Economic evaluation of clinical quality registries: a systematic review
Bibliographic record
Abstract
OBJECTIVES: The objective of this systematic review was to examine the existing evidence base for the cost-effectiveness or cost-benefit of clinical quality registries (CQRs). DESIGN: Systematic review and narrative synthesis. DATA SOURCES: Nine electronic bibliographic databases, including MEDLINE, EMBASE and CENTRAL, in the period from January 2000 to August 2019. ELIGIBILITY CRITERIA: Any peer-reviewed published study or grey literature in English which had reported on an economic evaluation of one or more CQRs. DATA EXTRACTION AND SYNTHESIS: Data were screened, extracted and appraised by two independent reviewers. A narrative synthesis was performed around key attributes of each CQR and on key patient outcomes or changes to healthcare processes or utilisation. A narrative synthesis of the cost-effectiveness associated with CQRs was also conducted. The primary outcome was cost-effectiveness, in terms of the estimated incremental cost-effectiveness ratio (ICER), cost savings or return-on-investment (ROI) attributed to CQR implementation. RESULTS: Three studies and one government report met the inclusion criteria for the review. A study of the National Surgical Quality Improvement Programme (NSQIP) in the USA found that the cost-effectiveness of this registry improved over time, based on an ICER of US$8312 per postoperative event avoided. A separate study in Canada estimated the ROI to be US$3.43 per US$1.00 invested in the NSQIP. An evaluation of a post-splenectomy CQR in Australia estimated that registry cost-effectiveness improved from US$234 329 to US$18 358 per life year gained when considering the benefits accrued over the lifetime of the population. The government report evaluating five Australian CQRs estimated an overall return of 1.6-5.5 times the cost of investment. CONCLUSIONS: Available data indicate that CQRs can be cost-effective and can lead to significant returns on investment. It is clear that further studies that evaluate the economic and clinical impacts of CQRs are necessary. PROSPERO REGISTRATION NUMBER: CRD42018116807.
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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 | 0.079 | 0.329 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.015 | 0.016 |
| Bibliometrics | 0.017 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".