Adding more beds to the emergency department or reducing admitted patient boarding times: which has a more significant influence on emergency department congestion?

Ann Emerg Med. 2009 May;53(5):575-85. doi: 10.1016/j.annemergmed.2008.07.009. Epub 2008 Sep 10.


Study objective: We evaluate a computer simulation model designed to assess the effect on emergency department (ED) length of stay of varying the number of ED beds or altering the interval of admitted patient departure from the ED.

Methods: We created a computer simulation model (Med Model) based on institutional data and augmented by expert estimates and assumptions. We evaluated simulations of increasing the number of ED beds, increasing the admitted patient departure and increasing ED census, analyzing potential effects on overall ED length of stay. Multiple sensitivity analyses tested the robustness of the results to changes in model assumptions and institutional data.

Results: With a constant ED departure rate at the base case and increasing ED beds, there is an increase in mean length of stay from 240 to 247 minutes (95% confidence interval [CI] 0.8 to 12.6 minutes). When keeping the number of beds constant at the base case and increasing the rate at which admitted patients depart the ED to their inpatient bed, the mean overall ED length of stay decreases from 240 to 218 minutes (95% CI 16.8 to 26.2 minutes). With a 15% increase in daily census, the trends are similar to the base case results. The sensitivity analyses reveal that despite a wide range of inputs, there are no differences from the base case.

Conclusion: Our computer simulation modeled that improving the rate at which admitted patients depart the ED produced an improvement in overall ED length of stay, whereas increasing the number of ED beds did not.

Publication types

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

MeSH terms

  • Bed Occupancy / statistics & numerical data*
  • Computer Simulation*
  • Crowding*
  • Efficiency, Organizational
  • Emergency Service, Hospital / organization & administration*
  • Humans
  • Monte Carlo Method
  • Outcome and Process Assessment, Health Care
  • Patient Admission / statistics & numerical data*