Applying health-six-sigma principles helps reducing the variability of length of stay in the emergency department
Bibliographic record
Abstract
BACKGROUND: Reducing length of stay (LOS) is one of the urgent problems in healthcare systems worldwide. Popular methods that are used to reduce LOS are the Lean and the 6 Sigma, which in practice result in limited improvements. In this paper, we introduce and test a tailored method for implementing the 6 Sigma principles in healthcare (we call H-6S). OBJECTIVE: To reduce the variability in the time between admission and discharge of patients in the emergency department. METHODS: The study took place within the emergency department (ED) of Josephtal Medical Center in Eilat, Israel. Our analysis focused on the processes of examining and treating patients from admission to ED until discharge home. The analysis was done during the second quarter of 2018. The implementation of the recommendations took place during Q3 2018. The reported results are from Q3 2018 to Q2 2019, compared to the corresponding period in 2017 (experienced team). RESULTS: In Q2 2017, LOS was 2.42 ± 2.07 h (experienced team, n = 9928). In Q2 2018, the LOS was 2.62 ± 7.04 h (before the H-6S, inexperienced team, n = 9484). In Q2 2019 following the intervention, it reached 2.3 ± 1.74 h (n = 7647). The differences between the standard deviations of the three periods are significant. CONCLUSION: Implementing H-6S dropped the variance of LOS within 3 months and remained low for the whole year. Each new team of physicians who enter the ED should be thoroughly instructed as to the routines and expectations of the system from them, which should narrow the differences of previous education between them.
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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 | 0.007 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".