A Review of Measuring the Cognitive Workload of Electronic Health Records
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The To Err Is Human report stated that 98 000 patients die yearly because of medical errors, and that medication errors kill more people than workplace injuries. The inadequate design and utilization of the electronic health record have been identified as major contributing factors to medical errors. Increased cognitive workload of clinicians has consistently been linked to the occurrence of medical errors. The purpose of this article was to synthesize the current state of the science on measuring clinicians' cognitive workload associated with using electronic health records in order to inform evidence-based guidelines. The major considerations identified in the literature involve the use of psychometric instruments, using efficiency as a proxy for cognitive workload, and eye tracking. The National Aeronautics and Space Administration Task Load Index was the most used psychometric instrument, but reliability measures were not reported. It is important to evaluate reliability of psychometric instruments because the consistency of the instrument can change when administered to different populations. Efficiency is an observable measure defined by the total time to complete a task and the total number of physical interactions with the user interface. Efficiency can allow the use of statistical modeling, but it does not directly evaluate the mental activity associated with using an electronic health record interface. Eye tracking has been used extensively in the literature to measure cognitive workload via changes in pupil size related to mental activity, but it is not often used to measure the cognitive workload associated with using the electronic health record. Eye tracking is very useful for continuous monitoring of cognitive workload.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it