Abstract WP489: Evaluation of Post-discharge Callback Data from Stroke Patients and Caregivers
Bibliographic record
Abstract
Background and Purpose: The early period after hospital discharge is a critical time for stroke patients during which transitions of care can be optimized. In our Comprehensive Stroke Center, patients are called within 3 days of discharge to conduct point of service feedback utilizing yes/no and open-ended questions related to the hospital stay, discharge instructions, follow-up care, and prescriptions. We sought to examine post-discharge feedback to identify areas of need. Methods: A multidisciplinary team collaborated to classify callback responses for patients discharged from 1/1/2018 to 6/30/2018 within the following domains from the Hospital Consumer Assessment of Healthcare Providers and Systems survey: care from doctors, care from nurses, hospital environment, experience in the hospital, and transitions of care. We provide a descriptive analysis (Table 1). Results: Among 700 patients discharged in the study period, 378 (54%) were discharged home and 207 (55%) of these were contacted for feedback. Eighty four (40.5%) of patients/caregivers expressed at least one concern (129 total), with the largest proportion in the transitions of care domain (67.4 %). Patients reported difficulties with prescriptions (15.5%), obtaining outpatient therapy services (13.2%) and follow-up appointments (10.9%), new or persistent clinical symptoms (8.5%), and insufficient hospital discharge education (5.4%). Approximately 5% (11/207) of all patients reported hospital readmission during the call. Conclusions: This study reveals that stroke patients and caregivers identify transitional care as an area for improvement following discharge from a CSC. Interventions aimed at facilitating care from hospital to home after stroke are warranted, and we are implementing patient-centered initiatives to enhance the discharge process and provide additional support early after stroke discharge.
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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 | 0.010 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".