Patient Process Mapping at the Emergency Department in Humber River Hospital: A Case report
Bibliographic record
Abstract
Introduction: Benchmarking, a powerful management approach for implementing excellent practices at the best cost and quality, is a recent concept in the healthcare system. Aim: The ultimate goal of this research project was to describe and map patient care-flow process at the Emergency Department (ED) in the Humber River Hospital (HRH) as a benchmark and the first full digital hospital in Canada. The motivation of the researcher to select the ED as a research territory was the existence of a massive model and benchmark ED with four zones. Methods: This study was a cross-sectional, case report study. The population under the study was the staff who worked in the ED and were willing to participate in the research study. Informed written consent was obtained from the participants in the study. Several interviews were done to approve the validity of the questions with care providers that were co-investigators. Then, Staff in the ED were interviewed to get an understanding of the terminology and classifications used in the ED. Results: The hospital was designed and built on three core principles; Lean, Green and Digital. It uses the best possible technology to support hospital delivery, such as dynamic and smart glass, Ascom phone (connects to Humber Information System and Electronic Medical Record), smart bed technology; robotic technology for certain surgical procedures; automated laboratory processing; automated guided vehicles that deliver medical supplies; and bedside computer screens that allow the patients to control their environments.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".