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Perspective of Sample Data Analysis: Optimalisasi Data BPJS Kesehatan

Learn how to optimally analyze BPJS Kesehatan sample data to reveal population insights and drive evidence-based policy decisions. Explore various dimensions and variables for in-depth analysis and potential applications in health policy evaluation.

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Perspective of Sample Data Analysis: Optimalisasi Data BPJS Kesehatan

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  1. Perspective of Sample Data Analysis: Optimalisasi Data BPJS Kesehatan Budi Hidayat CHEPS FKM UI 28 August 2019

  2. The Beauty of Sampling If you don’t believe in sampling, the next time you have blood test, tell the doctor to take it all.

  3. The Beauty of Sampling (2) • Sampling methods (and procedures) should be designed appropriately, and hence capture true population parameter. • BPJS should check the sample data routinely • Employ analysis for key important indicators (defined), and contrasted the results between sample data and population data. And let the public know.

  4. Agenda Understanding data sample Use of claim data: knowing the world! Variables in the data A case study: Evaluating policy initiatives Detecting providers behaviors

  5. BPJS Sample Dataset -- publicly available Periods: 2015-16 n= 1.697.452 n= 906.905 cases n= 104.456 services n= 1.733.759 visits n= 700.885 Dx

  6. The BPJS claim data has a rich dimension for analysis Time period: admission date Easily to aggregate by month, quarter or year. • Cross section: • Visit • Cohort by participant • Participant • Provider • Time series and Panel: • Monthly to Annual Key variable: Unique ID member Unique ID visits Unique ID provider Date INA-CBGs code Referral information • Unit observation: • Patient level • Providers level

  7. Potential Analysis: health related dimensions Cross-sectional, time-series, and panels are possible to be employed continuous temporal diagnostic Claims ≈ behavioral high-stakes spatial financial standardized operational

  8. Potential Analysis: Health dimensions (2) • Some--but not limited--examples: • Estimates prevalence and its determinants • Patient journey by specific diseases (NCDs: Diabet, Jantung, Kanker, dll) • Providers behaviors in treating patients • Treatment protocols for benchmarking • Resource use: prob, costs, etc • Evaluating policy initiatives • Etc the observed world: health data only Integrated Data Warehouse Claims Health Data

  9. Public Data Social Service Data Potential Analysis: All (interested) dimensions Housing School Justice Child Services Property Census Health Plan Other Data the observed world: capturing all dimensions EMR Integrated Data Warehouse ? Claims Health Data

  10. Availability of the data supports a better decision.. • The researchers and academics provides a technically rigor evidence. • Policy makers needs a policy relevant and clear message shortly. • The policy dialogue between the researcher and the policy maker will drive a high-quality policy. Evidence Providers (Researchers, Evaluators, Statisticians, etc) Policy Makers Dialogue is Key

  11. Key Variablesin claimsdata

  12. Visit: FKTP (kapitasi & nKapitasi; DRGs Membership Financial

  13. Basic question in the data construction What is the unit analysis? • patient? • health provider, i.e. hospital? How does the time-period measurement in the data? • monthly? What is the data structure? • cross-section • time series • panel data How to impose the regulation in the dataset? • Specific time period • Classification: • Particular disease based on the diagnosis code • Area

  14. Case Study Evaluating policy initiatives Detecting providers behaviors

  15. Feature: Applied econometrics modeling to detect causal effect of P4P on its outcome • Quasi-experiment • Applying PSM-DID • Employ: Fixed-efefcts panel model • Combined several BPJS datasets [FKTP (Kapitasi) and Kepesertaan] with Facility Based Data (Puskesmas) from MoH The effectiveness of implementing P4P

  16. Matching: balancing control & treatment C & T balance at t0 C & T imbalance at t0 • Ada 9.345 Puskesmasmitra BPJS & melayanipasien JKN sejakJanuari 2014 hingga Des 2016. • 995 PKM KBK: 127 (13%) sejakAgustus 2015 dan 868 (87%) mulaiJanuari 2016. • Hasil PSM menemukan 997 Puskesmasmemilikikarakteristikhomogenpadakondisibaseline: • Puskesmas: intervensi 653 dankontrol 344.

  17. Balancing Treatment & Control – evidence

  18. Balancing Treatment & Control – evidence (cont)

  19. Profiling kinerjaPuskesmas: 2014-2016 • How do we generate P4P indicators? • KONTAK: • VISIT: • RPPB: • RRALL: • RRNS:

  20. Trend KONTAK: treatment vs control

  21. Trend PROLANIS: treatment vs control

  22. Trend RUJUK all: treatment vs control

  23. Trend RUJUK-sp: treatment vs control

  24. True impacts of P4P – FE panel model Where: Yhdt = the outcome indicator provided by health center h of districts d in time period t, ttrend= a service-specific time trend capturing general trends over time, Postt= to captures general average monthly change compared to the pre-intervention period, P4Phd captures the additional improvements in indicators observed in treated areas, are facility fixed effects Month = calendar month indicators to control for seasonal fluctuations in Y outcome.

