The role of medical regulations and medical regulators in fostering the use of eHealth data for strengthened continuing professional development (CPD): a document analysis with key informants’ interviews
Bibliographic record
Abstract
BACKGROUND: In recent times, medical regulators have been taking measures to strengthen CPD requirements for medical practitioners. In particular, greater emphasis has been placed on CPD activities linked to workplace-based assessment, health outcomes measurement, and quality improvement. These activities require the use of health data, and eHealth data analytics is emerging as a digital solution to simplify tasks and processes. Although there is a growing interest and need for alignment between regulatory policies, impactful CPD activities, and digital health research and innovation, there is little or no research into the role that medical regulations and regulators are playing in fostering the use of eHealth data to strengthen CPD. METHODS: Medical regulations and CPD requirements of 5 selected countries (Australia, Canada, New Zealand, UK, USA) were collected and analysed using the systematic READ approach for qualitative health policy research. Online semi-structured interviews were conducted with 20 key informants from 13 medical bodies to validate findings and gather additional insights. Informants were purposively selected because of their direct involvement in policy development. The interviews were analysed using a hybrid approach of deductive and inductive thematic analysis. The COREQ checklist was used for reporting the findings. RESULTS: The documents analysed do not mention the use of eHealth data for CPD purposes or refer to it only as a potential data source for CPD completion and compliance. Participants corroborated the document analysis results and provided insights into the following themes: context and rationale of current policy choices and future policy development; roles, responsibilities, and functions of relevant medical bodies in fostering the use of eHealth data for strengthened CPD; barriers, challenges, and enablers for implementation. CONCLUSION: Current medical regulations and CPD requirements do not foster the use of eHealth data for CPD purposes. Recommendations for future policy development are reliant on further research on key policy concepts, regulators' internal organisational factors, and interorganisational collaboration within the CPD ecosystem. The alignment of all relevant CPD stakeholders is required to tackle existing barriers and challenges and promote digital health innovation in the CPD landscape. Medical regulators are called to play a leadership role in this scenario.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".