title: Optimizing cardiology capacity to reduce emergency department boarding: a systems engineering approach.
abstract: BACKGROUND
  Patient safety and emergency department (ED) functionality are compromised when inefficient coordination between hospital departments impedes ED patients' access to inpatient cardiac care. The objective of this study was to determine how bed demand from competing cardiology admission sources affects ED patients' access to inpatient cardiac care.
  
  
  METHODS
  A stochastic discrete event simulation of hospital patient flow predicted ED patient boarding time, defined as the time interval between cardiology admission request to inpatient bed placement, as the primary outcome measure. The simulation was built and tested from 1 year of patient flow data and was used to examine prospective strategies to reduce cardiology patient boarding time.
  
  
  RESULTS
  Boarding time for the 1,591 ED patients who were admitted to the cardiac telemetry unit averaged 5.3 hours (median 3.1, interquartile range 1.5-6.9). Demographic and clinical patient characteristics were not significant predictors of boarding time. Measurements of bed demand from competing admission sources significantly predicted boarding time, with catheterization laboratory demand levels being the most influential. Hospital policy required that a telemetry bed be held for each electively scheduled catheterization patient, yet the analysis revealed that 70.4% (95% CI 51.2-92.5) of these patients did not transfer to a telemetry bed and were discharged home each day. Results of simulation-based analyses showed that moving one afternoon scheduled elective catheterization case to before noon resulted in a 20-minute reduction in average boarding time compared to a 9-minute reduction achieved by increasing capacity by one additional telemetry bed.
  
  
  CONCLUSIONS
  Scheduling and bed management practices based on measured patient transfer patterns can reduce inpatient bed blocking, optimize hospital capacity, and improve ED patient access.
authors.affiliation: Department of Emergency Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21230, USA. slevin33@jhmi.edu
authors.forename: Scott R
authors.initials: SR
authors.lastname: Levin
authors.forename: Robert
authors.initials: R
authors.lastname: Dittus
authors.forename: Dominik
authors.initials: D
authors.lastname: Aronsky
authors.forename: Matthew B
authors.initials: MB
authors.lastname: Weinger
authors.forename: Jin
authors.initials: J
authors.lastname: Han
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Boord
authors.forename: Daniel
authors.initials: D
authors.lastname: France
mesh_terms: D001071:Appointments and Schedules; D006328:Cardiac Catheterization / Q000706:statistics & numerical data; D003198:Computer Simulation; D003325:Coronary Care Units / Q000706:statistics & numerical data / Q000600:supply & distribution; D004636:Emergency Service, Hospital / Q000706:statistics & numerical data*; D006297:Health Services Accessibility / Q000706:statistics & numerical data*; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data*; D006801:Humans; D007902:Length of Stay / Q000706:statistics & numerical data; D010343:Patient Admission / Q000706:statistics & numerical data*; D010351:Patient Discharge / Q000706:statistics & numerical data; D040841:Resource Allocation / Q000706:statistics & numerical data; D013269:Stochastic Processes; D013686:Telemetry / Q000706:statistics & numerical data; D013714:Tennessee; D013996:Time and Motion Studies