"Future development of INA CBGs"

Transcription

1 "Future development of INA CBGs" 15 March 2017 Supasit Pannarunothai, MD, PhD Chairoj Zungson5porn, MD Orathai Khiaocharoen, PhD

2 Objec;ves To understand key success factors in health care provider payment systems To understand circumstances in future development of DRG/INA CBGs 2

3 Background Indonesia has been conduc5ng the new na5onal social health insurance scheme since 2014 Now Indonesia is implemen5ng payment capita5on for primary health care providers DRG's payment for hospitals The problem on DRG's payment is Indonesia is s5ll using United Na5on University Grouper (UNU Grouper) but Indonesia have a 5 years plan to own the grouper by developing a new Indonesian Grouper Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

4 IMPLEMENTATION OF DRG S PAYMENT Jan 2009, 945 Hospitals + Clinics Sept 2008, 15 Jamkesmas Hospitals, Program Jamkesmas program Jan 2013, 1273 Hospitals + Clinics, Jamkesmas Program Jan 2014, 1708 Hospitals + Clinics JKN Program December Hospitals +Clinics JKN Program Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

5 Casemix System in Indonesia Jamkesmas program JKN program Oct Jan Jan 2014 Se]ng INA DRG Implementa5on INA DRG Changing grouper from IR- DRG to UNU grouper Updating INA- CBG tariff Implementation of updated tariff Updating INA-CBG tariff for JKN preparation Updating INA- CBG tariff New INA- CBG tariff Commercial grouper UNU grouper 5

6 Current Situa;on INA CBG S PAYMENT IN JKN Ø implemented in >1,815 hospitals and clinics Ø using ICD 10 for diagnosis and ICD 9 CM for procedures. Ø classified by UNU grouper (1,077 INA-CBG s codes, 288 outpa5ent care and 789 inpa5ent care) Ø addi5onal claim à special CMG and FFS Ø the claim must be verified by BPJS Ø the tariff must be updated at least every 2 years 6 Center for Health Financing and Health Insurance, Ministry of Health, Indonesia 2016

7 INA-CBG S CODE UNU-IIGH Casemix Grouper Version 2.0 : 5 Digit System A III CMG Case Type CBG s Type Resource Intensity Level Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

8 NO. Case-Mix Main Groups (CMG) CMG Codes 1 Central nervous system Groups G 2 Eye and Adnexa Groups H 3 Ear, nose, mouth & throat Groups U 4 Respiratory system Groups J 5 Cardiovascular system Groups I 6 Diges5ve system Groups K 7 Hepatobiliary & pancrea5c system Groups B 8 Musculoskeletal system & connec5ve 5ssue Groups M 9 Skin, subcutaneous 5ssue & breast Groups L 10 Endocrine system, nutri5on & metabolism Groups E 11 Nephro-urinary System Groups N 12 Male reproduc5ve System Groups V 13 Female reproduc5ve system Groups W 14 Deleiveries Groups O 15 Newborns & Neonates Groups P 16 Haemopoei5c & immune system Groups D Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

9 NO. Case-Mix Main Groups (CMG) CMG Codes 17 Myeloprolifera5ve system & neoplasms Groups C 18 Infec5ous & parasi5c diseases Groups A 19 Mental Health and Behavioral Groups F 20 Substance abuse & dependence Groups T 21 Injuries, poisonings & toxic effects of drugs Groups S 22 Factors influencing health status & other contacts with health services Groups Z 23 Ambulatory Groups-Episodic Q 24 Ambulatory Groups-Package QP 25 Sub-Acute Groups SA 26 Special Procedures YY 27 Special Drugs DD 28 Special Inves;ga;ons I II 29 Special Inves;ga;ons II IJ 30 Special Prosthesis RR 31 Chronic Groups CD 32 Errors CMGs X Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

10 Severity Level 0 : outpa5ent care I light : inpa5ent care severity level 1 (without complica5on or comorbidity) II moderate: inpa5ent care severity level 2 (with mild complica5on and comorbidity) III severe: inpa5ent care severity level 3 (with major complica5on and comorbidity) Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

