Is South Africa ready for a national Electronic Health Record(EHR)
Bibliographic record
Abstract
BackgroundeHealth Strategies in countries have shown a trend that countries are moving to Electronic Health Records(EHR).EHR implementation is expected to produce benefits for patients, professionals, organisations, and the population as a whole.The use of some format of an Electronic Health Record is used by many countries and others are in the implementation or planning phases.South Africa has kicked of the project to implement a national EHR as part of the national eHealth Strategy.This study aims to analyse the key success factors from other EHR implementation projects and evaluate if South Africa is ready to implement an EHR. MethodsThis research study will use a qualitative case study research method approach, to analyse the data and debate an opinion to answer if South Africa is ready for a National Electronic Health record. ResultsAnalysis of the following country case studies; Australia, Belize, Canada, Denmark, Estonia, Hong Kong, Netherlands and Sweden will be tabled under the subcategories to compare the different country parameters with one another to establish key success factors. ConclusionsThe key success factors can form the basic blueprint objectives in a guideline for not only the South African implementation of an EHR but any national initiative.As seen in the results the case countries had several ways to achieve the same outcome.This highlights the fact that there is not a right or wrong but a requirement to contextualise the factors in the country environment.Unique patient Identification: .....
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".