title: A Conceptual Framework for Improving Critical Care Patient Flow and Bed Use.
abstract: BACKGROUND
  High demand for intensive care unit (ICU) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine ICU bed assignment policies, accounting for patient acuity, to reduce ICU admission delays.
  
  
  OBJECTIVE
  To provide a framework for data-driven modeling of ICU patient flow, identify key measurable outcomes, and present illustrative analysis demonstrating the impact of various bed allocation scenarios on outcomes.
  
  
  METHODS
  A description of key inputs for constructing a queuing model was outlined, and an illustrative simulation model was developed to reflect current triage protocol within the medical ICU and step-down unit (SDU) at a single tertiary-care hospital. Patient acuity, arrival rate, and unit length of stay, consisting of a "service time" and "time to transfer," were estimated from 12 months of retrospective data (n = 2,710 adult patients) for 36 ICU and 15 SDU staffed beds. Patient priority was based on acuity and whether the patient originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied ICU/SDU sizes, (2) reserved ICU beds as a triage strategy, (3) lower targets for time to transfer out of the ICU, and (4) ICU expansion by up to four beds. Outcomes included ICU admission wait times and unit occupancy.
  
  
  RESULTS
  With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as ICU decreased overall wait times by 7.2% to 1.06 (SD, 1.39) hours and increased bed occupancy from 80 to 84%. Reserving the last available bed for acute patients reduced wait times for acute patients from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute patients' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all ICU/SDU patients to 1 hour decreased wait times for incoming ICU patients, comparable to building one to two additional ICU beds.
  
  
  CONCLUSIONS
  Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in ICU bed assignment could impact unit occupancy levels and patient wait times. Trade-offs associated with dedicating resources for acute patients versus expanding capacity for all patients can be examined.
authors.affiliation: 1 Division of Pulmonary, Critical Care, and Sleep Medicine, Department of Medicine, and.|2 Department of Emergency Medicine, Icahn School of Medicine at Mount Sinai, New York, New York; and.
authors.forename: Kusum S
authors.identifier: http://orcid.org/0000-0002-8810-2794
authors.initials: KS
authors.lastname: Mathews
authors.affiliation: 3 UCLA Anderson School of Management, Los Angeles, California.
authors.forename: Elisa F
authors.initials: EF
authors.lastname: Long
mesh_terms: D000328:Adult; D000368:Aged; D001509:Bed Occupancy / Q000706:statistics & numerical data; D003198:Computer Simulation; D005260:Female; D015276:Health Care Rationing / Q000458:organization & administration*; D006297:Health Services Accessibility / Q000592:standards*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration*; D008297:Male; D008875:Middle Aged; D017146:Models, Organizational; D062072:Patient Acuity; D018579:Patient Selection; D040841:Resource Allocation / Q000458:organization & administration*; D013598:Systems Theory; D014218:Triage / Q000379:methods / Q000706:statistics & numerical data; D014481:United States
keywords: computer simulation; intensive care unit; queuing theory; resource allocation
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