Informatics for Health Policy and Systems Research: Lessons Learned from a Study of Healthcare...

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

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)

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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

PresentaAon Outline

3

“What exactly is HPSR?”

Pix source: online.wsj.com

New Health Research Mapping?

Source: Hoffman et al. (2012).

New Health Research Mapping?

Source: Hoffman et al. (2012).

Different kinds of knowledge needed

“The Systems”

Source: WHO )2012); de Savigny & Adam (2009); Scheerens and Bosker (1997); Pix source: humanrevod.wordpress.com

•  The WHO Six Building Blocks” of health (services) systems

Different Levels of Health Systems

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

Health Systems & Health Policy

•  Terrain of Health Policy and Systems Research


What Is & What Is Not HPSR?

Research “on” health systems VS.

Research “for” Health systems


Research Strategies in HPSR


Example of HPSR: Study of Healthcare Cross-‐subsidizaAon in Thai Public Hospitals


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.

CGD (CSMBS),

NHSO (UCS)

Taxes Payers

Employer-based private health

insurance

Individual & Employer’s

private health insurance

(Voluntary)

Hospitals

Medical Specialists

Generalists & PCPs

Patients paying out-of-pocket

Ambulatory Facilities

Payment Mechanisms: Salary, Fee-for-Service,

Global Budget, Capitation, DRGs, etc.

Financing of Thai Healthcare Systems

Providers in Public & Private Sector

Commercial Insurance

Companies

Social Security

Office (SSS)

Motor vehicle’s owners (Mandatory by the Motor

Vehicle Victim Protection Law)

Study RaAonale

Figure source: Benjaporn (2007) 14

of the former period grew about 68 percent, 26 percent and 41 percent, respectively,

while during the latter period they were about 224 percent, 43 percent and 127 percent.

Furthermore, when consider growth of expenditure compare year on year; growth rate

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 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.

Graph 2.4: CSMBS expenditure during the fiscal years 1996-2007

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

4,826 5,625 5,866 6,206 7,007 8,123

9,509

11,350 13,905

16,943

21,896

30,833

8,761 9,877 10,574 9,048 10,050 11,058 10,967

11,335 12,138 12,437 15,109

15,649

13,587 15,502

16,440 15,253

17,058 19,181 20,476 22,686

26,043

29,380

37,004

46,481

0 5,000

10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000 50,000

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007

Mill

ion

Bah

t

Year

CSMBS Expenditure in the fiscal years 1996-2007

Out-patient In-patient Total

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

Study RaAonale

Figure source: www.be2hand.com; www.imdb.com

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?

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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.

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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.

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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.

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Methodology: “Mixed Methods”

Source: Creswell (2009). Research design: Qualitative, quantitative, and mixed methods approaches. 3rrd ed. 21

Ø Mixed-‐methods research with concurrent embedded design, which quan?ta?ve data analysis is used to compliment as the qualita?ve data analysis.

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.

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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

Research Findings


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

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Ø 13 sub-‐themes, categorized into 4 emerging themes.

QualitaAve Findings

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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

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.

QuanAtaAve Analysis

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QuanAtaAve Findings #1: Cost Differences across Health Schemes

“Total Cost Across Health Schemes”

010

,000

20,0

0030

,000

mea

n of

tota

lcos

t

CSMBS SSS UC Cash

Source: Center hospital #1 28

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.

QuanAtaAve Findings #2: “Profit” or “Loss” across Health Schemes

“Total Charge, Total Cost, and Reimbursement” (by Health Scheme)

010

,000

20,0

0030

,000

40,0

00

CSMBS SSS UC Cash

mean of totalcharge mean of totalcostmean of reimbursement


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.


“Charge-‐Cost’ vs. ‘Reimbursement-‐Cost”

-2,0

000

2,00

04,

000

6,00

08,

000

CSMBS SSS UC Cash

mean of charge_cost_diff mean of reimb_cost_diff


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.

QuanAtaAve Findings #2: “Profit” or “Loss” across Health Schemes “Difference between Reimbursement and Cost”

(by Health Scheme)

-10,

000

-5,0

000

5,0

00

me

an o

f rei

mb_

cost

_diff

csmbs sss uc foreign cash Others


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)

QuanAtaAve Findings #2: “Profit” or “Loss” across Health Schemes “Difference between Reimbursement and Cost”

(by DRG-‐MDC) -30

,000

-20,00

0-10

,000

010,

000

mean

of reim

b_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

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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.

MDC 5 = Diseases & disorders of the circulatory system

MDC 22 = Burns


“Difference between Reimbursement and Cost” (by Health Scheme and Age group)

-5,0

000

5,00

010

,000

<20 21-30 31-40 41-50 51-60 61-70 71+

mean of reimb_cost_diff_CS mean of reimb_cost_diff_SSmean of reimb_cost_diff_UC mean of reimb_cost_diff_cash


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.

QuanAtaAve Findings #3: Evidence for Cross-‐SubsidizaAon?

RelaAonship between ‘Charge-‐Cost’ for OOP and ‘Reimbursement-‐Cost’ for UCS

-500

000

5000

010

0000

1500

0020

0000

(mea

n) c

harg

e_co

st_d

iff_c

ash

-300000 -200000 -100000 0 100000 200000(mean) reimb_cost_diff_UC


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 clear evidence of ‘ac?ve’ cross-‐subsidiza?on.

QuanAtaAve Findings #4: LimitaAons of Available Data

Reimbursement-‐to-‐Cost RaAo

050

100

150

200

mea

n of

reim

b_co

st_r

atio



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!!

QuanAtaAve Findings #4: LimitaAons of Available Data

Reimbursement-‐to-‐Cost RaAo aeer DeleAng Outliers

05

1015

20

mea

n of

reim

b_co

st_r

atio



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.

•  No 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.

Summary of Findings

Mental Models of Hospital Administrators

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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.

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InformaAon Systems for DeterminaAon: The Case of Policies for Healthcare Financing


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.

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).

Lessons Learned

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.

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Lessons Learned #3

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Informatics for Health Policy and Systems Research: Lessons Learned from a Study of Healthcare...

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Transcript of Informatics for Health Policy and Systems Research: Lessons Learned from a Study of Healthcare...