informatics for health policy and systems research: lessons learned from a study of healthcare...
TRANSCRIPT
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)
2
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
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.
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.
Research Strategies in HPSR
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.
Research Strategies in HPSR
Source: Gilson, editor (2012). Health Policy and Systems Research: A Methodology Reader.
Example of HPSR: Study of Healthcare Cross-‐subsidizaAon in Thai Public Hospitals
Pix source: online.wsj.com
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?
17
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
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
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
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.
22
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
Pix source: online.wsj.com
QualitaAve Analysis Ø Construc?vist grounded theory (Chamaz, 2005; 2006) Ø Coding process (Strauss & Corbin 1990)
25
Ø 13 sub-‐themes, categorized into 4 emerging themes.
QualitaAve Findings
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
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
27
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
Source: Center hospital #1 29
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.
QuanAtaAve Findings #2: “Profit” or “Loss” across 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
Source: Center hospital #1 30
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
Source: Center hospital #2 31
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
32
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
QuanAtaAve Findings #2: “Profit” or “Loss” across Health Schemes
“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
Source: Center hospital #1 33
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
Source: Center hospital #1 34
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
csmbs sss uc foreign cash Others
Source: Center hospital #2 35
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
csmbs sss uc foreign cash Others
Source: Center hospital #2 36
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
38
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
InformaAon Systems for DeterminaAon: The Case of Policies for Healthcare Financing
Pix source: online.wsj.com
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.
43
Lessons Learned #3
Bibliography 1. สุพล ลิมวัฒนานท์ และคณะ. (2555) รายงานผลการพัฒนาระบบการจัดเก็บข้อมูลด้านยาเพื่อติดตามประเมินการสั่งใช้ยาและส่งผลวิเคราะห์ข้อมูลป้อนกลับโรงพยาบาล: ค่าใช้จ่ายยาผู้ป่วยนอกระบบจ่ายตรงสวัสดิการรักษาพยาบาลข้าราชการ (ข้อมูลเดือนกรกฎาคม 2554 – มีนาคม 2555). กรุงเทพมหานคร; สำนักพิมพ์อักษรกราฟฟิคแอนด์ดีไซน.์
2. สำนักงานหลักประกันสุขภาพแห่งชาติ (2554). การจัดกลุ่มวินิจฉัยโรคร่วมและน้ำหนักสัมพัทธ์ ฉบับที่ 5.0 เล่มที่ 1 และเล่มที่ 2. นนทบุร;ี สำนักงานหลักประกันสุขภาพแห่งชาติ.
3. Scientific Sofware Development. ATLAS/ti. version 7.0. [Computer sofware] (2013). Berlin, ATLAS.ti Scientific Sofware Development GmbH.
4. Braeutigam, R. R. (1989). Optimal policies for natural monopolies. In Handbook of Industrial Organization, edited by R. Schmalensee and R. D. Willig. Amsterdam: North-Holland, pp.1289-346.
5. Charmaz, K. (2005). Grounded theory in the 21st century: application for advancing social justice studies. In N.K. Densin & Y.S. Lincoln, The Sage handbook of qualitative research (3rd ed, pp.507-536). Thousand Oaks, CA: Sage.
