Practice guidelines, did they reduce medical practice variation?
Bibliographic record
Abstract
Background: A persistent finding in health services research is that medical practice varies widely between small areas. When clinical variables and patient characteristics are taken into account, residual variation still remains. The existence of this variation is often interpreted as a sign of overuse of health care resources, endangering the financing of health care. Growing awareness of variation and the development of clinical guidelines both took place in the 1970s in the USA and Canada. Concerns about variation ask for rationalizing medical practice. Variation therefore is one of the reasons for developing guidelines. The use of clinical guidelines, that provide recommendations about appropriate health care, is a way of reducing variation and maintaining or improving the quality of health care. In Europe the development of clinical guidelines started in the 1980s. The Netherlands are a precursor in the development of clinical guidelines, compared to other European countries. The Dutch College of General Practitioners (NHG) has published guidelines since 1989. Aim: The aim of the study is to examine whether practice guidelines influence medical practice variation. Two questions are asked, first: is variation in prescription, referral, and treatment reduced after the introduction of guidelines? Second: are there differences in the reduction of variation between types of practice? Based on the assumption of the importance of social influences on behavioural change, variation between group practices is further reduced than variation between inglehanded practices. Methods: We used data from two Dutch National Surveys of General Practice conducted in 1987-1988 and 2000-2002 respectively (DNSGP-1 and DNSGP-2). DNSGP-1 data were gathered before the introduction of guidelines for GPs in the Netherlands, while DNSGP-2 data were gathered after the introduction. The studies included medical record based data about 335,000 and 390,000 patients respectively, listed with approximately 100 practices. Two groups of diagnoses were made: one for which guidelines were developed and one for which no guidelines were developed. Variation in prescription, treatment and referral for the two groups were compared between DNSGP-1 and DNSGP-2 using multilevel analysis. Results: First results show that total variation in general practice, irrespective of guidelines, did not decrease. Considerable variations have been observed. It is now examined whether variation did decrease for diagnoses for which guidelines were developed. Furthermore, the effect of practice type will be studied. Conclusion: As yet, no statistical evidence has been found that total variation has decreased in the past ten years. However, a difference between the two groups, diagnoses with and without guidelines, was not made in these preliminary analyses. It might turn out that guidelines cause variation to decrease, as they are supposed to. Knowledge on differences in the reduction of variation for instance related to type of practice should be used in the development of effective\nimplementation strategies. (aut.ref.)
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 | 0.048 | 0.334 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".