Abstract 206: Aiming to Improve Stroke Care Continuity with Primary Care Follow-up Appointments Scheduled Prior to Hospital Discharge
Bibliographic record
Abstract
Background: Follow-up care can facilitate patient recovery. To improve care continuity the Ohio Coverdell Stroke Program aimed to improve the proportion of hospitalized stroke patients for whom prior to discharge the hospital scheduled a follow-up appointment with primary care. This is not yet standard of care for stroke. Methods: Of 48 Ohio Coverdell Stroke Program participating hospitals, 37 had data entered into the registry for each quarter of the 12 month quality improvement initiative. Admitted stroke patients of any type planned for discharge home were included. Patients younger than 18 years of age, not admitted to the hospital from the emergency department, or admitted and only receiving comfort care were excluded. Patient-level and hospital-level data were examined to determine performance in a 6 month baseline period (quarters 1-2) and a 6 month active improvement phase (quarters 3 and 4). Patients discharged with and without an appointment scheduled were compared to identify targeted areas for continued improvement. Results: There were 4,558 stroke patients discharged home over 12 months from 37 geographically distributed hospitals (62.4% ischemic stroke, 29.0% TIA, 8.5% hemorrhagic stroke). At baseline, 13.4% of patients had an appointment scheduled with a primary care provider prior to discharge home. This increased to 18.7% in quarter 3 and 26.6% in quarter 4, representing a 98.5% improvement from baseline to quarter 4 in the proportion of patients with a primary care follow-up appointment scheduled. Median quarter 4 performance at a hospital level was 10.5% (distributed from 0-80.0% across 37 hospitals). Analysis of patient characteristics showed differences in the proportion of patients with an appointment by age (15.5% for ages>65 years vs. 18.5% of younger patients, p=0.004); race (15.1% white race vs. 22.1% of African Americans, p<0.001); history of prior stroke (19.0% with prior stroke vs. 16.3% with no prior stroke, p=0.031); comorbid diabetes (18.6% with diabetes vs. 16.1% without, p=0.021); dyslipidemia (18.2% with vs. 15.6% without, p=0.013); and obesity (14.4% were obese/overweight vs. 17.7% who were not overweight/obese, p=0.007). No significant patient level differences were found by patient sex, insurance, stroke type, stroke severity, prior myocardial infarction or coronary artery disease, prior TIA, family history of stroke, or comorbid atrial fibrillation, heart failure, or hypertension. Conclusions: Hospital performance with scheduling primary care follow-up appointments improved significantly; however, only 1 in 4 patients had an appointment scheduled prior to discharge. Case study analysis of missed opportunities may help identify barriers and facilitators associated with access, availability, and awareness that can be addressed in future improvement cycles.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".