11 6 TYPES OF PRICE IN INA-CBG 1. Class D Hospital 2. Class C Hospital 3. Class B Hospital 4. Class A Hospital 5. Top referral General Hospital 6. Top referral Specialist Hospital Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

12 FOR EACH HOSPITAL THERE ARE 3 CLASSES OF PRICE 1. Ward class 1 2. Ward class 2 3. Ward class 3 Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

13 5 REGION OF INA-CBG 1. Region I 2. Region 2 3. Region 3 4. Region 4 5. Region 5 Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

14 HOSPITAL PAYMENT Addi5onal payment INA-CBG (1077 INA-CBG CODES) Special CMG (Drugs,Procedures, Inves;ga;on, Prothesis, Subacute, Chronic) Top Up Payment,, FFS (some medical supplies, chemoteraphy, haemophilia medicines, chronic cases medicine, CAPD., Petscan) Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

15 Provider payment challenges Less understanding on DRG s payment from provider side à less changes on provider behavior The demand of Na5ve Grouper (Logic Grouper) Limited capacity of INA CBG s team Fraudulent on implementa5on of INA CBG s Standardiza5on of health services and cost containment Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

16 Development plan of INA CBG Upda;ng tariff 2. Refinement of algorithm 1. Developing monitoring system 2. Developing Ina Grouper 3. Capacity building 1. Developing Ina Grouper 2. Upda;ng tariff 3. Capacity building 1. Finalizing Ina Grouper 2. Seeng manual 3. Pilo;ng Ina Grouper Implementa;on Ina Grouper Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

17 Why Thailand DRGs Center for Health Financing and Health Insurance, Ministry of Health, Indonesia contacted Thai team for technical support on DRG's payment (27/11/2015) DRG's payment workshops were arranged in Indonesia and field visit benchmarking in Thailand that were funded by Australian government (DFAT) (01/02/ /03/2016) Center for Health Financing and Health Insurance, Ministry of Health, Indonesia

18 DRG technical results in 3 workshops Improve technical skill of INA-CBG team Methodology and Data Prepara;on ( on exercise of MDC 02 [H] Classifica;on) Determining scope of MDC 02 Eye Group (Clinically coherence) by Ophthalmologist panel Collec5ng demographic, clinical, cost and charge data Developing classifica5on Sojware Development Sta;s;cal Analysis on Classifica;on 31 March 2016: Jakarta 18

19 MDC 02 Diagram 19

20 Data Ajer Grouping by new mini Grouper Informa;on Hospital 1 Hospital 2 Hospital 3 Hospital 4 Groupable Cases Ungroupable Cases Total Total % of Total ,39 % ,61 % % 20

21 Grouping Result INA DC INA DC Descrip;on Hospital 1 Hospital 2 Hospital 3 Hospital 4 Total % of Total 0202 Enuclea5on and orbital procedures Major Corneal, Sclera dan Conjunc5va Proc Re5nal procedures ,126 2, Dacriocystorhinostomy Glaucoma & Cataract Complex Proc LOS <= Glaucoma & Cataract Complex Proc > , Lens procedures , , Strabismus Procedure Eyelid Procedures Other Corneal, Sclera and Conjuc5va Proc Lacrimal procedures Other eye procedures Acute and major eye infec5ons Neurological and vasc disor of the eye Hyphema and medically managed trauma Malignant neoplasm of the eye Other disorders of the eye Total

22 Sta5s5cal Analysis on Classifica5on Used for : Tes5ng performance of classifica5on à Reduc5on In Variance (R²) higher R² indicates good or confirmed classifica5on Predict homogeneity of the group classifica5on à Coefficient of Variance (CV) Low CV indicates narrow varia5on within each group. 22

23 Reduc;on In variance (RIV) Y i is the value of the variables (claims or charges) for the ith patient, A is average value of the variable in the database, A g is the average value of the variable in that group Formula Hospital Charge Groups Ya=(Yi-A)^ Ya=(Yi-Ag)^ RIV

