Unravelling the drivers of repeat hospital visits: Insights from internal medicine emergency department discharges and short-stay admissions
Bibliographic record
Abstract
Introduction: Hospital readmissions are an important indicator of health care quality, but the rate and causes of hospital revisits among short-stay internal medicine (IM) patients has been understudied. In this study, we aimed to identify predictors of hospital revisits in two cohorts of patients: those seen by IM and discharged home without admission (DHWA) and those with IM admissions of less than 24 hours. Methods: We conducted a two-phase retrospective study of patients DHWA and with short IM admissions between 2017 and 2022. The outcomes of interest were the rate and predictors of a hospital revisit within 14 days of discharge. The associations of revisits with demographics, comorbidity, and other health care-related variables were analyzed and rational subgrouping was used to identify drivers of revisits among a purposely sampled subgroup of patients with a 14-day revisit. Results: A total of 2,874 hospitalizations were analyzed and 135 chart reviews conducted. The hospital revisit rate was lower in the short-admission group (18.2% versus 26.4%, P <.001). Applying rational subgrouping, we grouped patients by symptom progression, along with frailty and residence status. Patients living with dementia re-presented with stable symptoms, and patients with inadequate follow-up often had frailty indicators and/or heart failure and respiratory diseases. Discussion: This study demonstrates how a classical approach to risk factor analysis failed to lead to actionable root causes of hospital revisits in medically complex IM patients. Applying rational subgrouping helped understand deeper drivers of revisits, findings which can empower internists to make more informed discharge decisions and support the design of future quality improvement strategies.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".