Implementation of Casemix System as Prospective Provider Payment Method in Social Health Insurance: a Case Study of Acheh Provincial Health Insurance

By Prof Dr Syed Mohamed Aljunid and Dr Irwan Saputra

The Government of Aceh Province in Indonesia has established the Social Health Insurance (SHI) called Jaminan Kesehatan Aceh (JKA) in 2006 that provide health coverage to all 4.6 million population of the province. Fee-for-service was initially used as the provider payment method in the programme until 2013. In 2014, in line with the National Health Insurance of Indonesia (Jaminan Kesehatan Nasional JKN), INA-CBG (Indonesia Case-Based Group) casemix system was adopted by JKA to replace the Fee-for-Service method. This book presents outcome of the evaluation done using a combination of qualitative and quantitative methods on the implementation of JKA programme. The quantitative study was conducted to assess income of three selected hospitals (Type B, C and D) reimbursed using INA-CBG groups covering more than 17,000 cases. Quantitative data analysis revealed that overall, the hospitals received 32.4% higher income when reimbursed with casemix system (INA-CBG) as compared to fee-for-service. Type D hospital is the biggest gainer with 81.0% increase in income. In conclusion, the use of Casemix (INA-CBG) as a prospective payment method has benefitted the hospitals a lot. It is hope that additional resources gained through this programme will allow the hospitals to provide optimum care to the population.

• Language: English
• Publisher: Partridge Publishing Singapore
• Release date: Nov 20, 2022
• ISBN: 9781543771985

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Copyright © 2022 by Prof Dr Syed Mohamed Aljunid &

Dr Irwan Saputra.

