**Background:** A robust health information system is essential for an effective health system, yet low-and-middle-income countries (LMICs) like Indonesia face challenges including multiple disparate applications, varied program data requirements, and poor data quality. Although a standard information system (SIMPUS) exists for Indonesian community health centers (CHCs), many health programs have unique applications, creating an additional burden on health centers that must run more than 10 required programs. This study aimed to demonstrate potential disparities in health information system applications and data collection among Indonesian CHCs by province and region.
**Methods:** This was a cross-sectional analysis of data from the 2019 Indonesian Health Facility Research (RIFASKES). The study included all CHCs registered with the Ministry of Health in July 2018 and verified by local District Health Offices. Data were collected from April to May 2019 by trained enumerator teams (minimum Diploma III health education, under 45 years old) who visited each CHC for four days. Enumerators interviewed program managers using a standardized questionnaire and observed documents and applications. The analysis focused on 13 types of health information applications: SKDR (early warning of epidemic diseases), ASPAK (health facilities and supplies), PISPK (Healthy Family Program), P Care (primary care/health insurance), HFIS (health financing information system), SITT (tuberculosis information), SIHA (HIV/AIDS information), SIHEPI (hepatitis information), SIPTM (non-communicable disease information), SIPD3I (vaccine-preventable diseases), ESISMAL (malaria information), SISTBM (community-based health sanitation), and EPPGBM (nutrition information). Territories were divided into seven regions according to Indonesia's Medium-Term Development Plan: Region 1 (Sumatra), Region 2 (Java and Bali), Region 3 (Nusa Tenggara), Region 4 (Kalimantan), Region 5 (Sulawesi), Region 6 (Maluku), and Region 7 (Papua). Significance was assessed using chi-square tests and ANOVA. Data were mapped using STATA version 14's spmap command with quartile-based categorization.
**Key Results:** Of 9,909 total CHCs, 9,831 were analyzed (response rate 99.2%). The majority of CHCs were located in rural areas, and at least 2,262 health centers were not accredited. Most CHCs operated under non-public service (non-BLUD) financial management. On average, health centers had 10 information system programs (mean = 10.37, median = 10.80). Region 2 (Java and Bali) had the highest mean number of applications (10.8–10.9), followed by Region 1 (Sumatra) and Region 3 (Nusa Tenggara). Regions 4 (Kalimantan), 5 (Sulawesi), 6 (Maluku), and 7 (Papua) all had means in the lowest quartile (5.5 to <10.5). Within Region 2, Banten and East Java provinces had the highest means (11.27–11.87). In Region 1, Jambi, Lampung, and Bangka Belitung provinces achieved means in the highest quartile. Outside Regions 1 and 2, only Gorontalo Province had a mean in the highest quartile, and only South Sulawesi had a mean of 10.78 to <11.27. ASPAK, PISPK, and P Care were owned by approximately 90% of health centers nationally. Papua and West Papua had less than 60% ownership for all types of information applications. Jakarta had less than 50% ownership for SIHA, SIHEPI, and ESISMAL. No province achieved over 90% ownership of SKDR. Many provinces had below 70% ownership of HFIS despite it being mandatory for the national health insurance provider.
**Clinical Implications:** This study demonstrates significant geographic disparities in health information system infrastructure across Indonesian CHCs, with eastern Indonesia (particularly Papua and West Papua) substantially underserved compared to Java and Bali. These disparities likely reflect unequal distribution of human resources, electronic devices, internet access, electricity supply, and organizational support. The findings suggest that simply having a standard information system (SIMPUS) is insufficient—targeted interventions are needed to improve application availability in underserved regions. The government should strengthen surveillance data systems, increase provincial and district-level support to CHCs, evaluate existing information systems, improve interoperability, deploy adequate human resources, and continuously update systems. Addressing these disparities is critical because well-functioning health information systems can replace expensive population-based surveys, support research and clinical trials, and potentially assist in disease diagnosis. The study's limitations include the absence of evaluation of implementation processes and data output quality, and the exclusion of some information systems such as medical records and SIMPUS.