Electronic health records in non-hospital settings of developing economies: A systematic review on enablers and barriers
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In recent years, rapid advancements in Information and Communications Technology (ICT) have greatly transformed the healthcare landscape by streamlining health data management and providing decision-makers with secure and convenient access to health records. In developing economies, limited resources hinder healthcare access. Implementing EHRs in non-hospital settings is essential for enhancing healthcare quality and accessibility. While existing literature supports EHR use, further research is needed to pinpoint specific barriers and enablers. Using PRISMA guidelines, 18 relevant articles were systematically analyzed with the Human, Organization, and Technology Fit (HOT-fit) framework to examine these factors in non-hospital settings within developing economies. This study found that human factors take precedence in both enablers and barriers. The first two barriers emphasize the human element, highlighting the critical importance of addressing individual user challenges. However, organizational issues take on a supporting role, highlighting the possibility that the prominence of user-centric challenges stems from the lack of devolution of governance and leadership in non-hospital settings. Additionally, the findings indicate that prioritizing robust IT infrastructure, which meets both functional and usability requirements, remains a fundamental concern for EHR implementation. By focusing on the enablers and barriers of EHR implementation, this study highlights the research gaps that can be explored as well as the potential and challenges that are faced by healthcare systems within the non-hospital settings of -developing economies. From these findings, we infer that further research is needed to identify specific training components for EHR systems to enable individuals for effective system use in non-hospital settings.
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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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| 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