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Contents

Acknowledgements

Chapter 1     Introduction

1.1   Intoduction

1.2   Study Justification

1.3   The Scope Of Study

1.4   Study Objectives

1.4.1   General objective

1.4.2   Specific objectives

1.4.3   Hypothesis

1.5   Conclusion

Chapter 2     Literature Review

2.1   Introduction

2.2   Social Health Insurance

2.2.1   Definition Social Health Insurance (SHI)

2.2.2   The Objectives of Social Health Insurance

2.2.3   Social Insurance Scheme

2.2.4   Development of Schemes

2.2.5   Benefit Package

2.2.6   Social Insurance in Indonesia

2.2.7   Civil Servant Social Health Insurance Scheme (Askes)

2.2.8   Private Employee Social Health Insurance Scheme (Jamsostek)

2.2.9   Community Health Insurance For the Poor (Jamkesmas)

2.2.10   The Aceh Health Insurance (JKA)

2.3   Reimbursement Of Health Care

2.3.1   Characteristics of Reimbursement

2.3.2   Types of Healthcare Reimbursement Methodologies

2.3.3   Retrospective Payment

2.3.4   Fee for Service Method

2.3.5   Prospective Payment Methods

2.3.6   Capitation Payment Method

2.3.7   Global Payment Method

2.4   Casemix And Diagnosis Related Group (DRG)

2.4.1   Casemix and Casemix Management

2.4.2   Hospital Casemix

2.4.3   History of Diagnosis Related Group (DRG)

2.4.4   Definition of DRG

2.4.5   Data in DRG

2.4.6   The Purpose of DRG

2.4.7   The Advantages of DRG

2.4.8   Major Diagnostic Category (MDC)

2.4.9   Type of DRG

2.4.10   The Components DRG System

2.4.11   Guide to Implement DRGs

2.4.12   Strategies for Casemix Implementation System in the Hospital

2.5   The Experiences of High And Low Income Countries in Implementing the Casemix System

2.6   Conceptual Framework

2.7   Conclusion

Chapter 3     Methodology

3.1   Introduction

3.2   Background of Study Area

3.3   Duration of the Study

3.4   Study Design

3.4.1   Qualitative Study

3.4.2   Quantitative Study

3.5   Ethical Approval

3.6   Conclusion

Chapter 4     Results

4.1   Introduction

4.2   Evaluation the JKA Programme.

4.2.1   The Government Commitment in JKA Financing Programme

4.2.2   The Impact of JKA Financing to Other Publicly Funded Programmes

4.2.3   The Adequacy of Financing JKA Programme

4.2.4   Hospital Tariff under JKA

4.2.5   Technical Capacity of Claims Verificators

4.2.6   The Impact of JKA Financing on Health Care Services

4.3   Assessment of Feasibility in Implementing the Casemix System.

4.3.1   The Commitment and Government Policy in Casemix Implementation.

4.3.2   The Readiness of Hospital in Implementing Casemix Sytem

4.4   The Comparison of JKA and INA CBGs Tariff.

4.4.1   Hospital Selection.

4.4.2   Data Collection.

4.4.3   Data Trimming Process.

4.4.4   The Tariff Comparison Based on Type of Hospital.

4.5   Conclusion

Chapter 5     Discussion and Conclusion

5.1   Introduction

5.2   Background of Respondents

5.3   The Government Commitment in JKA Financing Programme.

5.4   The Evaluation of JKA Programme Financing

5.5   The Impact of JKA Financing on Health Care Services

5.6   Assessment of Feasibility in Implementing Casemix System

5.6.1   The Preparation of JKA Implementation Based on Aceh-CBGs

5.7   The Readiness of JKA integrated with the JKN Based on INA CBGs

5.8   The Comparison of JKA and INA CBGs Tariff.

5.8.2   Data Collection.

5.8.3   Casemix Data

5.8.4   Comparison of Hospital Tariff

5.9   The Limitation of the Study

Chapter 6    Conclusion and Recommendation

6.1   Introduction

6.2   Conclusion

6.3   Recommendations

Refferences

List of Figures

Figure 2.1: A theory of change due to health insurance

Figure 2.2: Use of DRGs by Stages

Figure 2. 3: Typical DRG structure for a Major Diagnostic Category

Figure 2.4: The Implementation of DRG

Figure 2.5: Conceptual Framework

Figure 3.1: Map of Aceh

Figure 3.2: Qualitative Study Design

Figure 3.3: Quantitative Study Design

Figure 4.1: Financial Allocation of JKA Programme in 2010 - 2014

List of Tables

Table 2.1: Status of Social Health Insurance in Low-and Middle-Income Countries

Table 2.2: List of Major Diagnostic Category (MDC)

Table 3.1: Population Census of Aceh Province in 2010

Table 3.2: List of Respondents

Table 3.3: List of Type B, C and D Public Hospitals in Aceh Province

Table 4.1: Budget Realization* of JKA in 2010

Table 4.2: Allocation and Realization of JKA Funds Based on Type of Services 2010

Tabel 4.3: Allocation and Realization of JKA Funds Based on Type of Services 2011 (January – August)

Table 4.4: Basic Information Of Three General Hospitals in Aceh (B, C and D)

Table 4.5: Total Number of Inpatients Cases In The Three Hospitals

Table 4.6: Gender Distribution of Patients in The Three Hospitals

Table 4.7: Mean Age (Years) of Patients in TheThree Hospitals

Table 4.8: Length of Stay of Patients in The Three Hospitals

Table 4.9: Distribution of Length of Stay in The Three Hospitals

Table 4.11: Distribution Patients by CMG

Table 4.11: Top Ten CBGs in Cut Meutia General Hospital and Length of Stay (LOS)

Table 4.12: Top Ten CBGs in Tgk Chik Di Tiro General Hospital and Length of Stay (LOS)

Table 4.13: Top Ten CBGs in Sabang General Hospital and Length of Stay (LOS)

Table 4.14: Discharge Status in INA-CBGs

Table 4.15: Discharge Status of Patients in The Three Hospitals.

Table 4.16: Severity of Illness of Patients in The Three Hospitals

Table 4.17: ALOS by Severity of Illness in The Three Hospitals.

Table 4.18: Distribution of Data Inliers and Outliers in The Three Hospital

Table 4.19: The Tariff Comparison in Cut Meutia General Hospital (Type B)

Table 4.20: The Tariff Comparison in Tgk Chik Di Tiro General Hospital (Type C)

Table 4.21: The Tariff Comparison in Sabang General Hospital (Type D)

Table 4.22: The Tariff Comparison in Cut Meutia General Hospital (Type B) Based on Type of CBGs

Table 4.23: The Tariff Comparison in Tgk Chik Di Tiro General Hospital (Type C) Based on Type of CBGs

Table 4.24: The Tariff Comparison in Sabang General Hospital (Type C) Based on Type of CBGs

Table 4.25: Different in Hospital Tariff between INA CBGs and JKA(fee-for-service) based on Top 5 in Hospital Type B

Table 4.26: Difference in Hospital Tariff between INA CBGs and JKA(fee-for-service) based on Top 5 in Hospital Type C

Table 4.27: Different in Hospital Tariff between INA CBGs and JKA(fee-for-service) based on Top 5 in Hospital Type D

