Discharge against medical advice (AMA), in which a patient chooses to leave the hospital before the treating physician recommends discharge, continues to be a common and vexing problem. This article reviews the prevalence, costs, predictors, and potential interventions for this clinical problem. Between 1% and 2% of all medical admissions result in an AMA discharge. Predictors of AMA discharge, based primarily on retrospective cohort studies, tended to be younger age, Medicaid or no insurance, male sex, and current or a history of substance or alcohol abuse. Interventions to reduce the rate of AMA discharges have not been systematically studied. This article offers suggestions for interventions based on studies in other areas of clinical care as well as the psychiatric AMA discharge literature. Studies for this review were identified by searching the relevant MeSH heading (discharge) and key words (against medical advice, leave, elope, hospital, and self-discharge) in PubMed databases and selecting all English-language articles from 1970 through 2008 that included data on adult medical inpatients.

Factors shown to have an adverse affect on opioid prescribing disproportionately impact on the attitudes of internists compared with geriatricians. Further research is needed to determine if there is also a differential impact on how internists care for their elderly patients with chronic pain.

Hospital discharges against medical advice (AMA) are common, costly, stigmatizing to patients, and are associated with excess morbidity and mortality. Achieving better quality care for patients discharged AMA has been limited both by the sparse research illuminating how best to care for this challenging patient population, as well as a lack of standards regarding this clinical practice. This paper will review elements of the AMA literature and highlight the gaps, including the predictors of AMA discharge, challenges to high quality informed consent in AMA discharges, problematic aspects of AMA discharge forms, and the stigma associated with patients discharged AMA. These gaps in the evidence base collectively limit the ability to adequately and completely address AMA discharges and improve health care quality. This paper will recommend future directions to answer remaining questions for the field, and offer guidance for providing ethically sound and high quality care for the affected population. Applying the widely accepted principles of patientcentered care and shared decision making to AMA discharges offers the opportunity to improve quality of care and promote ethical health care practice.KEY WORDS: against medical advice; shared decision making; patientcentered care.

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The "Things We Do for No Reason" (TWDFNR) series reviews practices which have become common parts of hospital care but which may provide little value to our patients. Practices reviewed in the TWDFNR series do not represent "black and white" conclusions or clinical practice standards, but are meant as a starting place for research and active discussions among hospitalists and patients. We invite you to be part of that discussion.

Background: Although hepatitis C virus (HCV) has an estimated national prevalence of 1.8%, testing rates are lower than those recommended by guidelines, particularly in primary care. A critical step is the ability to identify patients at increased risk who should be screened. We sought to prospectively derive and validate a clinical predication tool to assist primary care providers in identifying patients who should be tested for HCV antibodies. Methods: A total of 1000 randomly selected patients attending an inner-city primary care clinic filled out a 27item questionnaire assessing 5 HCV risk factor domains: work, medical, exposure, personal care, and social history. Afterward, the patients underwent HCV antibody testing. Multivariable logistic regression analysis was performed to identify risk factors associated with HCV antibodies. Results: There was an 8.3% (95% confidence interval, 6.7%-10.2%) prevalence of HCV antibodies. The patients who were HCV antibody positive were more likely to be male, older, and insured by Medicaid (PՅ.02). Those who had risk factors within the medical, exposure, and social history domains were more likely to be HCV antibody positive. The area under the receiver operating characteristic curve for the screening tool based on these 3 domains was 0.77. With an increasing number of positive domains, there was a higher likelihood of HCV antibody positivity. Only 2% of patients with 0 risk factors had HCV antibodies. Conclusions: A prediction tool can be used to accurately identify patients at high risk of HCV who may benefit from serologic screening. Future studies should assess whether wider use of this tool may lead to improved outcomes.

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