Applying Human Factors Principles to Mitigate Usability Issues Related to Embedded Assumptions in Health Information Technology Design
Bibliographic record
Abstract
BACKGROUND: There is growing recognition that design flaws in health information technology (HIT) lead to increased cognitive work, impact workflows, and produce other undesirable user experiences that contribute to usability issues and, in some cases, patient harm. These usability issues may in turn contribute to HIT utilization disparities and patient safety concerns, particularly among "non-typical" HIT users and their health care providers. Health care disparities are associated with poor health outcomes, premature death, and increased health care costs. HIT has the potential to reduce these disparate outcomes. In the computer science field, it has long been recognized that embedded cultural assumptions can reduce the usability, usefulness, and safety of HIT systems for populations whose characteristics differ from "stereotypical" users. Among these non-typical users, inappropriate embedded design assumptions may contribute to health care disparities. It is unclear how to address potentially inappropriate embedded HIT design assumptions once detected. OBJECTIVE: The objective of this paper is to explain HIT universal design principles derived from the human factors engineering literature that can help to overcome potential usability and/or patient safety issues that are associated with unrecognized, embedded assumptions about cultural groups when designing HIT systems. METHODS: Existing best practices, guidance, and standards in software usability and accessibility were subjected to a 5-step expert review process to identify and summarize those best practices, guidance, and standards that could help identify and/or address embedded design assumptions in HIT that could negatively impact patient safety, particularly for non-majority HIT user populations. An iterative consensus-based process was then used to derive evidence-based design principles from the data to address potentially inappropriate embedded cultural assumptions. RESULTS: Design principles that may help identify and address embedded HIT design assumptions are available in the existing literature. CONCLUSIONS: Evidence-based HIT design principles derived from existing human factors and informatics literature can help HIT developers identify and address embedded cultural assumptions that may underlie HIT-associated usability and patient safety concerns as well as health care disparities.
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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 | 0.144 | 0.176 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.006 | 0.017 |
| Scholarly communication | 0.014 | 0.007 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.004 | 0.008 |
| 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".