Table 4.28: Difference in Hospital Tariff between INA CBGs and JKA(fee-for-service) Based on Severity Level in Hospital Type B

Table 4.29: Different in Hospital Tariff between INA CBGs and JKA(fee-for-service) Based on Severity Level in Hospital Type C

Table 4.30: Different in Hospital Tariff between INA CBGs and JKA(fee-for-service) Based on Severity Level in Hospital Type D

Table 4.31: Different in Hospital Mean Tariff between INA CBGs and JKA(fee-for-service) each type of Hospitals

Table 4.26: Difference in Hospital Income Comparing Tariff between INA CBGs and JKA in Each Type of Hospitals

Table 5.1: Projection of Gas and Special Autonomy Funds 2011-2027

List of Appendices

Appendix 1 Interview Guide For Qualitative Study

Appendix 2 List Of In-Depth Interview Respondents

Appendix 3 The Fee-For Service and INA-CBG Tariff of Cut Meutia General Hospital

Appendix 4 The Fee-For Service and INA-CBG Tariff of Tgk Chik di Tiro General Hospital

Appendix 5 The Fee-For Service and INA-CBG Tariff of Sabang General Hospital

Acknowledgements

It gives us great pleasure in expressing our gratitude to all those people who have supported us and had their contributions in making this book possible. First and foremost, we must acknowledge and thank The Almighty Allah for His blessing, protecting and guiding us throughout this period. We could never have accomplished this without the faith we have in the Almighty.

We would like to express our special thanks to Prof. Hasbullah Thabrany, The Head of Health Economic Study Center, Faculty of Public Health, Universitas Indonesia and Prof. Supeni, Faculty of Public Health, Universitas Indonesia. Our great appreciation to Iskandar, Syahrial, and Erizar, for their great supports, and also special thanks to all our colleagues in Health Academy of North Aceh.

We also like to express our sincere thanks to the Government of Aceh, the Head of Aceh Health Department and the Directors of the Hospitals who had given us the permission conduct this study and granted the access to the data for the research. Special thanks to Institute of Aceh Human Resources Development for being providing the sponsorship to Dr Irwan Saputra to conduct this research.

Dr Irwan Saputra would like to remember and thank his parents and his family (his beloved wife Gusti Heranita, and his sweetheart children; Ahmad Fatih Alghaisan, Nibras Davia Althaf and Lubna Talia Althaf) for his greatest love. He strongly believes that his prayers played very important role in his life. Eventhough they are not always with him, with their prayers, Insha-Allah Taa’la everything will be good (Ameen).

I     INTRODUCTION

1.1   Intoduction

Globally there exists an enormous mismatch between countries’ health financing needs and their current health spending. Developing countries account for 84 percent of global population and 90 percent of the global disease burden, but only 12 percent of global health spending. The poorest countries bear an even higher share of the burden of disease and injury, yet they have the least resources for financing health services. These underlying population and epidemiological dynamics will have profound effects on the economies and future health needs of all countries. The world’s population is predicted to grow significantly higher (7.5 billion by 2020 and to 9 billion by 2050) that mostly occurs in developing countries, especially in low income countries. In the next two decades, there were changes in population size along with the structure that might increase the health care spending in all of the countries. In addition to this, demographically developing countries will face 2–3 percent annual increases in health care expenditure (Gottret & Schieber 2006).

Health financing in many low and middle-income countries (LMICs) is characterized by high levels of out-of-pocket expenditure for serious illnesses leading to potentially catastrophic payment for health care among its citizens (WHO 2005). Financial constraint is one of the major barriers of access to healthcare in these countries for marginalized sections of society where health care expenditure is a major cause of impoverishment (Xu 2003; Peters 2002; Garg 2007; Wagstaff 2003; Russell 2004). A study of 59 countries found the lack of health insurance as one of the main factors engendering health expenditure at a level that can be considered as catastrophic, up to nearly 40% of all household expenditure, and recommended the provision of some forms of financial risk protection (Xu 2003). Such expenditure is likely to cause further impoverishments among households; for example, 3-5% of the Indian annual poverty rate can be attributed to high level of health expenditure relative to total household expenditure (Garg 2007).

