A panel discussion on health information systems at presenting the related issues between health informatics and health systems research.

1. Informatics for Health Policy and Systems Research:!
Lessons Learned from a Study of Healthcare Financing!
Cross-subsidization in Thai Public Hospitals
Borwornsom Leerapan, MD PhD!
!
JITMM2014 & FBPZ8!
Bangkok, Thailand!
December 2, 2014
Pix source: workwithbrianandfelicia.com

2. Special
thanks
to:
Ø Pha1a
Kirdruang,
Ph.D.
Ø Thaworn
Sakulpanich,
M.D.
Ø Patchanee
Thamwanna
Ø Utoomporn
Wongsin
Ø NutniAma
Changprajuck
Ø Health
Insurance
System
Research
Office
(HISRO)
&
Health
System
Research
InsAtute
(HSRI)
2

3. PresentaAon
Outline
1. Introducing
Health
Policy
&
Systems
Research
(HPSR)
– Purposes
of
HPSR
– Overview
of
HPSR
methodology
&
Data
for
HPSR
2. Example:
Study
of
Cross-­‐subsidizaAon
of
Health
Services
in
Thai
Public
Hospitals
– Study
objec?ves,
methods,
results
3. Discussion:
InformaAon
Systems
for
“DeterminaAon”
– Implica?ons
for
policy
and
prac?ces
– Informa?cs
needed
for
future
HPSR
3

4. “What
exactly
is
HPSR?”
Pix source: online.wsj.com

5. New
Health
Research
Mapping?
Source: Hoffman et al. (2012).

6. New
Health
Research
Mapping?
Different
kinds
of
knowledge
needed
Source: Hoffman et al. (2012).

7. “The
Systems”
• The WHO Six Building Blocks” of health (services) systems
Source: WHO )2012); de Savigny & Adam (2009); Scheerens and Bosker (1997); Pix source: humanrevod.wordpress.com

8. Different
Levels
of
Health
Systems
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.

9. Health
Systems
&
Health
Policy
• Terrain of Health Policy and Systems Research
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.

10. What
Is
&
What
Is
Not
HPSR?
Research “on” health systems
VS.
Research “for” Health systems
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.

11. Research
Strategies
in
HPSR
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.

12. Research
Strategies
in
HPSR
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.

13. Example
of
HPSR:
Study
of
Healthcare
Cross-­‐subsidizaAon
in
Thai
Public
Hospitals
Pix source: online.wsj.com

14. Financing
of
Thai
Healthcare
System
CSMBS SSS UCS Motor Vehicle
Victim
Protection
Law
Private Health
Insurance
Feature State/Employer
welfare
Compulsory
heath insurance
with state
subsidies
State welfare Compulsory
heath insurance
for vehicle
owners
Voluntary health
insurance
Targeted groups
of beneficiaries
Civil servants,
state enterprise
employees and
dependents
Employees in
private sector and
temporary
employees in
public sector
Thai citizens
without the
coverage of
CSMBS & SSS
Victims of
vehicle accidents
General public
Source of
financing
Govt. budget
Tri-party
(Employee,
employer and
govt. budget)
Govt. budget
Vehicle owners Household
Method of
payment to
health facilities
Fee-for-service Capitation and
Fee-for-service
Capitation and
Fee-for-service
Fee-for-service Fee-for-service
Major problems Rapidly and
constantly rising
costs
Covering while
being employed
only
Inadequate
budget
Redundant
eligibility and
slow
disbursement
Redundant
eligibility and
slow
disbursement
Source: Adapted from Wibulpolprasert et al. (2011). Thailand Health Profile 2008-2010.

