Barriers and facilitators to Electronic Medical Record (EMR) use in an urban slum
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
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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 it