Hospital workload and adverse events

Med Care. 2007 May;45(5):448-55. doi: 10.1097/01.mlr.0000257231.86368.09.


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.

Publication types

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

MeSH terms

  • Aged
  • Bed Occupancy / statistics & numerical data
  • Diagnosis-Related Groups
  • Female
  • Hospitals, Teaching / standards*
  • Hospitals, Teaching / statistics & numerical data
  • Humans
  • Male
  • Medical Audit
  • Medical Errors / statistics & numerical data
  • Medical Errors / trends*
  • Middle Aged
  • Personnel, Hospital / psychology
  • Personnel, Hospital / statistics & numerical data*
  • Poisson Distribution
  • Quality of Health Care
  • Safety Management
  • United States
  • Workload / statistics & numerical data*