6. Charmaz, K. (2006). Constructing grounded theory. London: Sage. 7. Craik, K. J. W. (1943). The nature of explanation. Cambridge University Press, Cambridge, UK. 8. Creswell J. W., Fetters M. D., Ivankova N.V. (2004). Designing a mixed methods study in primary care. Ann Fam Med, 2(1):
7-12. 9. Cutler, D. (1998). Cost shifting or cost cutting? The incidence of reductions in Medicare payments. Tax Policy and Economy,
12, 1-27. 10. Dobson, A., DaVanzo, J., & Sen, N. (2006). The cost-shift payment "hydraulic": Foundation, History, and Implications. Health
Affairs, 25(1), 22-33. 11. Dranove, D. (1988). Pricing by non-profit institutions: The case of hospital cost shifting. Journal of Health Economics, 7, 47-57. 12. Faulhaber, G. R. (1975). Cross-subsidization: Pricing in public enterprises. The American Economic Review, 65(5), 966-977. 13. Feldman, R., Wholey, D., & Christianson, J. B. (1998). Do Medicare HMOs cost shift? Inquiry, 35(3), 315-331. 14. Frakt, A. (2010). Estimating hospital cost shift rates: A practitioners' guide. Health Care Financing & Economics (HCFE)
Working Paper. 15. Frakt, A. (2011). How much do hospital cost shift? A review of evidence. The Milbank Quarterly, 89(1), 90-130. 16. Health Systems Research Institute, World Health Organization (2004). The report of monitoring and evaluation of universal
health coverage inThailand, first phase. Nonthaburi, Thailand: HSRI. 17. Jones, N. A., H. Ross, T. Lynam, P. Perez, and A. Leitch. (2011). Mental models: an interdisciplinary synthesis of theory and
methods. Ecology and Society, 16(1): 46. [online] Accessed from: http://www.ecologyandsociety.org/vol16/iss1/art46 44
Bibliography 18. Khiaocharoen, O, Pannarunothai, P., Chairoj Zungsontiporn, A. (2011). Patient-Level Cost for Thai Diagnosis Related Groups:
Micro-Costing Method Journal of Health Science, 20(4). 19. Morrisey, M. A. (1994). Cost Shifting in Health Care: Separating Evidence from Rhetoric. Washington, DC: The AEI Press. 20. Morrow, S. L. & Smith, M. L. (1995). Constructions of survival and coping by women who have survived childhood sexual
abuse. Journal of Counseling Psychology, 42, 24-33. 21. Sloan, F. A. (2000). Not-for-profit ownership and hospital behavior. In A. J. Culyer & J. P. Newhouse (Eds.), Handbook of
Health Economics (Vol. 1, pp. 1141-1174). Amsterdam: Elsevier Science. 22. Sloan, F. A., & Becker, E. R. (1984). Cross-subsidies and payment for hospital care. Journal of Health Politics, Policy and Law,
8(4), 660-685. 23. StataCorp. (2011). Stata Statistical Software: Release 12. [computer sofweware]. College Station, TX: StataCorp LP. 24. Strauss, A. & Corbin, J. (1990). Basic of qualitative research; Grounded theory procedure and techniques. Newbury Park, CA:
Sage. 25. Troyer, J. L. (2002). Cross-subsidization in nursing homes: Explaining rate differentials among payer types. Southern
Economic Journal, 68(4), 750-773. 26. Vikitset, T. (2008). An Alternative Retail Pricing Policy for Petroleum Products: A Case Study of Gasoline and High Speed
Diesel in Thailand. Accessed from: http://news.nida.ac.th/th/images/PDF/article2551/%E0%B8%AD.%E0%B8%98%E0%B8%B5%E0%B8%A3%E0%B8%B0%E0%B8%9E%E0%B8%87%E0%B8%A9%E0%B9%8C.pdf
27. Viscusi, W.K. , Vernon, J., and Harrington, E. C. (1992). Economics of Regulation and Antitrust. Lexington, MA: D.C. Health and Company.
28. World Health Organization (2006). Everybody’s business: strengthening health$systems to improve health outcomes: WHO’s framework for action. Geneva, Switzerland: WHO Document Production Services.
29. Wu, V. Y. (2010). Hospital cost shifting revisited: new evidence from the balanced budget act of 1997. International Journal of Health Care Finance and Economics, 10(1), 61-83.
30. Zuckerman, S. (1987). Commercial insurers and all-payer regulation: Evidence on hospitals' responses to financial need. Journal of Health Economics, 6(3), 165-187. doi: 10.1016/0167-6296(87)90007-5
31. Zwanziger, J., & Bamezei, A. (2006). Evidence of cost shifting in California hospitals. Health Affairs, 15(1), 197-203.
45
Q & A [email protected]
Pix source: online.wsj.com