Patients treated at multiple acute health care facilities: quantifying information fragmentation

Arch Intern Med. 2010 Dec 13;170(22):1989-95. doi: 10.1001/archinternmed.2010.439.

Abstract

Background: Fragmentation of medical information places patients at risk for medical errors, adverse events, duplication of tests, and increased costs. We sought to quantify, at the population level, the burden of fragmentation in the acute care setting across the state of Massachusetts by measuring the rates at which individuals seek care across multiple sites.

Methods: A retrospective observational study of all adult patients with at least 2 visits or hospitalizations to the emergency departments, inpatient units, and observation units in Massachusetts from October 1, 2002, to September 30, 2007.

Results: The 3,692,178 adult patients who visited an acute care site during our study period accounted for 12,758,498 acute care visits. A total of 1,130,124 adult patients (31%) visited 2 or more hospitals during the study period, accounting for 56.5% of all acute care visits, while a subgroup of 43,794 patients (1%) visited 5 or more hospitals, contributing to almost one-tenth of all acute visits. Patients who visited multiple sites were younger (P < .001), more likely to be male (P < .001), more likely to have a primary psychiatric diagnosis (P < .001), and more frequently hospitalized (P < .001) and incurred higher charges than patients who used only a single site of care (P < .001).

Conclusions: A large number of patients seek care at multiple acute care sites. These findings provide one basis for assessing the value of an integrated electronic health information system for clinicians caring for patients across sites of care and therefore the return on investment in health information technology.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, U.S. Gov't, P.H.S.

MeSH terms

  • Adult
  • Aged
  • Emergency Service, Hospital / statistics & numerical data*
  • Female
  • Hospitalization / statistics & numerical data*
  • Humans
  • Logistic Models
  • Male
  • Massachusetts
  • Medical Records / statistics & numerical data*
  • Medical Records Systems, Computerized / statistics & numerical data
  • Middle Aged
  • Multivariate Analysis
  • Predictive Value of Tests
  • Research Design
  • Retrospective Studies
  • Risk Factors