Barriers and facilitators to Electronic Medical Record (EMR) use in an urban slum
Bibliographic record
Abstract
OBJECTIVE: Rapid urbanization has led to the growth of urban slums and increased healthcare burdens for vulnerable populations. Electronic Medical Records (EMRs) have the potential to improve continuity of care for slum residents, but their implementation is complicated by technical and non-technical limitations. This study sought practical insights about facilitators and barriers to EMR implementation in urban slum environments. METHOD: Descriptive qualitative method was used to explore staff perceptions about a recent open-source EMR deployment in two primary care clinics in Kibera, Nairobi. Participants were interviewed using open-ended, semi-structured questions. Content analysis was used when exploring transcribed data. RESULTS: Three major themes - systems, software, and social considerations - emerged from content analysis, with sustainability concerns prevailing. Although participants reported many systems (e.g., power, network, Internet, hardware, interoperability) and software (e.g., data integrity, confidentiality, function) challenges, social factors (e.g., identity management, training, use incentives) appeared the most important impediments to sustainability. DISCUSSION: These findings are consistent with what others have reported, especially the importance of practical barriers to EMR deployments in resource-constrained settings. Other findings contribute unique insights about social determinants of EMR impact in slum settings, including the challenge of multiple-identity management and development of meaningful incentives to staff compliance. CONCLUSIONS: This study exposes front-line experiences with opportunities and shortcomings of EMR implementations in urban slum primary care clinics. Although the promise is great, there are a number of unique system, software and social challenges that EMR advocates should address before expecting sustainable EMR use in resource-constrained settings.
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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 | 0.006 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.005 |
| 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".