Evaluation of an ICD logging system to supplement an EMR in a Sub-Saharan country
Bibliographic record
Abstract
Adoption of electronic medical records (EMRs) has been spotty and sluggish in the world, including the United States, despite the multiple benefits of medical technology and informatics. Though there are difficulties in establishing and maintaining an EMR system in a developing country, it is not impossible. The International Classification of Diseases (ICD) Logger, called CREDO (Clinical Rotation Evaluation and Documentation Organizer), developed by Edward Via College of Osteopathic Medicine (VCOM), provides a straightforward, economical EMR system to use in a developing country, such as Ghana. However, with a recently established EMR system developed locally and being used at the target new hospital, Healthwise Medical Center, the aim of the study was to use the common medical documentation language of the World Health Organization (WHO) ICD-10 codes to add value to the local EMR. This demonstration enabled the comparison of medical encounters in Ghana to those in the United States, specifically in Appalachia where VCOM students typically do their clinical rotations. We also evaluated the issues and tested the CREDO ICD Logger as a simple, stand-alone EMR system. Therefore, by collecting ICD data twice weekly from Ghana, a data point in Sub-Saharan Africa, it became possible to compare a public health snapshot of developing countries and sites in the United States.
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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 | 0.024 | 0.061 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".