  25. Apadampak KBK thd Spending FKRTL: Overall reduksi spending 1.35% Parameter: analisishistorisklaimmenemukandarisatupeserta dirujuk, berpeluangmemanfaatkan FKRTL 1.44 kali rawatinapdan4.49 kalirawatjalan [Gimanaanalisisnya??]

  26. Other Related Case Study 1. Combining several data sources (BPJS Data, Riskesdas And CHEPS survey)

  27. T2DM Complication Trends (n=1658) Based on Patient Self-reporting questionnaire Diabetes lead to a number of complications, including: MACROVASCULAR22% 66% MICROVASCULAR 35% 4 % Stroke • Retinopathy 11% 11% kardiovaskular • Hipoglikemia 7.2% Komplikasi pertama rata-rata terjadi 4 tahun Nephrophaty 56% 11% Neuropathy Blood vessels Source: CHEPS 2019

  28. Patient Self-reporting questionnaire and medical cost database SNEAK PEAK! BURDEN OF T2DM Diabetes can lead to a number of further complications Average medical cost per year per person Average medical cost per year per person IDR 5.7 million (without complication) IDR 5.4 million (without complication) IDR 14 million (with complication) IDR 11 million (with complication) Total Medical cost of T2DM overall 199 Trillion, Total Medical cost of T2DM diagnosed by HCWs overall 59 Trillion Total Medical cost of T2DM under JKN 8 Trillion Source: CHEPS 2019; Additional data are generated from BPJS Claim dataset & Riskesdas

  29. The drivers of complications? Complications significantly increased with age, younger DM observed, family member with DM, the existence of co-morbidity (e.g., hypertension and high-cholesterol), lowest edu, poorest. Source: CHEPS 2019

  30. Region based analysis.. Cardiac catheterization cases based on province Cardiac catheterization cases based on tariff region and severity level Severity Level 1 • Which area would spend the health spending on specific case? • The level of severity can be imposed according to the tariff region. Severity Level 2 Severity Level 3 Source:Analisis data olehKedeputianbidangRisetdanPengembangan BPJS Kesehatan

  31. Time series analysis.. • Is there any seasonal pattern? • Does the pattern of the series reflect the health provider behavior? • Were there any regulation drives the health provider behavior? Cardiac catheterization cases by month Source:Analisis data olehKedeputianbidangRisetdanPengembangan BPJS Kesehatan

  32. Provider behaviors in response to INA-CBGs: Why hospital claims data?

  33. DRGs Incentive for Hospitals BHidayat August 2016 Exist in Indonesia? Readmissions Bloody Discharge Upcoding Dumping • DRGs incentives for Hospitals: • Reduce cost (1a & 1b) • Increase income (2a & 2b) • Add patient (not seen)

  34. BHidayat August 2016 Readmission in Indonesia Repeated Visits for a similar diagnosis treated by the same hospital(Claim Data Jan 2014 s/d July 2015)

  35. BHidayat August 2016 Readmission Mapping:Outpatient and Inpatient

  36. BHidayat August 2016 Readmission due to SID:Outpatient Not all readmissions are problematics,except those having an SID (Supplier Induced Demand) indication

  37. BHidayat August 2016 Readmisiion due to Bloody Discharge: RANAP In case of inpatient, Readmission can be due to Bloody Discharge

  38. BHidayat August 2016 Upcoding Detection Identification of suspected Upcoding (miss-match between hospitals capacity with the incidence of severity). Triangulation “SUSPECTED” must be done by “MEDICAL AUDIT”

  39. BHidayat August 2016 Suspected Upcoding:Cases & cost savings

  40. Trend of Upcoding BHidayat August 2016

  41. BHidayat August 2016 Referral and Dumping Dangers for patients, and Overall System

  42. Dumping makes patient inconvenient Cost shifting due to Dumping was Rp 14,56 T. It is not efficeny, but cost shifting amongst hospitals • DANGER! Trend of dumping is increasing

  43. BHidayat August 2016 CBGS and Practice of Medical Services: Case of Obgyn Is this rational? From IDR 5.51T total claim obgyn, 3.8 T (70%) was used to pay sectio cases klaimSesar

  44. BHidayat August 2016 …and trend of sectio is increasing ProporsiSesarcenderungnaikdaribulankebulan

  45. The Beauty of Sampling • If you don’t believe in sampling, the next time you have blood test, tell the doctor to take it all. • However, sampling methods should be designed appropriately to capture the true population parameters. • Hence, BPJS should double check the sample data routinely; e.g. employ analysis for several key-indicators, and contrasted the results between sample data and population data. And let the public know.

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