Methodologic Issues in Health Informatics Trials: The Complexities of Complex Interventions
Bibliographic record
Abstract
OBJECTIVE All electronic health (e-health) interventions require validation as health information technologies, ideally in randomized controlled trial settings. However, as with other types of complex interventions involving various active components and multiple targets, health informatics trials often experience problems of design, methodology, or analysis that can influence the results and acceptance of the research. Our objective was to review selected key methodologic issues in conducting and reporting randomized controlled trials in health informatics, provide examples from a recent study, and present practical recommendations. DESIGN For illustration, we use the COMPETE III study, a large randomized controlled clinical trial investigating the impact of a shared decision-support system on the quality of vascular disease management in Ontario, Canada. RESULTS We describe a set of methodologic, logistic, and statistical issues that should be considered when planning and implementing trials of complex e-health interventions, and provide practical recommendations for health informatics trialists. CONCLUSIONS Our recommendations emphasize validity and pragmatic considerations and would be useful for health informaticians conducting or evaluating e-health studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.903 | 0.950 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.011 | 0.007 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.005 | 0.043 |
| Scholarly communication | 0.017 | 0.020 |
| Open science | 0.012 | 0.009 |
| Research integrity | 0.015 | 0.018 |
| Insufficient payload (model declined to judge) | 0.004 | 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".