Assessing 30-day avoidable readmission rates: Is it an appropriate tool to manage emergency department quality of care?
Bibliographic record
Abstract
Objective: Quality indicators, based on administrative data, are being increasingly used to assess avoidable hospital readmission rates. Their potential to identify areas for improvement at low cost is attractive, but their performance in emergency departments (EDs) has been criticised.Methods: Hospital readmissions were categorised as potentially avoidable or non-avoidable, by a computerised algorithm (SQLape®, version 2016 - Striving for Quality Level and analysing of patient expenditures). Half-yearly rates were reported between July 2015 and June 2016. Two senior physicians conducted a medical record review on 100 randomly selected cases from an ED, flagged as potentially avoidable readmissions (PAR). Results were then discussed with the algorithm’s designer.Results: The algorithm screened 2,182 eligible emergency visits - 105 cases (4.8%), were deemed potentially avoidable by the algorithm. Among 100 randomly selected cases, nine exclusions were due to coding issues and four due to false positives. Overall (N = 87), 20/87 (23%) of readmissions were directly related to sole emergency care, 31/87 (36%) related to healthcare providers other than the ED, and 23/87 (26%) were of mixed provision, while 13/87 (15%) were attributed to the course of the disease.Conclusions: The study confirms the need for a better understanding of the algorithm’s measurement and of its reported results. Careful interpretation is required before a sound conclusion can be made. Indeed, it is apparent that the 30-day PAR quality indicator rate reflects a wider parameter of care than hospitals alone, who understandably tend to concentrate on their own, direct liability of care. In particular the 30-day PAR quality indicator is not well-suited to evaluate ED performance.
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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 | 0.051 | 0.183 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".