Development of a quality scoring tool to assess quality of discharge summaries
Bibliographic record
Abstract
INTRODUCTION: Timely, precise, and relevant communication between hospital-based clinicians and primary care physicians post-discharge (DC) ensures quality transitions, thereby reducing patient safety incidents and preventing readmission. At the present time there is limited knowledge of elements of quality or methods to score the quality criteria in the context of DC summaries. The Nova Scotia Health Authority, a provincial health system responsible for the delivery of services in a small Canadian province, embarked on a system-level approach to the standardization of DC summaries in an effort to improve quality and safety at care transitions from hospital to primary care. MATERIALS AND METHODS: A comprehensive literature review to retrieve items relevant to quality in DC summaries, retrospective audit of charts, a consensus development process, and, finally, validation of a scoring tool were conducted in order to develop a quality scoring tool for DC summaries. RESULTS: Relevant items were identified through the literature review and consensus development process. Corresponding definitions that were established assisted the development of the quality criteria, which were subsequently used to score the quality of DC summaries in our organization. CONCLUSION: The scoring tool developed through this work will be applied to help us gain a more in-depth understanding of quality in DC summaries and support the development of suitable education and quality processes in the health authority that can best support safe care transitions for patients.
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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 | 0.114 | 0.216 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.025 | 0.013 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".