In the past, health care system focused attention was on infectious diseases such as poliomyelitis and tuberculosis (TB) or other acute crisis; the challenges facing health care today are more likely to relate to chronic disease, alcohol and drug addiction, work-related illness and accidents, a slow decline, and remission rather than recovery or cure. In addition, there has been a rapidly expanding knowledge base giving rise to new treatment technologies, which are not only expensive, but also which have changed the nature and quality of health care. Public expectations of higher standards of care, including the use of high technology care, are in part of the result of rising educational standard. Hospitals and professionals who work in them now have to confront these expectations (Scoot & Scoot 2003).

Catastrophic spending (for each individual/household) is usually defined as occurring when hospitalization spending for that person/household as a proportion of ability to pay (household consumption spending less combined survival income for all household members) exceeds a certain threshold (Mahal 2010). The threshold value can range from 5 to 40% (Wyszewianski 1986; Mahal 2010; Xu 2003; WHO 2000); thus there is no much agreement on how to measure this notion (Wagstaff 2003; Russell 2004). The measurement is considered theoretically unsound (Flores 2008); and the welfare implication of the measurement is unclear especially when measured across income classes. It is most likely to be true that for the already impoverished a 40% drop in their usual consumption is likely to affect their wellbeing significantly. The need for large one time or even large life-time health expenditure can be prevented if health insurance successfully spreads risk across time and people. Thus, social health insurance has the potential to prevent such impending impoverishment.

Social health insurance schemes are generally considered as health insurance schemes provided by governments to its citizens, especially to low and middle income populations. Recently, apart from governments, several non-government organizations at the community level provide social health insurance in developing countries (Churchil 2006). Furthermore Townsend (1994) added that Social health insurance can bring about welfare improvement through improved health status and maintenance of non-health consumption goods through ensuring that health expenditures are smoothed over time and that there is no significant decline in household labor supply.

Historically, social health insurance originated in developed countries as work related insurance programmes and the coverage has been gradually expanded to the non-working parts of the population (Saltman 2004). In recent years, social health insurance is being introduced in parts of the developing world as an alternative to tax financing and out-of pocket payments (Vietnam 1993, Nigeria 1997, Tanzania 2001 and Ghana 2005). Discussions on implementation of the schemes are underway in several countries (South Africa, Zimbabwe, Cambodia, Laos, Malaysia) and countries with social health insurance already in place are making vigorous efforts to extend coverage to the informal sector (i.e. self-and unemployed, retired people) (e.g. Colombia, Mexico, Philippines, and Vietnam) (Wagstaff 2007).

In addition, WHO (2005) mentioned that the transition of health systems to reach universal and equitable access to quality health care such as social insurance programme requires a sustainable financial resource base in meeting the health needs of the population, without causing impoverishment, and contributing to the attainment of national development goals and economic growth through improved health status. Moreover, Currin (2001) stated that, a critical objective of social health insurance is equity in access to health care, which may be reflected in the use of health care. Pattern of utilization are obviously very important with regard to the impact of provider payment mechanisms. One approach that has been done over 44 countries in developed and developing countries is financing health services by applying casemix system.

Casemix system is amongst the best solutions in achieving an efficient and effective health care financing system. It is a special form of patient classification system that combines type of disease treated in hospital with cost of treatment in providing a quality and effective treatment. This system can be used to gauge usage of resources needed to provide health care service in hospital according to patient’s conditions. It is very appropriate especially in justifying the usage of optimum resources in hospital. Casemix also facilitates in the implementation of quality enhancement programme in line with its original objectives of classifications. Information patients’ treatments such as length of stay help in identifying differences in treatment and problem in quality of patient care, so it can be highlighted and managed immediately. This system can also encourage hospitals to standardize the treatment process using clinical guideline and critical pathways in accordance to best practice to ensure that patients received the best and most effective treatment (Aljunid et al. 2005).

Furthermore, Rohaizat (2010) added that the implementation of casemix systems in Europe has given a significant impact on health care services. For example, Germany has started the programme since 1993, and enabled to decrease the Length of Stay (LOS) and increased number of admission of patients. In Australia, casemix has resulted primarily in improving health services, reducing the waiting list of patients and improving the equity in hospital financing. In addition, Japan as one of the developed countries in Asia has also started the programme in 1998 and proven to reduce LOS by 20%, then, Malaysia has begun the programme in 2002 by using the UNU-CBGs, which currently has been developed into MY- DRG. This system followed by such countries in South East Asia as Indonesia, Philippine, some middle-east countries and Latin America.

Indonesia as a country with the largest population in South East Asia and fourth largest in the world is facing a serious problem regarding the achievement of universal coverage. The inequitable distributed population in the large territory area and more than 17,000 islands