o. thonon , p. van herck , d. gillain n. laport , w. sermeus · 2014-07-17 · a nursing minimum...
TRANSCRIPT
O Thonon1 P Van Herck2 D Gillain1 N Laport1 W Sermeus2
University of Liegravege Belgium1
University of Leuven Belgium2
Research team (co-authors) Leuven University
P Van Herck N Robyns W Sermeus
Liege University
D Gillain N Laport O Thonon
The study was supported and funded by the Belgian Ministry of Public Health 2009-2012 wwwhealthfgovbe
(keyword Profi(e)l DI-VG)
Team amp acknowledgements
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 2
Outlines
Belgian Nursing Minimum Data Set (Be-NMDS)
What are Nursing Related Groups (NRG)
Development of NRG
Validation of NRG
NRG resources weighting
And now hellip whatrsquos next
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 3
A Nursing Minimum Data Set hellip
ldquoA minimum set of items of information with uniform definitions and categories concerning the specific dimension of professional nursing which meets the essential needs of multiple data users in the health care system (Werley et al 1986)rdquo
Data Sets in the world Australia Belgium Finland Ireland Portugal Switzerland USA hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 4
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS I (1988 - 2006)
Compulsory registration in all Belgian acute hospitals based on 4 data samples (15d) year
19 million nursing records since 1988 one of the largest nursing database in the world used at national level (MacNeela et al 2006)
Content 23 nursing interventions patient demographics nurse staffing data (FTE nurses qualification level)
Integrated in hospital reimbursement system for medical surgical units paediatrics ICU (65 of budget)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 5
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Research team (co-authors) Leuven University
P Van Herck N Robyns W Sermeus
Liege University
D Gillain N Laport O Thonon
The study was supported and funded by the Belgian Ministry of Public Health 2009-2012 wwwhealthfgovbe
(keyword Profi(e)l DI-VG)
Team amp acknowledgements
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 2
Outlines
Belgian Nursing Minimum Data Set (Be-NMDS)
What are Nursing Related Groups (NRG)
Development of NRG
Validation of NRG
NRG resources weighting
And now hellip whatrsquos next
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 3
A Nursing Minimum Data Set hellip
ldquoA minimum set of items of information with uniform definitions and categories concerning the specific dimension of professional nursing which meets the essential needs of multiple data users in the health care system (Werley et al 1986)rdquo
Data Sets in the world Australia Belgium Finland Ireland Portugal Switzerland USA hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 4
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS I (1988 - 2006)
Compulsory registration in all Belgian acute hospitals based on 4 data samples (15d) year
19 million nursing records since 1988 one of the largest nursing database in the world used at national level (MacNeela et al 2006)
Content 23 nursing interventions patient demographics nurse staffing data (FTE nurses qualification level)
Integrated in hospital reimbursement system for medical surgical units paediatrics ICU (65 of budget)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 5
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Outlines
Belgian Nursing Minimum Data Set (Be-NMDS)
What are Nursing Related Groups (NRG)
Development of NRG
Validation of NRG
NRG resources weighting
And now hellip whatrsquos next
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 3
A Nursing Minimum Data Set hellip
ldquoA minimum set of items of information with uniform definitions and categories concerning the specific dimension of professional nursing which meets the essential needs of multiple data users in the health care system (Werley et al 1986)rdquo
Data Sets in the world Australia Belgium Finland Ireland Portugal Switzerland USA hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 4
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS I (1988 - 2006)
Compulsory registration in all Belgian acute hospitals based on 4 data samples (15d) year
19 million nursing records since 1988 one of the largest nursing database in the world used at national level (MacNeela et al 2006)
