Population-Based Research to Identify Higher Users of Acute Care Hospital Beds in Canada for Individualized Attention
Bibliographic record
Abstract
Background: The end of the COVID-19 pandemic left hospitals needing to address pandemic-delayed healthcare needs and increase hospital efficiency. Hospital patients who are frequently admitted or have long stays could potentially be helped to require less inpatient-based care. The purpose of this retrospective cross-sectional study was to identify and describe patients who are high hospital users for attention aimed at reducing their hospitalization needs or replacing some or all of their hospital stays with community-based options in a country where any person could be a high user, given that universal and accessible healthcare services are mandated in Canada. Methods: We obtained complete inpatient hospital data on all persons admitted to a Canadian hospital in the year immediately preceding the COVID-19 pandemic (April 1, 2019 to March 31, 2020). Four previously-developed high-use definitions (2+ hospitalizations, 30+ day hospital stays, 30+ total bed days, and the combination of 2+ admissions and 30+ total bed days) were explored using logistic regression for insight on high users of hospitals. Results: Of 1.88 million individuals admitted to a hospital in 2019–20, 21.3% were admitted 2+ times, 3.6% had 30+ day hospital stays, 7.1% spent 30+ cumulative days in hospital, and 5% had both 2+ hospitalizations and 30+ cumulative bed days. Each high-use definition identified different sets of people, although multi-admission patients were more commonly younger (<65) and patients with 30+ bed days were more often over age 65. While almost any patient could be a high user, common characteristics across younger and older patients who were classified variably as high users were ambulance arrival, unplanned admission to a hospital through an emergency room, having a diagnosis consistent with 1 of 4 diagnostic categories (circulatory diseases, factors influencing health status and contact with health services, mental and behavioral disorders, and respiratory diseases), and dying in hospital. Conclusions: People with high-use patterns could have services designed to reduce their need for and use of inpatient-based care. Hospital beds becoming available for others to use is one of many possible benefits if high use became a focus of efficiency attention.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".