15. Financing
of
Thai
Healthcare
Systems
CGD
(CSMBS),
NHSO
(UCS)
Taxes Payers
Employer-based
private health
insurance
Individual &
Employer’s
private health
insurance
(Voluntary)
Hospitals
Medical
Specialists
Generalists
& PCPs
Social
Security
Office (SSS)
Patients paying out-of-pocket
Ambulatory
Facilities
Payment Mechanisms:
Salary, Fee-for-Service,
Global Budget,
Capitation, DRGs, etc.
Providers in
Public & Private Sector
Commercial
Insurance
Companies
Motor vehicle’s owners
(Mandatory by the Motor
Vehicle Victim Protection Law)

16. of the out-patient expenditure during the second period showed an upward trend and
had very rapid growth in the last two years, 2006 and 2007 (graph 2.5).
With respect to expenditure per patient, this study can merely consider the average in-patient
Study
RaAonale
expenditure, because of data limitations. According to data from the electronic
payment system, the average in-patient expenditure in 2003-2006 increased over time as
shown in graph 2.6.
CSMBS Expenditure in the fiscal years 1996-2007
13,587 15,502
16,440
15,253
17,058 19,181
20,476 22,686
8,761 9,877 10,574 9,048 10,050 11,058 10,967
4,826 5,625 5,866 6,206 7,007 8,123
50,000
45,000
40,000
35,000
30,000
25,000
20,000
15,000
10,000
5,000
0
1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007
Million Baht
Figure
source:
Benjaporn
(2007)
46,481
15,649
14
Graph 2.4: CSMBS expenditure during the fiscal years 1996-2007
9,509
26,043
11,350 13,905
37,004
29,380
16,943
21,896
30,833
11,335 12,138 12,437 15,109
Year
Out-patient In-patient Total
Source: The Comptroller General’s Department and the Government Fiscal
Management Information System (GFMIS)
Note: 1 Euro = 49.4450 Baht, as of January 8, 2008
Ø Common
assump?ons
of
what
causes
increasing
healthcare
expenditures:
• Overuse
of
NED
drug?
• Overuse
of
brand-­‐named
drugs?
• Limited
EBM
prac?ces?
• Corrup?on
in
healthcare
sector?
Ø Cross-­‐subsidiza,on
can
be
a
missing
piece!
16

17. Study
RaAonale
Ø “Do
hospitals
use
payments
of
a
type
of
health
services
to
subsidize/support
financing
of
other
services?”
• If
so,
how?,
at
which
level?,
at
what
degree?
Figure
source:
www.be2hand.com;
www.imdb.com
17

18. Literature
Review
Ø Concepts
of
“cross-­‐subsidiza?on”
or
“cost-­‐shi^ing”
from
developed
countries
such
as
the
U.S.
(Morrisey
1994,
Cutler
1998,
Dranove
1988,
Feldman
et
al.
1998,
Frakt
2010
&
2011).
Ø Such
theorec?cal
concepts
might
not
be
applicable
in
Thailand’s
healthcare
systems,
especially
that
Thai
public
hospitals
do
not
have
the
ability
to
set
prices
by
themselves.
Ø There
was
no
empirical
study
of
cross-­‐subsidiza?on
in
the
contexts
of
Thai
healthcare
systems.
18

19. Study
ObjecAves
1. To
explore
mo?va?ons
and
exis?ng
prac?ces
of
the
administrators
of
Thai
public
hospitals
that
poten?ally
can
lead
to
cross-­‐subsidiza?on (“to
use
payments
of
a
type
of
health
services
to
support
financing
of
other
services”).
2. To
develop
mental
models
of
the
administrators
of
Thai
public
hospitals
regarding
organiza?onal
responses
to
healthcare
financing
policies.
3. To
demonstrate
an
empirical
evidence
related
to
cross-­‐
subsidiza?on
at
the
hospital
level,
including
the
cost
difference
and
the
difference
of
excess
of
revenues
over
expenses
among
health
schemes.
19

20. Methodology:
Research
Design
Ø No
empirical
study
of
cross-­‐subsidiza?on
in
the
contexts
of
Thai
healthcare
system.
Ø Concepts
from
developed
countries
such
as
the
U.S.
might
not
be
applicable
in
Thailand.
Ø Mixed-­‐methods
research,
with
the
concurrent
embedded
research
design
(Creswell
et
al.,
2004).
Ø Qualita,ve
study:
the
mental
models.
Ø Quan,ta,ve
study:
an
empirical
evidence
related
to
cross-­‐subsidiza,on
at
the
hospital
level.
20

21. Methodology:
“Mixed
Methods”
Ø Mixed-­‐methods
research
with
concurrent
embedded
design,
which
quan?ta?ve
data
analysis
is
used
to
compliment
as
the
qualita?ve
data
analysis.
Source: Creswell (2009). Research design: Qualitative, quantitative, and mixed methods approaches. 3rrd ed.
21

