Barriers and challenges to Primary Health Care Information System (PHCIS) adoption from health management perspective: A qualitative study
Why this work is in the frame
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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. Keywords: Health care organization, Primary health care information system, Adoption, Primary health care, Barrier and challenge, Developing country
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it