Literature DB >> 29340940

Characterizing Potentially Preventable Admissions: A Mixed Methods Study of Rates, Associated Factors, Outcomes, and Physician Decision-Making.

Lisa M Daniels1, Atsushi Sorita2, Deanne T Kashiwagi2, Masashi Okubo3, Evan Small4, Eric C Polley5, Adam P Sawatsky6.   

Abstract

BACKGROUND: Potentially preventable admissions are a target for healthcare cost containment.
OBJECTIVE: To identify rates of, characterize associations with, and explore physician decision-making around potentially preventable admissions.
DESIGN: A comparative cohort study was used to determine rates of potentially preventable admissions and to identify associated factors and patient outcomes. A qualitative case study was used to explore physicians' clinical decision-making. PARTICIPANTS: Patients admitted from the emergency department (ED) to the general medicine (GM) service over a total of 4 weeks were included as cases (N = 401). Physicians from both emergency medicine (EM) and GM that were involved in the cases were included (N = 82). APPROACH: Physicians categorized admissions as potentially preventable. We examined differences in patient characteristics, admission characteristics, and patient outcomes between potentially preventable and control admissions. Interviews with participating physicians were conducted and transcribed. Transcriptions were systematically analyzed for key concepts regarding potentially preventable admissions. KEY
RESULTS: EM and GM physicians categorized 22.2% (90/401) of admissions as potentially preventable. There were no significant differences between potentially preventable and control admissions in patient or admission characteristics. Potentially preventable admissions had shorter length of stay (2.1 vs. 3.6 days, p < 0.001). There was no difference in other patient outcomes. Physicians discussed several provider, system, and patient factors that affected clinical decision-making around potentially preventable admissions, particularly in the "gray zone," including risk of deterioration at home, the risk of hospitalization, the cost to the patient, and the presence of outpatient resources. Differences in provider training, risk assessment, and provider understanding of outpatient access accounted for differences in decisions between EM and GM physicians.
CONCLUSIONS: Collaboration between EM and GM physicians around patients in the gray zone, focusing on patient risk, cost, and outpatient resources, may provide an avenues for reducing potentially preventable admissions and lowering healthcare spending.

Entities:  

Keywords:  avoidable admissions; health care costs; medical decision-making; potentially preventable admissions; risk assessment

Year:  2018        PMID: 29340940      PMCID: PMC5910342          DOI: 10.1007/s11606-017-4285-6

Source DB:  PubMed          Journal:  J Gen Intern Med        ISSN: 0884-8734            Impact factor:   5.128


  34 in total

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5.  Primary care and emergency department decision making.

Authors:  J E McNulty; L C Hampers; S E Krug
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6.  Predicting emergency department inpatient admissions to improve same-day patient flow.

Authors:  Jordan S Peck; James C Benneyan; Deborah J Nightingale; Stephan A Gaehde
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Authors:  Jennifer Prah Ruger; Lawrence M Lewis; Christopher J Richter
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9.  Using simulation to isolate physician variation in intensive care unit admission decision making for critically ill elders with end-stage cancer: a pilot feasibility study.

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Journal:  Crit Care Med       Date:  2008-12       Impact factor: 7.598

10.  How do emergency physicians make discharge decisions?

Authors:  Lisa A Calder; Trevor Arnason; Christian Vaillancourt; Jeffrey J Perry; Ian G Stiell; Alan J Forster
Journal:  Emerg Med J       Date:  2013-09-17       Impact factor: 2.740

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Authors:  Peter Cram
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2.  Triaging Inpatient Admissions: an Opportunity for Resident Education.

Authors:  Emily S Wang; Sadie Trammell Velásquez; Christopher J Smith; Tabatha H Matthias; David Schmit; Sherwin Hsu; Luci K Leykum
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3.  Risk factors for avoidable hospitalizations in Canada using national linked data: A retrospective cohort study.

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