22. Methodology:
Source
of
Data
Ø Data
was
based
on
three
selected
public
hospitals:
Ø Two
medical
centers
with
1,000
and
1,134
beds
Ø One
teaching
hospital
with
1,378
beds.
Ø Hospitals
were
purposefully
selected,
based
on
the
accessibility
to
the
hospital
administrators
and
the
availability
of
the
datasets
of
unit
cost,
claims,
and
reimbursement.
22

23. Methodology:
Data
Ø QualitaAve
data:
Ø Semi-­‐structure
interviews
and
focus-­‐group
interviews.
Ø 30
key
informants
who
are
responsible
for
the
administra?on
of
the
three
hospitals.
Ø Verba?m
was
transcribed
and
analyzed
using
ATLAS.?
7.
Ø QuanAtaAve
data:
Ø Secondary
data
of
inpa?ent
care,
collected
at
the
pa?ent
level,
from
the
two
medical
centers.
Ø Unit-­‐cost,
charge,
reimbursement,
pa?ent’s
health
scheme,
DRG
codes,
and
basic
demographic
characteris?cs.
Ø Analysis
was
conducted
using
Stata
12.
23

24. Research
Findings
Pix source: online.wsj.com

25. QualitaAve
Analysis
Ø Construc?vist
grounded
theory
(Chamaz,
2005;
2006)
Ø Coding
process
(Strauss
&
Corbin
1990)
25

26. QualitaAve
Findings
Ø 13
sub-­‐themes,
categorized
into
4
emerging
themes.
26
Sub-­‐themes
Themes
Varied
understanding
of
cross-­‐subsidiza?on,
Unclear
financing
for
non-­‐healthcare
missions
Different
understanding
of
ajtudes
towards
cross-­‐subsidiza?on
concepts
Inadequate
reimbursement,
Non-­‐performing
loan,
Unequal
nego?a?on
power
Obstacles
facing
management
due
to
policies
of
the
payers
Conflic?ng
roles
between
quality
&
equity-­‐
focus
and
efficiency-­‐focus,
Limited
informa?on
to
manage
prices
and
cost
Obstacles
facing
management
due
to
organiza?onal
limita?ons
To
be
missions-­‐driven
organiza?on,
To
focus
more
on
efficiency
than
revenues,
To
do
public
funds
raising,
To
control
the
volume
of
certain
groups
of
pa?ents
when
feasible,
To
advocate
changes
of
the
payer’s
policies
Organiza?onal
responses
to
policies
of
the
payers

27. QuanAtaAve
Analysis
Ø Analyze
the
cost
differences
across
health
schemes
Ø By
using
descrip?ve
sta?s?cs
and
a
regression
analysis.
Ø Compare
the
differences
among
charge,
cost,
reimbursement,
par?cularly
‘reimbursement-­‐cost’
and
‘reimbursement-­‐to-­‐cost
ra?o’:
Ø Across
health
schemes
Ø Across
MDC
groups
Ø Across
Age
groups
Ø Inves?gate
possibili?es
for
cross-­‐subsidiza?on
Ø By
examining
the
rela?onship
between
(charge-­‐cost)OOP
and
(reimbursement-­‐cost)UC.
27

28. QuanAtaAve
Findings
#1:
Cost
Differences
across
Health
Schemes
“Total
Cost
Across
Health
Schemes”
0 10,000 20,000 30,000
mean of totalcost
CSMBS SSS UC Cash
Source:
Center
hospital
#1
Ø
The
average
costs
per
visit
vary
across
health
schemes,
where
CSMBS
pa?ents
have
the
highest
cost.
Ø
A^er
controlling
for
age,
gender,
disease,
LOS,
the
regression
analysis
confirms
that
the
pa?ent’s
health
scheme
has
a
significant
impact
on
the
unit
cost
of
health
services.
28

