9 9 2 4 4Aug 8 8 8 0 0 0 13 13 13 12 12 12

Analysis

• 14 errors from 32 data entries (4 elements, 8 months)

• 43.75%.....• 6 mistakes during entering to DHIS• 8 mistakes during exchange of data from tally

sheet to summary form• Not counting errors in tally sheet

aggregation....(or those figures never ending up in the tally sheet in the first place)

More examples

4 deliveries checked off.......but the number recorded is 0!

7 IPT 1st doses....

... recorded as 2

This example is not uniqueWhat are the consequences?

Key points

• Lack of access to health information is a major issue

• Fragmentation is a main reason for this• Fragmentation at many different levels• Data quality is often a big issue

HMN study

• Mostly countries from low and middle-income countries

• Main findings– Data management and Resources are areas most

countries struggle

Overall score, 54 countries

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Across income levels...

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Low Income

Lower Middle Income

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Common problems I

• Policies for HIS– Access– Routines– Ownership– Standards

• Human resources– With right skills?– HIS Staffing not prioritized

Common problems II

• Data management– Fragmented, no central HIS unit– Appropriate technology

• Information use– Too much collected, too little used– Little incentive to use information locally