Human factors and systems simulation methods to optimize peri-operative EHR design and implementation
Bibliographic record
Abstract
The increase in adoption of Electronic Health records (EHR) in healthcare can be overwhelming to users and pose hidden safety threats and inefficiencies if the system is not well aligned with workflows. This quality improvement study, facilitated from September 2023-April 2024, aimed to proactively test a new EHR using systems focused simulation and Human factors methods, prior to go-live, in a peri-operative children's hospital setting to improve safety, efficiency and usability of the EHR. The project was conducted at a large, academic, quaternary care children's hospital undergoing a transition from one EHR to another. Two cycles of usability testing followed by in situ simulations focused on testing the new EHR with interprofessional peri-operative team members prior to go live. Usability testing, using relevant clinical workflows, was completed over zoom using the EHR "testing" environment with individual care providers across multiple peri-operative roles. In situ simulations were facilitated in the actual peri-operative and Otolaryngology clinic spaces with full interprofessional teams. Qualitative data was collected and summarized through debriefing and recordings of the sessions. Human factors and patient safety principles were integrated throughout the recommendations. A total of 475 recommendations were made to improve the safety, efficiency, usability, and optimization of the EHR. The outcomes included a range of usability and system issues including latent safety threats and their impact on safe and quality patient care. There was a plethora of usability improvements, including some critical issues that were uncovered and mitigated prior to the go live date.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".