2006
DOI: 10.1097/01.mlr.0000215811.68308.ae
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Fragmentation of Care for Frequently Hospitalized Urban Residents

Abstract: In NYC, Medicaid enrollment is associated with greater fragmentation of hospital care, but this is largely attributable to the preponderance of Medicaid enrollees with diagnoses of psychosis and substance abuse. Strategies to improve the efficiency of healthcare delivery should focus on patients with mental illness who are frequently admitted to general hospitals.

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Cited by 66 publications
(63 citation statements)
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“…Not only are these patients exposed to the harms associated with fragmented care; they also have a tendency to consume a larger share of healthcare expenditures. [38][39][40] It should be noted, however, that the number of patients shared by providers may also be influenced by other factors, such as crosscoverage within the same practice. Since claims do not capture group practice designations, we were unable to account for intra-organizational patient sharing, and therefore may have overestimated the collaborative ties among cross-covering providers.…”
Section: Discussionmentioning
confidence: 99%
“…Not only are these patients exposed to the harms associated with fragmented care; they also have a tendency to consume a larger share of healthcare expenditures. [38][39][40] It should be noted, however, that the number of patients shared by providers may also be influenced by other factors, such as crosscoverage within the same practice. Since claims do not capture group practice designations, we were unable to account for intra-organizational patient sharing, and therefore may have overestimated the collaborative ties among cross-covering providers.…”
Section: Discussionmentioning
confidence: 99%
“…In addition, definitions of being a "frequent" or "high" user of hospital services vary from study to study, and in many cases, frequent use is defined within a single year, rather than over a longer period of time. 7,12,16 To our knowledge, this is the first study to employ a validated predictive casefinding algorithm to identify, in real time, patients at risk of future hospital admission. Our algorithm was developed to promote accurate patient identification, an important feature if it is to provide a basis for interventions targeting patients with frequent hospital admissions.…”
Section: Discussionmentioning
confidence: 99%
“…It has been demonstrated that in addition to chronic disease, other factors such as mental illness, substance use, housing status, use of a medical home, medication adherence, and social networks influence use of health services. 7,[19][20][21] Thus, in addition to demographic information, we used previously validated tools to collect data on general health status, 22,23 substance use, 24 social support, 25 mental health, 26 patient participation in his/her own health care, 27 usual source of care, 28 living situation, hunger, 29 and medication adherence. 30 Tools were selected by investigator consensus and input from experts in the areas of interest.…”
Section: Data Collectionmentioning
confidence: 99%
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“…Frequent users of inpatient and emergency department (ED) services account for a disproportionate share of total health care visits and costs (Schrag et al, 2006). Such patients are affected by high rates of chronic disease, psychiatric illness, fragmented care, and low socioeconomic status (Billings, Dixon, Mijanovich, & Wennberg, 2006;Billings & Mijanovich, 2007;Ross & Turner, 1994).…”
Section: Introductionmentioning
confidence: 99%