Development and Implementation of Stre@mline, a Locally Developed Electronic Health Platform in Uganda (Preprint)
Bibliographic record
Abstract
BACKGROUND Electronic healthcare records(EHR) are especially important in low-resource settings due to their potential to address unique challenges such as the high numbers of requiring long-term treatments who are lost to follow-up, the frequent shortages of essential drugs, poor maintenance and storage of records and inefficient clinical triaging. However, there is a lack of affordable solutions and many challenges in successfully implementing EHRs in low OBJECTIVE To create a locally-developed electronic healthcare record system tailored to the specific context and needs of Ugandan hospitals. METHODS Stre@mline is an EHR platform that has been locally developed by Ugandan clinicians and engineers in Southwestern Uganda. It is tailored to the specific context and the needs of low-resource hospitals. It operates without internet access, incorporates locally relevant standards and key patient safety features, has a medication inventory management component, has local technical support available, and is economically sustainable without funding from international donors. RESULTS Stre@mline is currently used by over 60 000 patients at 2 hospitals, with plans to expand across Uganda. User surveys from Kisiizi Hospital indicate that the vast majority of Stre@mline users find it is easy to use and helpful in increasing clinical efficiency as well as enhancing patient care. CONCLUSIONS The partnership of local clinicians and developers is crucial to the design and adoption of user-centered technologies tailored to the specific needs of low-resource settings. The EHR described here could serve as a model for the development of future appropriate technologies in developing countries.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".