Validation of an Interoperability Framework for Linking mHealth Apps to Electronic Record Systems in Botswana: Expert Survey Study
Bibliographic record
Abstract
BACKGROUND: Electronic record (eRecord) systems and mobile health (mHealth) apps have documented potential to improve health service delivery, resulting in increased global uptake. However, their interoperability remains a global challenge hindering diagnosis, monitoring of health conditions, and data access irrespective of geographic location. Given the widespread use of mobile devices by patients and health care providers, linking mHealth apps and eRecord systems could result in a comprehensive and seamless data exchange within a health care community. The Botswana National eHealth Strategy recognizes interoperability as an issue and mHealth as a potential solution for some health care needs but is silent on how to make mHealth apps interoperable with existing eRecord systems. A literature review and analysis of existing mHealth interoperability frameworks found none suitable for Botswana. As such, it was critical to conceptualize, design, and develop an mHealth-eRecord Interoperability Framework (mHeRIF) to enhance the interoperability pillar of the Botswana National eHealth Strategy and leverage the full benefits of linking mHealth apps with other health information systems. OBJECTIVE: This study aimed to validate the developed mHeRIF and determine whether it requires further refinement before consideration towards enhancing the National eHealth Strategy. METHODS: Published framework validation approaches guided the development of a survey administered to 12 purposively selected local and international eHealth experts. In total, 25% (3/12) of the experts were drawn from Botswana, 25% (3/12) were drawn from low- and middle-income countries in sub-Saharan Africa, 25% (3/12) were drawn from low- and middle-income countries outside Africa, and 25% (3/12) were drawn from high-income countries. Quantitative responses were collated in a Microsoft Excel (Microsoft Corp) spreadsheet for descriptive analysis, and the NVivo software (version 11; QSR International) was used to aid the thematic analysis of the qualitative open-ended questions. RESULTS: The analysis of responses showed overall support for the content and format of the proposed mHeRIF. However, some experts' suggestions led to 4 modest revisions of the mHeRIF. CONCLUSIONS: Overall, the experts' reviews showed that the mHeRIF could contribute to the National eHealth Strategy by guiding the linking of mHealth apps to existing eRecord systems in Botswana. Similarly, the experts validated an architectural model derived from the mHeRIF in support of the first mobile telemedicine initiative considered for national rollout in Botswana. The mHeRIF helps identify key components to consider before and after linking mHealth apps to eRecord systems and is being considered for use as the foundation of such interoperability in Botswana.
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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 | 0.091 | 0.097 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".