Leaving the Hospital Against Medical Advice Among People Who Use Illicit Drugs: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Leaving the hospital against medical advice is an increasing problem in acute care settings and is associated with an array of negative health consequences that may lead to readmission for a worsened health outcome or mortality. Leaving the hospital against medical advice is particularly common among people who use illicit drugs (PWUD) and has been linked to a number of complex issues; however, few studies have focused specifically on this population beyond identifying them as being at an increased risk of leaving the hospital prematurely. Furthermore, programs and interventions for reducing the rate of leaving the hospital against medical advice among PWUD in acute care settings have not been well studied. OBJECTIVES: We systematically assessed the literature examining hospital discharge against medical advice from acute care among this population and identified potential methods to minimize the occurrence of this phenomenon. SEARCH METHODS: We searched 5 electronic databases (from database inception to August 2014) and article reference lists for articles investigating hospital discharge from acute care against medical advice among PWUD. Search terms consistent across databases included "patient discharge," "hospital discharge," "against medical advice," "drug user," "substance-related disorders," and "intravenous substance abuse." SELECTION CRITERIA: Studies were eligible for inclusion if they were published in a peer-reviewed journal as an original research article in English. We excluded gray literature, case reports, case series, reviews, and editorials. We retained original studies that reported illicit drug use as a predictor of leaving the hospital against medical advice and studies of discharge against medical advice that included PWUD as a population of interest, and we assessed significance through appropriate statistical tests. We excluded studies that reported patients leaving the hospital against medical advice from psychiatric hospitals, drug treatment centers and emergency departments, and studies that discussed misuse of alcohol but not illicit drugs. DATA COLLECTION AND ANALYSIS: We created an electronic database that included study abstracts and relevant information matching the keywords and search criteria. We reviewed potentially eligible articles independently by scanning the titles, abstracts, and full texts of articles after removing duplicates. We identified studies for which eligibility was unclear and decided which studies to include after thoroughly reviewing and discussing them. RESULTS: Of the 1649 studies that matched the search criteria, 17 met our inclusion criteria. Thirteen studies identified substance misuse as a significant predictor of leaving the hospital against medical advice. Three studies assessed the prevalence and predictors of leaving the hospital against medical advice among people who inject drugs and found that this phenomenon was commonly reported (prevalence range = 25%-30%). Factors positively associated with leaving the hospital against medical advice included recent injection drug use, Aboriginal ancestry, leaving on weekends and welfare check day. In-hospital methadone use, social support, older age, and admission to a community-based model of care were negatively associated with the outcome. CONCLUSIONS: To better understand risk factors associated with leaving the hospital against medical advice among PWUD, future research should consider the effect of individual, social, and structural characteristics on leaving the hospital against medical advice among PWUD. The development and evaluation of novel methods to address interventions to reduce the rate of leaving the hospital prematurely is necessary.
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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 | 0.007 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.015 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".