29. QuanAtaAve
Findings
#2:
“Profit”
or
“Loss”
across
Health
Schemes
“Total
Charge,
Total
Cost,
and
Reimbursement”
(by
Health
Scheme)
0 10,000 20,000 30,000 40,000 CSMBS SSS UC Cash
mean of totalcharge mean of totalcost
mean of reimbursement
Source:
Center
hospital
#1
Ø
CSMBS
pa?ents
are
the
only
group
whose
reimbursement
is
greater
than
cost,
while
reimbursement
is
lower
than
costs
for
UC
pa?ents.
Ø
Total
charge
is
set
to
be
greater
than
the
cost
for
all
health
schemes.
29

30. QuanAtaAve
Findings
#2:
“Profit”
or
“Loss”
across
Health
Schemes
“Charge-­‐Cost’
vs.
‘Reimbursement-­‐Cost”
-2,000 0 2,000 4,000 6,000 8,000 CSMBS SSS UC Cash
mean of charge_cost_diff mean of reimb_cost_diff
Source:
Center
hospital
#1
Ø
‘Reimbursement-­‐Cost’
is
the
highest
for
CSMBS,
but
is
nega?ve
for
other
groups.
Ø
‘Charge-­‐Cost’
are
posi?ve
for
all
groups,
but
is
very
small
for
OOP
pa?ents.
Ø OOP
pa?ents
may
not
be
the
‘profitable’
group
as
suspected.
30

31. QuanAtaAve
Findings
#2:
“Profit”
or
“Loss”
across
Health
Schemes
“Difference
between
Reimbursement
and
Cost”
(by
Health
Scheme)
-10,000 -5,000 0 5,000
mean of reimb_cost_diff csmbs sss uc foreign cash Others
Source:
Center
hospital
#2
Ø
Assume
that
charge
equals
reimbursement
for
foreign,
OOP,
and
‘others’
groups.
Ø
Reimbursement
(or
charge)
is
much
lower
than
the
cost
for
UC
and
foreign
pa?ents.
Ø
Insufficient
reimbursement
Ø
Hospital’s
burden
to
take
care
of
pa?ents
without
health
rights
(e.g.
foreign
pts)
31

32. QuanAtaAve
Findings
#2:
“Profit”
or
“Loss”
across
Health
Schemes
“Difference
between
Reimbursement
and
Cost”
(by
DRG-­‐MDC)
0 10,000
-30,000 -20,000 -10,000
mean of reimb_cost_diff
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 28
Source:
Center
hospital
#1
Ø
The
hospital
receives
reimbursement
more
than
the
cost
for
only
5
MDC
groups.
Ø
Some
major
diagnos?c
categories
create
a
large
deficit
for
the
hospital.
32
MDC
5
=
Diseases
&
disorders
of
the
circulatory
system
MDC
22
=
Burns

33. QuanAtaAve
Findings
#2:
“Profit”
or
“Loss”
across
Health
Schemes
“Difference
between
Reimbursement
and
Cost”
(by
Health
Scheme
and
Age
group)
-5,000 0 5,000 10,000
<20 21-30 31-40 41-50 51-60 61-70 71+
mean of reimb_cost_diff_CS mean of reimb_cost_diff_SS
mean of reimb_cost_diff_UC mean of reimb_cost_diff_cash
Source:
Center
hospital
#1
Ø
‘Reimbursement-­‐Cost’
is
generally
posi?ve
for
CSMBS,
and
the
difference
is
large
for
elder
pa?ents.
Ø
This
difference
is
nega?ves
for
almost
all
age
groups
for
UC
pa?ents.
33

34. QuanAtaAve
Findings
#3:
Evidence
for
Cross-­‐SubsidizaAon?
RelaAonship
between
‘Charge-­‐Cost’
for
OOP
and
‘Reimbursement-­‐Cost’
for
UCS
-50000 0 50000 100000 150000 200000
(mean) charge_cost_diff_cash
-300000 -200000 -100000 0 100000 200000
(mean) reimb_cost_diff_UC
Source:
Center
hospital
#1
Ø
If
there
is
cost-­‐shi^ing
between
UC
and
OOP
pa?ents,
we
expect
to
see
a
nega?ve
rela?onship
between:
(reimbursement-­‐cost)UC
and
(charge-­‐cost)OOP.
Ø
No
34
clear
evidence
of
‘ac?ve’
cross-­‐subsidiza?on.