Content 23 nursing interventions patient demographics nurse staffing data (FTE nurses qualification level)
Integrated in hospital reimbursement system for medical surgical units paediatrics ICU (65 of budget)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 5
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
A Nursing Minimum Data Set hellip
ldquoA minimum set of items of information with uniform definitions and categories concerning the specific dimension of professional nursing which meets the essential needs of multiple data users in the health care system (Werley et al 1986)rdquo
Data Sets in the world Australia Belgium Finland Ireland Portugal Switzerland USA hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 4
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS I (1988 - 2006)
Compulsory registration in all Belgian acute hospitals based on 4 data samples (15d) year
19 million nursing records since 1988 one of the largest nursing database in the world used at national level (MacNeela et al 2006)
Content 23 nursing interventions patient demographics nurse staffing data (FTE nurses qualification level)
Integrated in hospital reimbursement system for medical surgical units paediatrics ICU (65 of budget)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 5
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS I (1988 - 2006)
Compulsory registration in all Belgian acute hospitals based on 4 data samples (15d) year
19 million nursing records since 1988 one of the largest nursing database in the world used at national level (MacNeela et al 2006)
Content 23 nursing interventions patient demographics nurse staffing data (FTE nurses qualification level)
Integrated in hospital reimbursement system for medical surgical units paediatrics ICU (65 of budget)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 5
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Belgian Nursing Minimum Data Set (Be-NMDS)
Be-NMDS II (2008 - now)
78 nursing interventions based on Nursing Interventions Classification (NIC)
Same registration design as Be-NMDS I
Fully integrated and linked with the Hospital Discharge Data Set (HDDS) since 2008
Reimbursement scheme under review
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 6
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
From NIC to Be-NMDS II
Domains
Classes
Items interventions
Coding possibilities
Based on Nursing Interventions Classification (NIC) 2nd Ed classes J T U not considered for B-NMDS taxonomy Four main levels 6 domains 23 classes 78 interventions 91 coding possibilities (131 scoring possibilities)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 7
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Clinical validation of Be-NMDS II
International Nursing Language
Based on NIC framework
Expert Panels (N=89)
Selection of relevant classes (23)
Selection of relevant interventions for Belgium (286)
Translation into Be-NMDS (v 16 ndash January 2011)
78 items
classified in 6 domains and 23 classes
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 8
Source Sermeus et al International Journal of Medical Informatics (2005) 74 946mdash951
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 9
Selection of Be-NMDS II interventions
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Van de Heede etal International Journal of Nursing Terminologies and Classification 20 2009 122-131
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 10
Example of Be-NMDS II items (class B elimination management)
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Nursing care time-weights of Be-NMDS
Delphi study 895 candidates
678 participants (response rate = 76) 2 rounds
3 systematic questions per nursing activity (N=154) What is modal time (most frequent in daily practice) necessary to
carry out the considered nurse activity
What is minimal or maximum time necessary to carry out the considered nurse activity
What are the possible elements to justify this temporal variation in the carrying of the considered nurse activity
Collecting an average of 247 ldquotime responsesrdquo per nursing activity
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 11
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Example of nursing care time-weights per Be-NMDS II intervention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 12
Validated by Delphi panel - 678 participants - by e-mail - 2 rounds
