Examining the relationship between clinical practice guidelines and hospital efficiency
Bibliographic record
Abstract
Efficiency of care is an important topic in Canadian health care. One common measure of efficiency is length of stay (LOS) (Murphy Noetscher, 1999; Needham et al., 2003; Brownell Roos, 1995). Clinical practice guidelines (CPGs)---"systematically developed statements to assist practitioner and patient decisions about appropriate health care for specific clinical circumstances" (Field Lohr, 1990 p.38)---are an intervention used to reduce LOS. The current research addresses the question, what is the overall relationship between the use of CPGs and hospital efficiency, as measured by LOS?This thesis includes three chapters that explore this relationship. Chapter 2 discusses a systematic review that the researcher conducted to test the nature of the CPG-LOS relationship. One hundred seventy-three studies were included in the review and encompassed a wide array of disease states. The review found a statistically significant association between the use of CPGs and reduced LOS. However, the quality rating of the studies found that, for the most part, they had limitations, regardless of year of publication. A limited number of the studies included were conducted on more than one CPG, in more than one hospital, or in Canada. Therefore, Chapter 3 of this thesis adds to the literature by addressing all those issues. Chapter 3 discusses a secondary data analysis including data on more than 80 hospitals in numerous medical and surgical clinical areas. Only two significant relationships were discovered, one involving pneumonia and one involving prostatectomy. On the whole, there were no significant differences between CPG usage or efficiency over the two-year period. Possible reasons for these results are provided in the discussion. Considering the contradictory results found in Chapters 2 and 3, the researcher conducted qualitative interviews in a sample of Ontario hospitals to explore factors that could influence the relationship between the use of CPGs and LOS. Nine interviews were conducted, and five main factors---outlined in Chapter 4---were found, including the purpose of the implementation and the clinicians' response. Finally, Chapter 5 discusses the findings of the three research chapters, develops potential implications, and provides directions for future research.
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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 | 0.076 | 0.421 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.007 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".