Discharges against medical advice: a community hospital's experience.
Bibliographic record
Abstract
OBJECTIVES: To understand the characteristics of patients who leave hospital against medical advice (known as "discharges against medical advice" [DAMA]) in a small community hospital and to study how these patients compare to current literature on the topic. To evaluate chart documentation pertaining to such discharges. METHODS: A retrospective chart audit was performed, covering a 2-year period, on patients who had discharged themselves against medical advice. The data were compared to the general patient population of the same period. Evaluation of DAMA documentation was also conducted by chart survey. RESULTS: The rate of DAMA in the study hospital was found to be 0.57%, and the average length of stay was 2.8 days. Patients who leave hospital against medical advice differ from the general patient population: they include a higher proportion of males (p = 0.007), demonstrate a different age distribution (p < 0.001), have shorter stays in hospital (p < 0.001), and have a considerably greater frequency of substance abuse (p < 0.001) and psychiatric conditions (p < 0.001) associated with their admissions. DAMA documentation was included in the charts of 81.6% of patients involved, but only 22.9% of these charts included documentation with respect to patient competency. CONCLUSION: Patients who leave hospital against medical advice represent a high-risk population: they suffer a greater incidence of mental illness and substance abuse. Potential interventions are limited, but influence strategies may have a role. Early identification of patients at risk may facilitate this process, thereby decreasing the occurrence of DAMA and improving health outcomes. More consistent and comprehensive documentation is needed for these patients.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".