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Nursing Related Groups (NRG)
Initiated at 3rd Nusing amp Computers June 1988 Dublin (Sermeus et
al 1988)
Used in tasks of W Fisher (Switzerland 2002) amp D Hunstein
(Germany 2007)
Patient classification system based on the grouping of the patientrsquos
nursing profile per episode of care according to its clinical and nurse
resources homogeneity
Note an episode of care (EC) is the lenght of patientrsquos stay within one ward and lasts 24h or less A patient can have one or more ECs during one patient day
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 13
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Available data for NRGs construction
Be-NMDS II
Year 2009
133 hospitals 231 campus gt 2600 care units
gt 1375000 Episodes of Care (EC)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 14
N Episodes of Care 1378326
N Hospitals 133
Avg EC hospital 10363
Median EC hospital 8481
25th centile EC hospital 5146
75th centile EC hospital 13524
Minimum EC hospital 139
Maximum EC hospital 50236
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Methods
Step 12
Development of Major Nursing Categories (MNCs)
Based on clustering technique ndash FASTCLUS (Andenberg 1973 Hartigan 1975)
Focused on clinical homogeneity of the nursing profile (91) + length of the episode of care (1) 92 variables in total
8 MNCs built
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 15
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Methods
Step 22
Development of Nursing Related Groups (NRGs)
Based on Decision Tree methodology - CART Classification And Regression Tree (Hastie et al 2011)
Target-variable time per intervention
92 NRGs built
CMG_DIAG_CODE$
Terminal
Node 1
STD = 4850189
Avg = 3512012
W = 594300
N = 5943
CMG_DIAG_CODE$
Terminal
Node 2
STD = 7696015
Avg = 7020928
W = 575800
N = 5758
CMG_DIAG_CODE$
Node 3
CMG_DIAG_CODE$
STD = 6646166
Avg = 5238736
W = 1170100
N = 11701
CMG_DIAG_CODE$
Terminal
Node 3
STD = 12945319
Avg = 10550735
W = 674300
N = 6743
CMG_DIAG_CODE$
Terminal
Node 4
STD = 20118029
Avg = 15034500
W = 431100
N = 4311
CMG_DIAG_CODE$
Node 4
CMG_DIAG_CODE$
STD = 16274292
Avg = 12299378
W = 1105400
N = 11054
CMG_DIAG_CODE$
Node 2
CMG_DIAG_CODE$
STD = 12799524
Avg = 8668678
W = 2275500
N = 22755
CMG_DIAG_CODE$
Terminal
Node 5
STD = 30899049
Avg = 24156100
W = 86000
N = 860
CMG_DIAG_CODE$
Terminal
Node 6
STD = 41738146
Avg = 43364559
W = 29400
N = 294
CMG_DIAG_CODE$
Terminal
Node 7
STD = 60547006
Avg = 72647359
W = 5100
N = 51
CMG_DIAG_CODE$
Node 6
CMG_DIAG_CODE$
STD = 46200550
Avg = 47693328
W = 34500
N = 345
CMG_DIAG_CODE$
Node 5
CMG_DIAG_CODE$
STD = 37492941
Avg = 30894994
W = 120500
N = 1205
Node 1
CMG_DIAG_CODE$
STD = 15807598
Avg = 9786468
W = 2396000
N = 23960
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 16
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Development of MNCs en NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 17
MNC NRGs N ECs classified
010 011 012 013 014 015 016 017 018 019 0110 0111 0112 179 290
020 021 022 023 024 025 026 027 028 029 0210 54 033
030 031 032 033 034 035 036 037 038 039 0310 0311 0312 0313 303 375
040 041 042 043 044 045 046 047 048 049 0410 0411 0412 0413 69 151
050 051 052 053 054 055 056 057 058 059 0510 0511 0512 156 749
060 061 062 063 064 065 066 067 068 069 0610 0611 585 298
070 071 072 073 074 075 076 077 078 079 0710 0711 0712 28 040
080 081 082 083 084 085 086 087 088 089 2 390
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Description of MNCs
MNC Description
01 Pre post operative care post-delivery (12 NRGs)
02 Observation follow-up and education especillay at end of stay (10 NRGs)
03 Chronic care with high levels of dependency (13 NRGs)
04 Acute care and monitoring ndash highly technical (13 NRGs)
05 Independent care transfers (12 NRGs)
06 Rehab nursing care (11 NRGs)
07 Intensive care (12 NRGs)
08 Rest group (9 NRGs)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 18
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Ways for visualization
Text
title summary main text and details
Graphic
lsquofingerprintrsquo (ridit scoring Bross 1958)
Excel sheet with items distribution
19
Description of MNCs NRGs
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
NRGs validation
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
