Barriers and challenges to Primary Health Care Information System (PHCIS) adoption from health management perspective: A qualitative study
Bibliographic record
Abstract
Enactment of a National Health Information System regulation in 2014 by the Indonesian government enabled the integration of healthcare data using electronic systems in the country. However, limited information was gained regarding the barriers from the healthcare management point of view that might cause slowness of adoption. We evaluated the implementation of the Primary Health Care Information System (PHCIS) in order to explore and describe the barriers and challenges during the adoption of Primary Health Care (PHC) from a health management perspective, and propose a PHCIS design to minimize the barriers. A qualitative form of research was conducted in an urban area of Banten Province from February–April 2018, as that area has gained experience of PHCIS implementation for more than five years. An in-depth interview was recorded to explore and describe the barriers during PHCIS adoption. Four themes of the barriers have been identified from a strategic and operational level perspective, namely: human resources, infrastructure, organizational support, and processing. Our analysis suggests that PHCIS adoption could be more effective if there were greater interaction between human resources, infrastructure, organizational support, and process factors. Hence, involvements including: strengthening staff competency, improving technology infrastructure, increasing organizational support with more investment for high-quality PHCIS, and re-designing the PHCIS to accommodate the basic process of PHC , might be beneficent to improve PHCIS adoption.
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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 | 0.013 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".