Benefits and challenges of EMR implementations in low resource settings: a state-of-the-art review
Bibliographic record
Abstract
BACKGROUND: The intent of this review is to discover the types of inquiry and range of objectives and outcomes addressed in studies of the impacts of Electronic Medical Record (EMR) implementations in limited resource settings in sub-Saharan Africa. METHODS: A state-of-the-art review characterized relevant publications from bibliographic databases and grey literature repositories through systematic searching, concept-mapping, relevance and quality filter optimization, methods and outcomes categorization and key article analysis. RESULTS: From an initial population of 749 domain articles published before February 2015, 32 passed context and methods filters to merit full-text analysis. Relevant literature was classified by type (e.g., secondary, primary), design (e.g., case series, intervention), focus (e.g., processes, outcomes) and context (e.g., location, organization). A conceptual framework of EMR implementation determinants (systems, people, processes, products) was developed to represent current knowledge about the effects of EMRs in resource-constrained settings and to facilitate comparisons with studies in other contexts. DISCUSSION: This review provides an overall impression of the types and content of health informatics articles about EMR implementations in sub-Saharan Africa. Little is known about the unique effects of EMR efforts in slum settings. The available reports emphasize the complexity and impact of social considerations, outweighing product and system limitations. Summative guides and implementation toolkits were not found but could help EMR implementers. CONCLUSION: The future of EMR implementation in sub-Saharan Africa is promising. This review reveals various examples and gaps in understanding how EMR implementations unfold in resource-constrained settings; and opportunities for new inquiry about how to improve deployments in those 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 | 0.021 | 0.114 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".