Development of a discharge readiness report within the electronic health record—A discharge planning tool
Bibliographic record
Abstract
BACKGROUND: With increasingly complex pediatric inpatients, efficient and effective hospital discharge requires optimal interdisciplinary care coordination and communication. We describe the development of a discharge readiness report (DRR) for the electronic health record (EHR), an integrated summary of discharge-related information organized into a highly visible and easily accessible report. METHODS: We used interviews and process mapping to understand the roles of all disciplines involved in discharge planning and identified key drivers affecting our aim of designing a discharge tool in the EHR. Based on identified key drivers, we designed the DRR and made changes to the report using rapid improvement cycles. The final report includes information necessary for discharge planning organized into 4 domains: potential barriers to discharge, transitional care, home care, and discharge criteria. RESULTS: The DRR was activated in June 2012. As planned, the final product incorporated previously existing discharge-related information from within the EHR, organized into 1 report. Shortly after its introduction, the DRR was included in daily care coordination rounds (CCRs) for medical and medical subspecialty patients. End users found the report to be completely populated and accurate. We measured time to completion of CCRs and found no difference between duration of CCRs pre- and postuse of the DRR. CONCLUSIONS: Given widespread adoption, EHRs should be optimized to improve healthcare delivery. A discharge planning tool in the EHR may improve the efficiency and effectiveness of care transitions by allowing for proactive discharge planning and improved interdisciplinary communication.
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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 | 0.048 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".