24 Coefficient of Variance (CV) INA DC N Mean SD CV ,711,926 6,335, ,586,054 4,432, ,697,887 8,582, ,135,281 3,511, ,793,123 5,501, ,405 12,085,767 6,799, ,642 9,041,674 3,641, ,692,990 3,138, ,443,942 7,825, ,833,642 4,958, ,870,277 3,409, ,193,498 3,386, ,552,010 12,739, ,430,812 7,583, ,079,736 6,154, ,244,042 14,462, ,696,287 5,884, Total 7,463 12,583,753 8,236,

25 Rela;ve Weight (RW) INA DC INA DC Descrip;on N Hospital Charge RW 0202 Enuclea5on & Orbital Procedures ,711, Major Corneal, Scleral & Conjuc5val Procedures 24 12,586, Re5nal Procedures 2,062 19,698, Dacryocystorhinostomy Procedures 48 13,135, Glaucoma & Complex Cataract Procedures 22 12,793, Glaucoma & Complex Cataract Procedures LOS>1 1,405 12,085, Lens Procedures 1,645 9,044, Strabismus Procedures 85 10,692, Eyelid Procedures ,443, Other Corneal, Scleral & Conjunc5val Procedures 571 8,829, Lacrimal Procedures 4 9,870, Other Eye Procedures 223 7,193, Acute & Major Infec5on 95 7,552, Neurological & Vascular Disorder of Eye 62 8,207, Hyphema & Medically Managed Trauma 142 7,056, Malignancy of Eye 81 12,244, Other Disorder of Eye 564 6,696,

26 LESSON LEARNT on exercise of MDC 02 [H] Classifica;on Data Quality Poor data quality : Ø Coded data : Unacceptable primary diagnosis Ø Cost data : some hospital only gave hospitals charge data, billing data not complete (ex: inpa5ent but no charges for accommoda5on) The needs for data : collected data lower than expected Previous cos5ng data collec5on provided by 157 hospitals but for this study only 4 hospitals (5me considera5on) 26

27 Knowledge for sustainable development Capacity building to increase knowledge Share knowledge to the hospitals and other stakeholders for having same percep5on Building trust between stakeholders 27

28 Full version update The needs for data : more data, bexer quality for coded data, more hospital par5cipant to provide data coding and cos5ng Reclassifica5on starts from each CMG/MDC Ac5ve par5cipa5on from stakeholders especially from Indonesian Doctor Associa5ons and Hospital Associa5on Tariff Seeng Bexer tariff with bexer classifica5on Reflect actual cost Transparency 28

29 Reflec;ons on Ina-CBG Good data pooling from all hospitals at BPJS Good hospital cost data from 157 hospitals Good data on outpa5ent and inpa5ent care Different base rates (tariffs) Cri5cal success factors include: Human resource factors to expand to all MDCs Data audit: prepayment and post-payment Adequate government funding 29

30 Work forward: INA-CBGs vs Thai DRGs MOU: MoH Indonesia and Centre for Health Equity Monitoring Founda5on ( CHEM ) Objec5ve: work closely for the sustainability of implementa5on of Indonesia Case-Based Group (hereina{er referred to as INA-CBG ) in Indonesia, also to intensify competencies of INA-CBG Team to develop and maintain the performance of INA-CBG. 30

31 What do we do? Now 2 workshops (November 2016, February 2017): First workshop: 16 hospitals sent data to MOH Re-classifica5ons: MDC 02, MDC 04, MDC 03, MDC 19 Second workshop: 39 hospitals sent data to MOH Re-classifica5ons: same MDC Study for new MDC development: MDC 06, 07, 13, 14, 15 31

32 Data quality (39 hospitals) DESCRIPTION Number of cases Percentage of cases IP OP ALL IP OP ALL Good data 249,529 1,800,184 2,049, Error data 270,527 1,748,214 2,018, All Data 520,056 3,548,398 4,068, Detail of error Length of Stay days is invalid for inpatient Length of Stay days is invalid for outpatient ,802 31, No Primary Diagnosis found 163, , , Primary Diagnosis is a dagger, but no asterisk pair found in secondary diagnosis Diagnosis code is not accepted as Primary Diagnosis (ref. C51i10vx) 2,555 2,034 4, ,478 73,033 91, No matching DC is found 4,889 3,260 8, Logic retrieval failure or logic sequence not found 80, , ,

