Evolution of Human Factors Research and Studies of Health Information Technologies: The Role of Patient Safety
Bibliographic record
Abstract
OBJECTIVES: The objective of this survey paper is to present and explain the impact of recent regulations and patient safety initiatives (EU, US and Canada) on Human Factors (HF)/Usability studies and research focusing on Health Information Technology (HIT). METHODS: The authors have selected the most prominent of these recent regulations and initiatives, which rely on validated HF and usability methods and concepts and aim at enhancing the specific process of identification and prevention of technology-induced errors throughout the lifecycle of HIT. RESULTS: The analysis highlights several points of consensus: 1) safety initiatives or regulations applicable to Medical Devices (MD) tend to extend to HIT, 2) Usability is considered a fundamental dimension of HIT safety, 3) HF/Usability methods and the overall Human Centred Design (HCD) approach are considered efficient solutions to ensure the design of safe and usable HIT. However, it appears that MD manufacturers, and a fortiori HIT designers and developers are still far from being able to routinely apply HCD to their products. DISCUSSION AND CONCLUSION: On the research side, we need to analyze manufacturers' difficulties with the application of the HCD process and imposed standards. For each given category of HIT, we need to identify the fundamental usability dimensions and design principles likely to impact patient safety independently of workplace settings or organizations. These should be described in terms of usability flaws, corresponding usage problems experienced by users and related outcomes. This approach requires good quality and well structured reporting of Human Factors / Usability research studies on HIT.
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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 | 0.108 | 0.144 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.017 |
| Science and technology studies | 0.002 | 0.021 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.005 | 0.005 |
| 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; 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".