Simulation based research for digital health pathologies: a multi-site mixed-methods study
Bibliographic record
Abstract
Abstract Background The advance of digital health technologies has created new forms of potential pathology which are not captured in current clinical guidelines. Through simulation-based research we have identified the challenges to clinical care that emerge when patients suffer from illnesses stemming from failures in digital health technologies. Methods Clinical simulation sessions were designed based on patient case reports relating to (i) medical device hardware errors (ii) medical device software errors, (iii) complications of consumer technology, and (iv) technology-facilitated abuse. Clinicians were recruited to participate in simulations at three UK hospitals; audiovisual suites were used to facilitate group observation of simulation experience and focused debrief discussions. Invigilators scored clinicians on performance, clinicians provided individual qualitative and quantitative feedback, and extensive notes were taken throughout. Findings Paired t-tests of pre and post-simulation feedback demonstrated significant improvements in clinician’s diagnostic awareness, technical knowledge, and confidence in clinical management following simulation exposure (p<0.01). Barriers to care included: (i) low suspicion of digital agents, (ii) attribution to psychopathology, (iii) lack of education in technical mechanisms and (iv) little utility of available tests. Suggested interventions for improving future practice included: (i) education initiatives, (ii) technical support platforms, (iii) digitally-oriented assessments in hospital workflows, (iv) cross-disciplinary staff and (v) protocols for digital cases. Conclusion We provide an effective framework for simulation training focused on digital health pathologies and uncover barriers that impede effective care for patients dependent on technology. Our recommendations are relevant to educators, practising clinicians, and professionals working in regulation, policy and industry.
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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 | 0.049 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".