A Conceptual Framework for Improving Critical Care XXXX Flow and Bed Use.

@entity1 @entity1213
RATIONALE: High demand for @entity1213 ( @entity1213 ) services and limited bed availability have prompted hospitals to address capacity planning challenges. Simulation modeling can examine @entity1213 bed assignment policies, accounting for @entity1 acuity, to reduce @entity1213 admission delays. OBJECTIVES: To provide a framework for data-driven modeling of @entity1213 @entity1 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 @entity1213 and step-down unit (SDU) at a single tertiary-care hospital. @entity1 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 @entity1 ) for 36 @entity1213 and 15 SDU staffed beds. @entity1 priority was based on acuity and whether the @entity1 originated in the emergency department. The model simulated the following hypothetical scenarios: (1) varied @entity1213 /SDU sizes, (2) reserved @entity1213 beds as a triage strategy, (3) lower targets for time to transfer out of the @entity1213 , and (4) @entity1213 expansion by up to four beds. Outcomes included @entity1213 admission wait times and unit occupancy. MEASUREMENTS AND MAIN RESULTS: With current bed allocation, simulated wait time averaged 1.13 (SD, 1.39) hours. Reallocating all SDU beds as @entity1213 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 @entity1 reduced wait times for acute @entity1 from 0.84 (SD, 1.12) to 0.31 (SD, 0.30) hours, but tripled subacute @entity1 ' wait times from 1.39 (SD, 1.81) to 4.27 (SD, 5.44) hours. Setting transfer times to wards for all @entity1213 /SDU @entity1 to 1 hour decreased wait times for incoming @entity1213 @entity1 , comparable to building one to two additional @entity1213 beds. CONCLUSIONS: Hospital queuing and simulation modeling with empiric data inputs can evaluate how changes in @entity1213 bed assignment could impact unit occupancy levels and @entity1 wait times. Trade-offs associated with dedicating resources for acute @entity1 versus expanding capacity for all @entity1 can be examined.
