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Composite quantitative analysis
of international healthcare
accreditation survey data
Australian Institute of Health Innovation

Max Moldovan, Charles Shaw and Jeffrey Braithwaite
Healthcare accreditation:
a process that a health care institution, provider, or program
undergoes to demonstrate compliance with standards
developed by an official agency.
[Miller-Keane Encyclopedia and Dictionary of Medicine, Nursing, and Allied Health,
Seventh Edition. © 2003 by Saunders, an imprint of Elsevier, Inc. All rights reserved.]
Study and data set description
The first international healthcare accreditation survey
was undertaken in 2000, commissioned by the World
Health Organisation [WHO Review 2003, WHO/EIP/OSD/2003.1]
The current data set has been collected over 2009-2011
44 accreditation agencies, located in 38 countries
10 broad categories of attributes (e.g. “Policy and
governance” and “Funding of the organization”)
173 questions leading to categorical, date/numeric or
free text responses
Geographical and age distribution of accreditation agencies*

Older than 20 years

Between 10 and 20 y.o.

* If more than one program per country, the oldest program is displayed

<10 y.o.;
Methods of analysis

Stratified categorical analysis
Supervised learning via penalised regression
Unsupervised learning via hierarchical clustering
Stratified categorical analysis
Organisations are stratified into two or more
groups based on observed factors
Responses arranged into contingency tables
Fisher’s exact conditional testing is applied
The results are assessed based on the plots of
sorted P-values
Stratified categorical analysis
Stratification factors:
1.Purchasing Power Parity adjusted GDP per capita
2.Government relatedness
3.Regional factor
4.Age of organisation
5.Country of origin population
pol1
pol3
tra5
rep1
pol7
agh12
rep10
phc14
fun1
pol16
phc12
rep4
int6
phc17
fun5
dev8
rep13
int3
rep11
fun4
tra1
phc8
agh8
pol19
pol10
rep2
pol6
agh14
pol13
rep12
fun2
rep16
tra9
fun8
fun6
phc16
pol18
rep5
tra6
fun10
rep3
int1
pol17
fun9
rep7
rep14
rep15
agh16
agh17

0.0

0.2

0.4

P-values
0.6

0.8

1.0

Stratified categorical analysis
GDP PPP factor (US$20,000 split value)

0.05

Question IDs
Supervised learning via
penalised regression
R package penalized: Penalised regressions via L1
and L2 penalties [Goeman (2010): Biometrical Journal 52 (1), 70–84]
L1: LASSO; L2: ridge; L1 and L2: elastic nets
Only L1 penalty (LASSO) has been used
Supervisory (response) variable is an ordinal fourlevel categorical variable defined by field experts
(similar to phenotypes defined by clinical experts)
Explanatory variables are categorical responses
coded as factors
Logistic link function has been used
Supervised learning via
penalised regression
LASSO [Tibshirani (1996): J. Royal. Statist. Soc. B 58 (1): 267–288] :
Supervised learning via
penalised regression
Vitality categorisation (“phenotype”):
0 – on “life support” and unlikely to survive
1 – hardly functional and deteriorating
2 – functional, with signs of improvement
3 – alive and well
Supervised learning via
penalised regression
The set of shifted (“phenotype”) response variables:
yi,1 – refined “deprived”: 0 vs. 1-2-3
yi,2 – mid split: 0-1 vs. 2-3
yi,3 – refined “winners”: 0-1-2 vs. 3
Supervised learning via
penalised regression

0.5
0.0
-0.5

coefficient

1.0

1.5

LASSO path

4.0

3.5

3.0

2.5

2.0
lambda1

1.5
tra5a
rep1d
rep11b
pol3a
pol3c
pol16a
pol10b
pol1c
rep14a
rep13g
pol1a
rep5c
rep13h
tra9e
rep10b
pol18e
pol13b
fun4c
fun4b
pol19c
fun1f
pol19b
pol3a
pol16c
rep16a
pol19d
tra6a

0.5

1.0

1.5

2.0

lambda1
2.5

3.0

3.5

4.0

Supervised learning via
penalised regression
LASSO path: Entrance distances
tra5a
rep1d
rep11b
pol3a
pol3c
pol16a
pol10b
pol1c
rep14a
rep13g
pol1a
rep5c
rep13h
tra9e
rep10b
pol18e
pol13b
fun4c
fun4b
pol19c
fun1f
pol19b
pol3a
pol16c
rep16a
pol19d
tra6a

0.5

1.0

1.5

2.0

lambda1
2.5

3.0

3.5

4.0

Supervised learning via
penalised regression
LASSO path: Entrance distances
tra5a
rep1d
rep11b
pol3a
pol3c
pol16a
pol10b
pol1c
rep14a
rep13g
pol1a
rep5c
rep13h
tra9e
rep10b
pol18e
pol13b
fun4c
fun4b
pol19c
fun1f
pol19b
pol3a
pol16c
rep16a
pol19d
tra6a

0.5

1.0

1.5

2.0

lambda1
2.5

3.0

3.5

4.0

Supervised learning via
penalised regression
LASSO path: Entrance distances
Supervised learning via
penalised regression
“Winners” refined

