Engineering the Electronic Health Record for Safety: A Multi-Level Video-Based Approach to Diagnosing and Preventing Technology-Induced Error Arising from Usability Problems
Bibliographic record
Abstract
Electronic health records (EHRs) promise to improve and streamline healthcare through electronic entry and retrieval of patient data. Furthermore, based on a number of studies showing their positive benefits, they promise to reduce medical error and make healthcare safer. However, a growing body of literature has clearly documented that if EHRS are not designed properly and with usability as an important goal in their design, rather than reducing error, EHR deployment has the potential to actually increase medical error. In this paper we describe our approach to engineering (and reengineering) EHRs in order to increase their beneficial potential while at the same time improving their safety. The approach described in this paper involves an integration of the methods of usability analysis with video analysis of end users interacting with EHR systems and extends the evaluation of the usability of EHRs to include the assessment of the impact of these systems on work practices. Using clinical simulations, we analyze human-computer interaction in real healthcare settings (in a portable, low-cost and high fidelity manner) and include both artificial and naturalistic data collection to identify potential usability problems and sources of technology-induced error prior to widespread system release. Two case studies where the methods we have developed and refined have been applied at different levels of user-computer interaction are described.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".