Context: Hospitals are under pressure to increase revenue and lower costs, and at the same time, they face dramatic variation in clinical demand.
Objective: We sought to determine the relationship between peak hospital workload and rates of adverse events (AEs).
Methods: A random sample of 24,676 adult patients discharged from the medical/surgical services at 4 US hospitals (2 urban and 2 suburban teaching hospitals) from October 2000 to September 2001 were screened using administrative data, leaving 6841 cases to be reviewed for the presence of AEs. Daily workload for each hospital was characterized by volume, throughput (admissions and discharges), intensity (aggregate DRG weight), and staffing (patient-to-nurse ratios). For volume, we calculated an “enhanced” occupancy rate that accounted for same-day bed occupancy by more than 1 patient. We used Poisson regressions to predict the likelihood of an AE, with control for workload and individual patient complexity, and the effects of clustering.
Results: One urban teaching hospital had enhanced occupancy rates more than 100% for much of the year. At that hospital, admissions and patients per nurse were significantly related to the likelihood of an AE (P < 0.05); occupancy rate, discharges, and DRG-weighted census were significant at P < 0.10. For example, a 0.1% increase in the patient-to-nurse ratio led to a 28% increase in the AE rate. Results at the other 3 hospitals varied and were mainly non significant.
Conclusions: Hospitals that operate at or over capacity may experience heightened rates of patient safety events and might consider re-engineering the structures of care to respond better during periods of high stress.
From the *Institute for Health Policy, Massachusetts General Hospital, Boston; †Department of Health Care Policy, Harvard Medical School, Boston, Massachusetts; ‡University of Western Australia, Perth; §Division of General Medicine and Primary Care, Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts; ¶LDS Hospital/Intermountain Healthcare, Department of Medical Informatics, University of Utah, Salt Lake City; ∥Newton-Wellesley Hospital, Newton, Massachusetts; **Division of General Medicine and Public Health, Vanderbilt University Medical Center and the Department of Veterans Affairs, Tennessee Valley Healthcare System, GRECC, Nashville; and ††Center for Health Policy, Stanford University, Stanford, California.
Supported by Grants 1 R01 HS12035 and RO1 HS12035-02-S1 from the Agency for Health Research and Quality, USDHHS.
The views and opinions expressed in this article are the authors’ and no endorsement by AHRQ is intended or implied.
Reprints: Joel S. Weissman, PhD, MGH Institute for Health Policy, 50 Staniford Street, 9th Floor, Suite 907, Boston, MA 02114. E-mail: firstname.lastname@example.org.