50‐50

“Losers” refined

tra5a

pol3a

pol3a

rep1d

pol13a

pol10b

rep11b

fun5b

pol13a

pol3a

dev8a

rep11b

pol3c

tra9b

pol7a

pol16a

rep11b

fun1a

pol10b

pol1a

int6b

pol1c

rep14b

tra1c

rep14a

tra5a

fun10a

rep13g

pol16a

pol7d

pol1a

fun4d

pol19a

rep5c

pol16d

pol16d

rep13h

fun1a

rep3a

tra9e

pol1c

rep15a

rep10b

fun8g

tra1b

pol18e

pol10b

fun8g

pol13b

pol6k

fun4d
Supervised learning via
penalised regression
“Winners” refined

50‐50

“Losers” refined

tra5a

pol3a

pol3a

rep1d

pol13a

pol10b

rep11b

fun5b

pol13a

pol3a

dev8a

rep11b

pol3c

tra9b

pol7a

pol16a

rep11b

fun1a

pol10b

pol1a

int6b

pol1c

rep14b

tra1c

rep14a

tra5a

fun10a

rep13g

pol16a

pol7d

pol1a

fun4d

pol19a

rep5c

pol16d

pol16d

rep13h

fun1a

rep3a

tra9e

pol1c

rep15a

rep10b

fun8g

tra1b

pol18e

pol10b

fun8g

pol13b

pol6k

fun4d
Supervised learning via
penalised regression
“Winners” refined

50‐50

“Losers” refined

tra5a

pol3a

pol3a

rep1d

pol13a

pol10b

rep11b

fun5b

pol13a

pol3a

dev8a

rep11b

pol3c

tra9b

pol7a

pol16a

rep11b

fun1a

pol10b

pol1a

int6b

pol1c

rep14b

tra1c

rep14a

tra5a

fun10a

rep13g

pol16a

pol7d

pol1a

fun4d

pol19a

rep5c

pol16d

pol16d

rep13h

fun1a

rep3a

tra9e

pol1c

rep15a

rep10b

fun8g

tra1b

pol18e

pol10b

fun8g

pol13b

pol6k

fun4d
Unsupervised learning via
hierarchical clustering
R function daisy (package cluster) has been used
207 categorical attributes have been coded as
asymmetric binomials
Dissimilarity matrix has been computed based on
Gower’s dissimilarity coefficient [Gower (1971) Biometrics 27,
857–874 ]
Dendrograms are based on Ward’s hierarchical
clustering method
Weights are imposed as an inverse of attribute
counts within each category
Bosnia_Herzegovina_AAQI
Kazakhstan
Portugal
France
South_Korea
Bulgaria
Lebanon
Philippines
Romania
Saudi_Arabia
Colombia
Croatia
Spain
Czech_Republic
Netherlands
Brazil
India
USA_JCI
Mongolia
Kyrgyzstan
Serbia
Bosnia_Herzegovina_AKAZ
Poland
South_Africa
Germany
Jordan
Argentina
Japan
Switzerland
USA_DNVHC
Taiwan
USA_Join_Comission
Denmark
Thailand
Australia_ACHS
Malaysia
Australia_QIC
England
New_Zealand_Telarc
Albania
Canada
Lithuania
Australia_AGPAL
New_Zealand_HDANZ

Unsupervised learning via
hierarchical clustering
All categorical attributes
Bosnia_Herzegovina_AAQI
Kazakhstan
Portugal
France
South_Korea
Bulgaria
Lebanon
Philippines
Romania
Saudi_Arabia
Colombia
Croatia
Spain
Czech_Republic
Netherlands
Brazil
India
USA_JCI
Mongolia
Kyrgyzstan
Serbia
Bosnia_Herzegovina_AKAZ
Poland
South_Africa
Germany
Jordan
Argentina
Japan
Switzerland
USA_DNVHC
Taiwan
USA_Join_Comission
Denmark
Thailand
Australia_ACHS
Malaysia
Australia_QIC
England
New_Zealand_Telarc
Albania
Canada
Lithuania
Australia_AGPAL
New_Zealand_HDANZ

Unsupervised learning via
hierarchical clustering
All categorical attributes
France
South_Korea
Bosnia_Herzegovina_AKAZ
Bulgaria
Lebanon
Albania
Philippines
Poland
Lithuania
Romania
Kazakhstan
Portugal
Serbia
Bosnia_Herzegovina_AAQI
Kyrgyzstan
Saudi_Arabia
Spain
Netherlands
Mongolia
Argentina
Switzerland
Japan
New_Zealand_Telarc
South_Africa
Jordan
Czech_Republic
Germany
Brazil
New_Zealand_HDANZ
England
Canada
USA_JCI
Australia_QIC
Malaysia
Taiwan
USA_Join_Comission
Australia_AGPAL
USA_DNVHC
India
Colombia
Croatia
Thailand
Australia_ACHS
Denmark

Unsupervised learning via
hierarchical clustering
Policy and governance
Example of results triangulation
It has been identified that the legal status of organization
is associated with the GDP PPP per capita factor
(stratified analysis) => This association can be attributed
to economic/social factors beyond the direct control
Relatively wealthy Portugal fell into a “deprived” cluster in
the Policy and Governance motivated dendrogram (cluster
analysis)
POL3a (What is the legal status of the accreditation
organization?: government agency) has been identified as
highly important attribute contributing to failure (LASSO)
It can be recommended to change the legal status
Challenges and extensions
Missing variables
Attributes interactions
Nonlinear associations
Challenges and extensions
Missing variables
Attributes interactions
Nonlinear associations
Risk factors analysis
Predictive modeling
Regulatory impact analysis
Questions

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