NRGs weighting based on nursing staff allocation and qualification mix
Focus on resource weight by patient groups using a Delphi study
92 NRGs 17 questions (Q amp q) per NRG
205 participants 2 rounds
each NRG was analyzed on average 50 times
Collected variables per NRG required staffing (NPPD) competencies (10) and skill-levels (5)
quantitative results on two levels (optmax) number of patients per day for each NRG
qualitative results median level for each 10 competencies
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 21
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Allocation of 10 competencies per nursing intervention per NRG
ID Comptencies
I Information and Education
II Knowledge and Best Practice (EBN)
III Clinical reasoning and problem solving
IV Selfcare support
V Assessment
VI Care Planning
VII Implementation
VIII Follow up
IX Communication en relationships
X Technical skills
PROFESSIONAL ETHICAL LEGAL PRACTICE
Accountability 5
Ethical Practice 8
Legal Practice 3
CARE PROVISION AND MANAGEMENT
Principles of Care Provision 13
a Promotion of Health 3
b Assessment 3
c Planning 7
d Implementation 4
e Evaluation 3
f Therapeutic Communication and Interpersonal Relationships
7
Leadership and Management 9
g Safe Environment 6
h Delegation and Supervision 4
i Inter-Professional Health Care 6
PROFESSIONAL PERSONAL amp QUALITY DEVELOPMENT
Enhancement of the Profession 8
Quality Improvement 2
Continuing Education 3
22
Source Nursing Care Continuum Framework and Competencies Copyright copy 2008 by ICN - International Council of Nurses
Delphi study competencies by item 113 participants
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Validation of NRG resources weights
Criterion validity
Differences between NRGs
Wilcoxon signed-rank observed nurse staffing in hospitals vs optmax nurse staffing from Delphi
Correlation with NRG resource weight and NRG-sum of nursing care time-weights per intervention r=09
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 23
Paires N NRG Mean ∆Means SD pWilcoxon
NPPD_M_obs 79 667
NPPD_max 79 878 -21 372 P lt 0000
NPPD_M_obs 79 667
NPPD_opt 79 686 -018 352 P lt 001
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Whatrsquos next
Integration in hospital reimbursement system
00
02
04
06
08
10
12
14
16
04
10
04
01
04
02
04
05
04
12
04
06
04
13
01
07
01
02
01
10
05
01
05
02
05
08
02
01
02
03
02
02
06
01
02
04
01
09
06
06
03
02
06
05
06
11
01
06
06
07
05
10
01
12
05
11
06
08
03
10
03
11
08
04
05
06
02
05
08
01
08
03
02
07
03
12
02
10
08
08
07
03
07
04
07
06
07
10
07
11
08
05
Nursing hours per patient day per NRG High sensitivity 019 (NRG 0410) to 1406 (NRG 0709)
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 24
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Be-NMDS II products
Nursing time-weights by care profiles
Nursing cost-weights by NRG (Q amp q resources)
Using for nurses allocation in care units based on patient needs
Using for nursing financing implementation amp schemes under review
Linking with DRGs
Evidence that nursing care nursing costs cannot be predicted from DRGs (best models explain a 20-25 variability)
Cost are explained by the combination of DRGs + NRGs (major increase in explanatory power ndash Welton amp Halloran JONA 2005)
Complementary not opposed
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 25
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Arguments
PROS
More sensitive than Be NMDS I
Based on Delphi data (staff amp competencies)
Validated on large data sets
Validated by large groups of experts
CONS
Complex
Financial impact unknown simulations needed
Link with DRGs not fully established
Delphi study too subjective more validation needed
NRGs not transparent but hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 26
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
Conclusions
From nursing interventions to care profiles hellip
hellip from care profiles to MNCs amp NRGs
Based on quantitative and qualitative research
Validated by nursing sector
Many discussions pros amp cons more healthcare financing
policy than scientific andor management arguments hellip
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 27
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention
SIEPC 7-9 April 2013 - Riyadh Saudi Arabia 28
Further contacts
olivierthononchuulgacbe ndash Follow me on or
waltersermeusmedkuleuvenbe
Thank you to all for your attention