Using report cards and dashboards to drive quality improvement: lessons learnt and lessons still to learn
Bibliographic record
Abstract
More than 50 years of health services research has driven home a core lesson: unintended and inappropriate variations in care are common.1 2 Identification of such variation in obstetrics was the impetus for Archie Cochrane to start his work.3 In this issue of BMJ Quality & Safety , Weiss and colleagues report an intervention developed to address inappropriate variation in aspects of maternal newborn care across Ontario, Canada’s most populous province.4 The intervention involved systematic collection and analysis of administrative data to assess key quality indicators for all hospital births in the province and provision of this data in a ‘dashboard’ back to hospitals. Measuring quality of care and comparing this against agreed-upon standards of practice or peer performance (ie, audit) and delivery of the results to healthcare professionals and/or administrators (ie, feedback) is a common quality improvement strategy.5 Whether referred to as ‘audit and feedback’, ‘report cards’, ‘benchmarking’, ‘practice profiles’ or other synonyms, the underlying rationale for audit and feedback is sound. The large literature evaluating this approach indicates that (1) clinicians are relatively poor at self-assessment,6 meaning that they tend to pursue continuing professional development or quality improvement in areas of interest (where performance is often already high) rather than areas of greatest need; (2) comparing current performance to a target can drive increased performance in motivated individuals,7–9 meaning that when desired behaviours can be measured and presented in a formative fashion,10 health professionals may respond positively to them; and (3) high-performing health systems tend to feature audit and feedback as an evidence-based, scalable and relatively inexpensive strategy to encourage uptake of best practices.11 The use of dashboards to encourage reflection on quality of care is expanding. In 2009, the National Health Service adopted a maternity dashboard; several countries and institutions …
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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 | 0.061 | 0.145 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.008 | 0.006 |
| Research integrity | 0.007 | 0.017 |
| Insufficient payload (model declined to judge) | 0.011 | 0.005 |
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".