33 Data for reclassifica5on in 4 MDCs GROUP DESCRIPTION Number of cases Percentage of cases IP OP ALL IP OP ALL MDC 12 Eye 4,011 13,226 17, MDC 13 ENT 3,001 7,926 10, MDC 14 Respiratory 6,182 5,836 12, MDC 29 Mental 1, , OTHER Other MDC 234,894 1,772,308 2,007, All 249,529 1,800,184 2,049, MDCs MDC12,13,14,29 14,635 27,876 42,

34 Sta5s5cal Analysis: new classifica5on vs INA CBG IP OP MDC Grouper Number of group Number of case RIV Number of group Number of case RIV 12 New 46 4, , INA CBG 31 4, , New 82 3, , INA CBG 96 3, , New 63 6, , INA CBG 91 6, , New 17 1, INA CBG 22 1,

35 What s next: Plan of Ac;on for 2017 (1) NO Descrip;on 1 Reclassifica5on for CMG Diseases and Disorders of the Diges5ve System 2 Reclassifica5on for CMG Diseases and Disorders of the Hepatobiliary System and Pancreas 3 Reclassifica5on for CMG Infec5ous and Parasi5c Diseases 4 Reclassifica5on for CMG Endocrine, Nutri5onal, and Metabolic Diseases and Disorders Reclassifica5on for CMG Diseases and Disorders of the Skin, Subcutaneous Tissue and Breast Reclassifica5on for CMG Myeloprolifera5ve Diseases and Disorders, Poorly Differen5ated Neoplasms Reclassifica5on for CMG Diseases and Disorders of the Blood and Blood Forming Organs and Immunological Disorders 8 Reclassifica5on for CMG Diseases and Disorders of the Nervous System 9 Reclassifica5on for CMG Diseases and Disorders of the Blood and Blood Forming Organs and Immunological Disorders 35

36 What s next: Plan of Ac;on for 2017 (2) NO Descrip;on 10 Reclassifica5on for CMG Diseases and Disorders of the Nervous System 11 Reclassifica5on for CMG Diseases and Disorders of the Circulatory System 12 Reclassifica5on for CMG Diseases and Disorders of the Musculoskeletal System and Connec5ve Tissue 13 Reclassifica5on for CMG Diseases and Disorders of the Kidney and Urinary Tract 14 Reclassifica5on for CMG Diseases and Disorders of the Male Reproduc5ve System 15 Reclassifica5on for CMG Pregnancy, Childbirth and Puerperium 16 Reclassifica5on for CMG Diseases and Disorders of the Female Reproduc5ve System 17 Reclassifica5on for CMG Newborns, and other Neonates with condi5ons Origina5ng in the Perinatal Period 18 Reclassifica5on for CMG Burns & Mul5ple Significant Trauma 19 Reclassifica5on for CMG Factors Influencing Health Status and Other Contact with Health 36 Services & Poisoning and Toxic Effect Of Drugs

37 Goal for INA CBGs New Indonesian DRG tool and grouper for alloca5on of inpa5ent and outpa5ent budget Pa5ent-level cost data for Indonesian DRG rela5ve weight and tariff The new tools for monitoring, evalua5on, and audit mechanisms 37

38 Lesson learnt from Thai DRG development Short cut start by the classifica5on and calibra5on using data from x hospitals Focus on hospitals that have already keyed in clinical data of all pa5ents with billing data Cri5cal success factors include: Human resource factors Data pooling power Involvement of key players at the beginning 38

39 Conclusions Law and vision on use of DRG/INA CBG is a driving force for development Informa5on infrastructure enhances the breadth and depth of development Interna5onal exchange of knowledge s5mulates the pace of change of development. Design appropriate payment method for service type and provider characteris5cs Adequate public sources of finance for UHC 39

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