PRIMARY HEALTH CARE PERFORMANCE INDICATORS MODEL IN A PERFORMANCE-BASED CAPITATION SCHEME TO MEASURE NATIONAL HEALTH INSURANCE HEALTH SERVICE EQUITY (PROTOCOL STUDY)
1Faculty of Public Health, University Indonesia, Depok, Indonesia
* Corresponding author; email: cicilyacandi@ui.ac.idAbstract
The inequity of health services still occurs after the implementation of the National Health Insurance (JKN). Regular monitoring of the Performance of Primary Health Care is the key to reducing the inequity of health services as the main goal of JKN. The application of Performance-Based Capitation (KBK) with three indicators since 2016, shows an improvement in Primary Health Care’s performance in improving quality and efficiency in the first level of service in comparison. Primary Health Care performance is influenced by Primary Health Care capacity and has an impact on the output of primary service performance (Health Service Equity). This study aims to develop a model of performance indicators, Primary Health Care capacity and equity indicators, in order to measure healthcare equity. The research design used an exploratory sequential-mixed method. The research is divided into three stages. Phase one is a systematic review to identify indicators that can be used in measuring the capacity, the performance of Primary Health Care and healthcare equity. Phase two is carried out with a qualitative approach with the Consensus Decision Making Group (CDMG) to determine indicators that can be used in measuring the capacity and performance of Primary Health Care as well as measuring health service equity with experts. Phase three is to develop a model of Primary Health Care performance indicators based on a capitation scheme that can measure the equity of healthcare access. This stage is carried out using Multilevel Structural Equation Modeling (MSEM) analysis. It is hoped that this model can be used by Social Security Administrator for Health (BPJS Kesehatan) and the Ministry of Health to measure the performance of Primary Health Care and health service equity.
Article notes
Competing Interest Statement
The authors have declared no competing interest.
Funding Statement
The author(s) received no specific funding for this work.
Introduction
Equity to access to healthcare is a challenge faced by many countries around the world. To achieve equitable access to health services, every country in the world is obliged to provide or bear the health insurance of its population in accordance with the main target of Sustainable Development Goals (SDGs) achieving Universal Health Coverage (UHC). This is in line with the implementation of the National Health Insurance (JKN) which is the state’s commitment to achieving UHC [1].
The principle of equity in the implementation of JKN according to the National Social Security System (SJSN) Law is the similarity of getting services according to medical needs regardless of how much contribution is paid. There has been an increase in access and utilization of health services since JKN began to be implemented in 2014. The utilization rate of health services reached 1.07 million per day, an increase of more than 100%. The utilization rate in Primary Health Care increased by 61.2 per cent to 140.47 per mile [2]. However, these achievements do not necessarily make JKN’s goal of achieving healthcare equity realized.
BPJS Kesehatan implements performance-based capitation payments (KBK), which aim to improve Primary Health Care performance through monitoring Primary Health Care performance and linking it to monthly capitation payments. Of the three Primary Health Care KBK performance indicators, only non-specialist referral ratio figures indicators were achieved nationally. Contact Number is an indicator to determine the level of accessibility and utilization of primary services in Primary Health Care by participants. But this indicator is assessed without taking into account geographical conditions and population density. So an indicator is needed that takes into account geographical conditions and the ratio of Primary Health Care compared to participants.
Another indicator is the Controlled Prolanis Participant Ratio, Prolanis is the abbreviation of ‘Program Pengelolaan Penyakit Kronis’ or Chronic Disease Management Program. This indicator is to measure the number of Prolanis DM and HT participants in Primary Health Care without taking into account the health condition of the participants and without strengthening promotional and preventive efforts. Currently, the preparation of new benefits of the JKN program is being carried out. The new benefit concept focuses on improving preventive efforts with increased health screening coverage. This concept is in line with the health transformation launched by the Ministry of Health. There was an increase in individual preventive services in the form of health screening for 14 catastrophic diseases. The new benefits in question are based on basic health needs with the aim of providing equitable and sustainable health services. So indicators are needed that can measure promotive and preventive efforts.
Within the framework of Primary Health Care performance monitoring, there are three interconnected domains, namely capacity, performance and output (quality, efficiency and equity). The performance of the first level of services is influenced by the capacity of the Primary Health Care and the output is the result of the performance of the Primary Health Care [3]. Several studies using a qualitative approach show that the performance achievement of KBK is influenced by the capacity of Primary Health Care which includes the availability of human resources, the availability of infrastructure, governance and organization, the availability of information systems, and financing [4]. In order to be able to carry out comprehensive performance monitoring, an Primary Health Care capacity indicator is needed.
The performance output in the KBK scheme shows an improvement in the quality of Primary Health Care, the quality index value achieved by Performance-based Primary Health Care is better than similar values in the Non-Performance-based Primary Health Care group. Although there is an increase in JKN’s first-level service performance nationally and has an impact on quality and efficiency, there are still differences in performance achievements between regions, per type of Primary Health Care and each Primary Health Care [1]. Therefore, it is necessary to assess the performance of Primary Health Care by region in order to measure equity.
This research is to develop a model of Primary Health Care performance indicators in the KBK scheme that can be used to periodically measure and monitor performance. So that in the end, it can increase the equity of health services.
Research Question
- What are the Primary Health Care capacity and performance indicators?
- What is the equity measurement indicator of Primary Health Care?
- What is the Primary Health Care performance indicator model in the KBK scheme that can measure healthcare equity?
Materials and Methods
Conceptual Framework
This research is expected to provide updates in the form of a KBK performance indicator model and the weight of assessing the calculation of performance achievements in accordance with the new JKN Program benefit policy based on basic health needs.
This conceptual framework is structured in reference to the theoretical framework which is a modification between the research primary service monitoring framework of Erica Barbazza (2019), Dionne Kringos (2020) and Ebert (2017). The primary service monitoring framework that is compiled consists of 3 dimensions, namely Primary Health Care capacity, Primary Health Care performance and primary service results (Fig.1).
Research Design
The research method used is mixed methods with qualitative methods and quantitative methods. There are several reasons to use mixed methods in health science research. Researchers can seek to look at problems from multiple perspectives to enhance and enrich the meaning of a single perspective. Researchers may also want to contextualise information, to take macro pictures of a system and additional information about individuals. Another reason to combine quantitative and qualitative data is to develop a complete understanding of a problem; develop complementary images; compare, validate, or triangulate results; provide context illustrations for trends; or to examine the process of shared experience (Plano Clark, 2010 in John W. Creswell, 2018). The research consists of 3 phases (Fig. 2) are described below (Table. 1). All the operational definitions are explained underneath (Table. 2).
Ethical Research
This research has been approved by the Research and Community Service Ethics Committee of the Faculty of Public Health, University of Indonesia (no. Ket-31/UN2.F10.D11/PPM.00.02/2023) From Jan 2023 – Feb 2023
Data Availability
No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.