High-Users of Acute Care in a Teaching Hospital: A Retrospective Chart Review and Survey of Primary Care Physicians
Bibliographic record
Abstract
Purpose To characterize high-users (HUs) of inpatient units, obtain insights from their primary care physicians (PCPs) and identify factors that can be modified to reduce resource use. Method The study design included retrospective chart reviews of high-user patients and qualitative surveys of their PCPs. HUs were defined as adults with 3 or more admissions to an index tertiary teaching hospital in Edmonton as well as a cumulative length of stay (cLOS) greater than 30 days at any hospital in the province of Alberta, between September 1, 2015 and September 30, 2016. The charts of HUs were reviewed to assess demographics, admitting and consulting services, medical profile, social profile, community supports, and scores on pre-existing risk-stratification tools to identify patient factors that might be characteristic of HUs. Additionally, a survey comprising 12 multiple-choice and 8 short-answer questions was faxed to their PCPs to assess HU attitudes and behaviors and collect recommendations to prevent high use of acute care. Results Of 125 HUs (median 62 years old, 5 admissions, cLOS 49 days, 14 emergency department (ED) visits, 10 medications), 74% lived at home, 86% had a PCP, 56% received homecare pre-admission and 34% had at least one critical care admission. HUs accounted for 2474 admissions or ED visits (median 14, IQR 10-22) at all sites in the year studied; 41% of their 1605 ED visits and 21% of their 869 admissions were at other hospitals. Their most prevalent comorbidities were hypertension, depression, and diabetes. 49 responses were received to 114 faxed surveys (43% response rate). Only 14 of 49 responding PCPs suggested interventions to address ED revisits and readmissions; PCPs most frequently cited living conditions and lack of social supports as key causative factors. Conclusions We have characterized high-user patients and discussed PCP perspectives and strategies to optimize their healthcare use.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".