Applying Human Factors and Systems Simulation Methods to Inform a Multimillion-Dollar Healthcare Decision
Bibliographic record
Abstract
PURPOSE: This article describes a case study of a collaborative human factors (HF) and systems-focused simulation (SFS) project to evaluate potential patient and staff safety risks associated with a multimillion-dollar design and construction decision. BACKGROUND: The combined integration of HF and SFS methods in healthcare related to testing and informing the design of new environments and processes is underutilized. Few realize the effectiveness of this integration in healthcare to reduce risk and improve decision-making, safety, design, efficiency, patient experience, and outcomes. This project showcases how the combined use of HF and SFS methods can provide objective evidence to help inform decisions. METHODS: The project was initiated by a healthcare executive team looking for an objective, user informed analysis of a current connector passageway between two existing buildings. The goal was to understand the implications of keeping the current route for simultaneous use for public and patients service flow versus building and financing a new passageway for separate flow and transport. An interprofessional team of intensive care unit professionals participated in two simulations designed to test the current connector. A failure mode and effects analysis and qualitative debrief feedback was used to evaluate risks and potential failures. RESULTS: The evaluation resulted in data that enabled informed executive decision making for the most effective, efficient, and safest option for public, staff, and patient transport between two buildings. This evaluation resulted in the decision to go forward with building a multimillion-dollar new connector passageway to improve integrated care and transport.
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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 | 0.033 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 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".