Human Factors for More Usable and Safer Health Information Technology: Where Are We Now and Where do We Go from Here?
Bibliographic record
Abstract
A wide range of human factors approaches have been developed and adapted to healthcare for detecting and mitigating negative unexpected consequences associated with technology in healthcare (i.e. technology-induced errors). However, greater knowledge and wider dissemination of human factors methods is needed to ensure more usable and safer health information technology (IT) systems. OBJECTIVE: This paper reports on work done by the IMIA Human Factors Working Group and discusses some successful approaches that have been applied in using human factors to mitigate negative unintended consequences of health IT. The paper addresses challenges in bringing human factors approaches into mainstream health IT development. RESULTS: A framework for bringing human factors into the improvement of health IT is described that involves a multi-layered systematic approach to detecting technology-induced errors at all stages of a IT system development life cycle (SDLC). Such an approach has been shown to be needed and can lead to reduced risks associated with the release of health IT systems into live use with mitigation of risks of negative unintended consequences. CONCLUSION: Negative unintended consequences of the introduction of IT into healthcare (i.e. potential for technology-induced errors) continue to be reported. It is concluded that methods and approaches from the human factors and usability engineering literatures need to be more widely applied, both in the vendor community and in local and regional hospital and healthcare settings. This will require greater efforts at dissemination and knowledge translation, as well as greater interaction between the academic and vendor communities.
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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 | 0.079 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.022 | 0.017 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.016 | 0.005 |
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".