Identification of EMR Hardware and Space Design Requirements using Human Factors Analyses
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
Electronic Medical Records (EMR) are being implemented globally in the hope of improving patient care, provider coordination, documentation accuracy, and information availability. Numerous factors impact successful EMR implementation including usability, accessibility and unique characteristics of the sociotechnical system within which it will be used. This paper describes the application of human factors methods to support effective EMR implementation at one pediatric hospital. The focus is on the problem of hardware selection and placement – a topic that has not received much attention in the literature to date. The requirements gathering process for two outpatient clinics included a task and gap analysis of current clinic workflows that led to the identification of specific hardware and design recommendations supporting future EMR workflows. Lessons learned post-implementation and requirements associated with hospital wide practices were extrapolated to generate guiding principles that apply to EMR implementation in other outpatient clinics.
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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 | 0.001 | 0.000 |
| 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 it