Framework for Patient Flow Improvement
Bibliographic record
Abstract
There has been much research where the flow of patients was improved, but most of this study is case-specific and only a few papers offer guidelines for patient flow analysis and improvement. In this study a general framework for the analysis and improvement of patient flow is presented, based on a literature review and on experience from a case study in a hospital in Mexico dealing with identifying improvement opportunities that reduced waiting times in the obstetrics/gynecology area of the emergency department. The framework involves an initial analysis using basic tools followed by the selection of a strategy based on system complexity; financial investment required and team participation. The alternative strategies considered were use of advanced analysis tools; use of kaizen events; or direct recommendations. The aim of the framework is to serve as guideline in patient flow improvement projects by helping select the most appropriate improvement path, resulting in project success.
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 | 0.015 | 0.010 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".