Improving healthcare and operating room efficiency using lean six sigma
Bibliographic record
Abstract
Wait times and availability of care are major issues within Manitoba’s healthcare system and to improve in these areas a new method for improvement is needed. The purpose of this research is to prove the efficacy of using Lean Six Sigma in healthcare to generate improvements and to promote the usage of continuous improvement methodologies in the healthcare environment. To demonstrate the effectiveness of using Lean Six Sigma, a project was completed at St. Boniface Hospital to reduce overtime in operating rooms. Lean Six Sigma was used to assess the entire system and identify multiple areas for improvement, with case duration estimates being found to have the most potential for reducing overtime. This resulted in predictive models being created and tested against the current method of surgeon estimates. All models improved on the surgeon estimates (45-63% increase in on-time cases, reduction in overtime error by 49-59%, and 71-89% improvement in overtime to undertime error ratio) and it is recommended that a predictive modeling approach be used in the future. The Lean Six Sigma project was successful and also resulted in multiple additional beneficial outcomes: identification of other areas needing improvement, ranked by potential impact; process analysis and mapping which can be used in future projects; and identification of other causes for error in scheduling. In addition, if Lean Six Sigma had not been used, the project would have focused on a less impactful area—first case on-time starts. As Lean Six Sigma is a data-driven process, the impact of bias was removed and thus it was found that first case on-time starts were not as influential to overtime as assumed. From this research, it can be concluded that Lean Six Sigma can be effectively applied in even the most complex of hospital environments. It is recommended that hospitals consider implementing experienced teams to lead and train hospital employees in Lean Six Sigma or other continuous improvement methods.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".