To Publish or Not: Experience and Evidence About Publishing Hospital Outcomes Data
Bibliographic record
Abstract
Since the mid-1980s, the publication of hospital outcomes data has become increasingly popular. Canada and the US operate performance reporting systems, with similar initiatives planned in Australia and New Zealand (Mannion and Davies, 2002). In Europe, outcomes data are collected and published in the UK, Italy, Scandinavia and the Netherlands (Marshall and Brook, 2002). Whilst political and regulatory attitudes differ across countries, the economic motivations are often similar. Concern with escalating health care costs and regional variations in the quality of care has led countries to examine ways to improve value for money from their health systems, one of which is to place key data in the public domain (Marshall et al., 2003, Davies and Lampel, 1998, Fottler et al., 1987). Publication also forms part of a framework of accountability, along with regulation such as audit, accreditation, licensing and inspections; market (or quasi market) forces; and legislation (Davies, 1999). More broadly, publication is a means of realising the key political and cultural objectives of transparency and openness - publication aims to promote – or restore – public trust (Marshall et al., 2000a, Davies and Shields, 1999). A related motivation is that the public places ever more emphasis on a 'right to know' about goods and services generally, a right that is recognised in the Freedom of Information Act 2000. Thus, the availability of information is also valued for its own sake, whether or not this changes behaviour. The aims of this report are to describe and discuss particular systems of publication of hospital outcomes data in some detail, to present a critique of what these systems, why they were set up and how they operate, and to explore the potential implications for policy and practice in the UK health care system.
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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 | 0.298 | 0.751 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.015 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.032 | 0.037 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.008 | 0.012 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".