Implementing Electronic Health Records in Philippine Primary Care Settings: Mixed-Methods Pilot Study
Bibliographic record
Abstract
Background: Between 2020 and 2022, the Philippine Primary Care Studies program, a government-funded initiative supporting universal health care implementation, piloted two electronic health records (EHR) systems across urban, rural, and remote primary care sites. Objective: The study aimed to evaluate the implementation of two EHR systems in diverse primary care settings in the Philippines over a three-year period. Methods: This implementation study used an explanatory mixed methods design. Two EHR systems were deployed: an Open Medical Records System (OpenMRS)-based platform in 2016, and a Microsoft-based system in 2021. Both systems integrated clinical documentation, pharmacy, laboratory, and reporting modules. Implementation strategies included training workshops and materials, iterative user feedback loops, and infrastructure cofinancing with local governments. Surveys were administered yearly to all end users. The primary outcome was behavioral intention to use the system. Quantitative data were supplemented by inductive content analysis of qualitative responses to explain observed trends. Results: A total of 351 survey responses were collected from 2020 to 2022. In 2020, the intention to use the OpenMRS-based EHR was high across all sites. By 2022, following the launch of the Microsoft-based EHR, acceptability declined significantly among doctors and administrative staff, particularly at the urban site. In contrast, the remote site which retained the OpenMRS-based system maintained high acceptability levels. Qualitative findings revealed that while the new EHR system provided a more privacy-focused design, users preferred a cross-platform EHR to allow more flexible access to patient data. At the rural site where the EHR was used to facilitate task-shifting among nurses involved in clinical management, users were less impacted by this shift. Conclusions: The disparities in EHR acceptability across urban, rural, and remote sites were influenced by contextual, technical, and demographic factors. The decline in acceptability following the EHR system transition highlights the importance of implementation strategies that reflect the specific needs and capacities of each setting. These findings offer practical insights for adapting EHR systems to diverse primary care contexts.
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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 | 0.016 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".