From Usability Testing to Clinical Simulations: Bringing Context into the Design and Evaluation of Usable and Safe Health Information Technologies. Contribution of the IMIA Human Factors Engineering for Healthcare Informatics Working Group.
Bibliographic record
Abstract
OBJECTIVES: The objective of this paper is to explore human factors approaches to understanding the use of health information technology (HIT) by extending usability engineering approaches to include analysis of the impact of clinical context through use of clinical simulations. METHODS: Methods discussed are considered on a continuum from traditional laboratory-based usability testing to clinical simulations. Clinical simulations can be conducted in a simulation laboratory and they can also be conducted in real-world settings. The clinical simulation approach attempts to bring the dimension of clinical context into stronger focus. This involves testing of systems with representative users doing representative tasks, in representative settings/environments. RESULTS: Application of methods where realistic clinical scenarios are used to drive the study of users interacting with systems under realistic conditions and settings can lead to identification of problems and issues with systems that may not be detected using traditional usability engineering methods. In conducting such studies, careful consideration is needed in creating ecologically valid test scenarios. The evidence obtained from such evaluation can be used to improve both the usability and safety of HIT. In addition, recent work has shown that clinical simulations, in particular those conducted in-situ, can lead to considerable benefits when compared to the costs of running such studies. CONCLUSION: In order to bring context of use into the testing of HIT, clinical simulation, involving observing representative users carrying out tasks in representative settings, holds considerable promise.
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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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".