35. QuanAtaAve
Findings
#4:
LimitaAons
of
Available
Data
Reimbursement-­‐to-­‐Cost
RaAo
0 50 100 150 200
mean of reimb_cost_ratio
csmbs sss uc foreign cash Others
Source:
Center
hospital
#2
Ø
The
reimbursement-­‐
to-­‐cost
ra?o
is
extremely
high
for
CSMBS,
possibly
because
of
the
outliers.
Ø
26
observa?ons
have
reimbursement-­‐to-­‐cost
ra?o
greater
than
2000!!
35

36. QuanAtaAve
Findings
#4:
LimitaAons
of
Available
Data
Reimbursement-­‐to-­‐Cost
RaAo
aeer
DeleAng
Outliers
0 5 10 15 20
mean of reimb_cost_ratio
csmbs sss uc foreign cash Others
Source:
Center
hospital
#2
Ø
A^er
dele?ng
the
outliers,
the
reimbursement-­‐to-­‐cost
ra?os
are
s?ll
rela?vely
high
for
CSMBS
and
SSS.
Ø
This
could
be
due
to
missing
informa?on
in
terms
of
recording
the
cost
data.
36

37. • No
Summary
of
Findings
direct
evidence
suggests
that
hospitals
cost-­‐shi^
by
increasing
prices
charged
to
out-­‐of-­‐pocket
payment
pa?ents
to
compensate
for
the
loss.
• Yet,
three
parerns
of
decision-­‐making
of
hospital
administrators
related
to
cross-­‐subsidiza?on
were
found.
• Therefore,
financing
policies
of
health
schemes
also
impact
other
pa?ents
groups
within
the
hospitals.

38. Mental
Models
of
Hospital
Administrators
38

39. ImplicaAons
for
Policy
and
PracAce
Ø To
policymakers:
• Demonstrates
an
empirical
evidence
of
that
current
healthcare
financing
of
hospitals
s?ll
inappropriate/inadequate.
• Suggests
that
payments
from
par?cular
payers
could
be
used
as
a
“buffer”
for
hospitals,
poten?ally
leading
to
“passive
cross-­‐subsidiza?on”
and
inequity
issues
of
healthcare
access.
• Suggests
how
to
“harmonize”
health
funds
in
a
more
efficient
and
equitable
fashion.
39

40. InformaAon
Systems
for
DeterminaAon:
The
Case
of
Policies
for
Healthcare
Financing
Pix source: online.wsj.com

41. Lessons
Learned
① HPSR
is
an
emerging
mul?disciplinary
field
of
study
that
aims
to
help
decision-­‐making
of
health
policymakers
and
healthcare
administrators.
– HPSR
is
a
study
“for”
health
system
development.
– HPSR
is
not
a
study
“on”
health
systems
or
specific
health
interven?onal
programs.
– HPSR
usually
requires
different
kinds
of
data
than
typical
clinical/epidemiological/cost-­‐effec?veness
studies.

42. Lessons
Learned
② HPSR
methodology
depends
on
research
ques?ons.
– Some
HPSR
use
primary
data
collec?on.
– Some
HPSR
use
secondary
data
collec?on.
– Some
HPSR
do
require
a
u?liza?on
of
administra?ve
data
of
healthcare
organiza?ons.
(e.g.
study
for
strengthening
healthcare
financing
policy).

43. ③ Data
needed
for
future
research
on
healthcare
financing:
Ø Micro-­‐data
(e.g.
data
at
DRG
level)
are
not
suitable
in
determining
cross-­‐subsidiza?on
across
health
schemes.
• Varia?on
across
pa?ents
within
the
same
DRG.
• Hospitals
unlikely
make
financial
decisions
at
the
micro-­‐level.
• Aggregate
data
at
the
hospital
level
are
more
suitable
to
study
cross-­‐
subsidiza?on.
Ø Results
are
highly
sensi?ve
to
the
data
accuracy.
Ø Data
from
different
sources
(e.g.
reimbursement
and
cost)
may
be
inconsistent,
and
could
result
in
misleading
results.
Ø Cross-­‐sec?onal
data
used
in
this
study
limits
the
ability
to
inves?gate
the
dynamic
of
changes
in
reimbursement
and
cost
over
?me.
43
Lessons
Learned
#3

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46. Q
&
A
borwornsom.lee@mahidol.ac.th
Pix source: online.wsj.com