Post discharge issues identified by a call-back program: identifying improvement opportunities
Bibliographic record
Abstract
Objectives: The period following discharge from the hospital is one of heightened vulnerability. Discharge instructions serve as a guide during this transition. Yet, clinicians receive little feedback on the quality of this document that ties into the patients’ experience. We reviewed the issues voiced by discharged patients via a call-back program and compared them to the discharge instructions they had received. Methods: At our institution, patients receive an automated call forty-eight hours following discharge inquiring about progress. If indicated by the response to the call, they are directed to a nurse who assists with problem solving. We reviewed the nursing documentation of these encounters for a period of nine months. The issues voiced were grouped into five categories: communication, medications, durable medical equipment/therapies, follow up and new or ongoing symptoms. The discharge instructions given to each patient were reviewed. We retrieved data on the number of discharges from each specialty from the hospital over the same period. Results: A total of 592 patients voiced 685 issues. The numbers of patients discharged from medical or surgical services identified as having issues via the call-back line paralleled the proportions discharged from medical and surgical services from the hospital during the same period. Nearly a quarter of the issues discussed had been addressed in the discharge instructions. The most common category of issues was related to communication deficits including missing or incomplete information which made it difficult for the patient to enact or understand the plan of care. Medication prescription related issues were the next most common. Resource barriers and questions surrounding medications were often unaddressed. Conclusions: Post discharge issues affect patients discharged from all services equally. Data from call back programs may provide actionable targets for improvement, identify the inpatient team’s ‘blind spots’ and be used to provide feedback to clinicians.
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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 | 0.007 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".