Literature DB >> 33231545

Healthcare Resource Utilization Following a Discharge Against Medical Advice: An Analysis of Commercially Insured Adults.

Aakash Bipin Gandhi1, Eberechukwu Onukwugha1, Jacquelyn McRae1, David Alfandre2.   

Abstract

BACKGROUND: A discharge against medical advice (DAMA) is associated with adverse health outcomes. Its association with postdischarge healthcare resource utilization (HcRU) outside an inpatient setting is unknown. This information can help us understand how a DAMA may affect healthcare-seeking behavior following a hospital stay. We evaluated the relationship between a DAMA and 30-day postdischarge HcRU.
METHODS: This retrospective cohort study uses a 10% random sample of enrollees in the IQVIA PharMetrics® Plus database. We included individuals aged 18 to 64 years with an inpatient admission during 2007-2015 and continuous insurance coverage. We defined comparison groups as DAMA and routine discharge. Both groups were matched on baseline covariates. We quantified the association between a DAMA and 30-day HcRU, as well as 90-day for sensitivity analysis, with use of generalized linear models for binary outcomes (inpatient readmissions, emergency department [ED] visits) and count outcomes (physician office visits, nonphysician outpatient encounters, prescription drug fills).
RESULTS: Of the 457,530 individuals in the unmatched sample, 2,245 (0.5%) had a DAMA. In the matched sample, a DAMA was positively associated with an ED visit (adjusted odds ratio, 2.28; 95% confidence interval, 1.90-2.72) but not with an inpatient readmission. There were no differences between groups based on the count outcomes. A DAMA was positively associated with 90-day HcRU (ie, inpatient readmission, ED visit, and prescription drug fills).
CONCLUSION: The relationship between a DAMA and HcRU varied with the HcRU category and postdischarge time interval. This examination of HcRU in the inpatient and outpatient settings provides important information about outcomes following a DAMA.

Entities:  

Year:  2020        PMID: 33231545      PMCID: PMC8034675          DOI: 10.12788/jhm.3516

Source DB:  PubMed          Journal:  J Hosp Med        ISSN: 1553-5592            Impact factor:   2.960


  23 in total

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Authors:  S T Normand; M B Landrum; E Guadagnoli; J Z Ayanian; T J Ryan; P D Cleary; B J McNeil
Journal:  J Clin Epidemiol       Date:  2001-04       Impact factor: 6.437

2.  Outpatient follow-up and rehospitalizations for sickle cell disease patients.

Authors:  John Leschke; Julie A Panepinto; Mark Nimmer; Raymond G Hoffmann; Ke Yan; David C Brousseau
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3.  Improving quality in against medical advice discharges--More empirical evidence, enhanced professional education, and directed systems changes.

Authors:  David Alfandre
Journal:  J Hosp Med       Date:  2017-01       Impact factor: 2.960

4.  Readmissions after unauthorized discharges in the cardiovascular setting.

Authors:  Eberechukwu Onukwugha; C Daniel Mullins; F Ellen Loh; Elijah Saunders; Fadia T Shaya; Matthew R Weir
Journal:  Med Care       Date:  2011-02       Impact factor: 2.983

5.  Change in readmissions and follow-up visits as part of a heart failure readmission quality improvement initiative.

Authors:  Jason Ryan; Sangwook Kang; Steven Dolacky; Joseph Ingrassia; Raj Ganeshan
Journal:  Am J Med       Date:  2013-09-18       Impact factor: 4.965

6.  Hospital discharge against advice after myocardial infarction: deaths and readmissions.

Authors:  Kevin Fiscella; Sean Meldrum; Steve Barnett
Journal:  Am J Med       Date:  2007-12       Impact factor: 4.965

Review 7.  Discharge against medical advice: how often do we intervene?

Authors:  Jason Edwards; Ronald Markert; Dean Bricker
Journal:  J Hosp Med       Date:  2013-09-20       Impact factor: 2.960

8.  Rates of readmission and death associated with leaving hospital against medical advice: a population-based study.

Authors:  Allan Garland; Clare D Ramsey; Randy Fransoo; Kendiss Olafson; Daniel Chateau; Marina Yogendran; Allen Kraut
Journal:  CMAJ       Date:  2013-08-26       Impact factor: 8.262

9.  Optimal caliper widths for propensity-score matching when estimating differences in means and differences in proportions in observational studies.

Authors:  Peter C Austin
Journal:  Pharm Stat       Date:  2011 Mar-Apr       Impact factor: 1.894

10.  A comparison of 12 algorithms for matching on the propensity score.

Authors:  Peter C Austin
Journal:  Stat Med       Date:  2013-10-07       Impact factor: 2.373

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