Understanding Health Information Systems Utilization Across Public Health Centers in Indonesia: Cross-Sectional Study
Bibliographic record
Abstract
Background: The primary health care service in Indonesia consists of 10,260 public health centers (Puskesmas), which play a major role in providing health care in the community, recording and reporting health data using digital health information systems (HIS) or manual reports. The utilization of HIS across Puskesmas is crucial to capture the dynamic evolution of health problems and monitor interventions, thus providing effective primary health care services for the community. Objective: This paper provides a national-level baseline mapping of HIS utilization in Indonesian Puskesmas. It evaluates the number of HIS used, associated challenges, and contextual factors influencing system adoption. Methods: A cross-sectional survey was carried out covering all Puskesmas across 34 Indonesian provinces between January and February 2022. The questionnaire covered a list of HIS used by Puskesmas, which developed the HIS, and the utilization and challenges during HIS implementation. Descriptive statistical analysis and bivariate analysis were applied. Results: A total of 2606 (25.5%) public health centers across 34 provinces participated in this study. On average, Puskesmas reported using 30 different HIS platforms, with notable variation across provinces and islands. Most systems (n=62,060, 72.94%) were developed by national ministries, though local governments and third parties also contributed. Despite 91.5% of respondents reporting that HIS aligned with their needs and 90% claiming data use for decision-making, many centers faced operational barriers: 49% (n=132,300) of systems required excessive data entry, 33% (n=89,100) experienced frequent downtime, and 29% (n=78,300) lacked automated analysis features. In terms of the infrastructure supporting HIS implementation, 9.45% (n=138) of Puskesmas have no access to the internet, while only 28.9% (n=422) have access to robust and efficient internet connections. As for the human resources, the study reveals that each health personnel manages up to six different HIS for data reporting tasks, 74.30% (n=1133) of Puskesmas only received training at the initial system's implementation stage, and 80.51% (n=1225) of respondents report the existence of an informal knowledge transfer process among the staff. The bivariate analysis shows that Puskesmas with the characteristics of being located in Java island and urban areas possessed higher accreditation levels, had more training and knowledge transfer, and had a greater chance to use >30 HIS. Conclusions: This descriptive study highlights substantial fragmentation in Indonesia's HIS environment and reveals critical disparities in system infrastructure, usability, and workforce capacity. Recommendations should be tailored to different contexts: offline-compatible systems and basic digital literacy training are needed in rural areas, while urban Puskesmas may benefit from advanced integration and analytics tools. Future research should address HIS interoperability, impact assessment, cost-effectiveness, and qualitative user experience through longitudinal and mixed methods studies to guide Indonesia's digital health transformation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".