title: A model to create an efficient and equitable admission policy for patients arriving to the cardiothoracic ICU.
abstract: OBJECTIVE
  To develop queuing and simulation-based models to understand the relationship between ICU bed availability and operating room schedule to maximize the use of critical care resources and minimize case cancellation while providing equity to patients and surgeons.
  
  
  METHODS
  Retrospective analysis of 6-month unit admission data from a cohort of cardiothoracic surgical patients, to create queuing and simulation-based models of ICU bed flow. Three different admission policies (current admission policy, shortest-processing-time policy, and a dynamic policy) were then analyzed using simulation models, representing 10 yr worth of potential admissions. Important output data consisted of the "average waiting time," a proxy for unit efficiency, and the "maximum waiting time," a surrogate for patient equity.
  
  
  METHODS
  A cardiothoracic surgical ICU in a tertiary center in New York, NY.
  
  
  METHODS
  Six hundred thirty consecutive cardiothoracic surgical patients admitted to the cardiothoracic surgical ICU.
  
  
  METHODS
  None.
  
  
  RESULTS
  Although the shortest-processing-time admission policy performs best in terms of unit efficiency (0.4612 days), it did so at expense of patient equity prolonging surgical waiting time by as much as 21 days. The current policy gives the greatest equity but causes inefficiency in unit bed-flow (0.5033 days). The dynamic policy performs at a level (0.4997 days) 8.3% below that of the shortest-processing-time in average waiting time; however, it balances this with greater patient equity (maximum waiting time could be shortened by 4 days compared to the current policy).
  
  
  CONCLUSIONS
  Queuing theory and computer simulation can be used to model case flow through a cardiothoracic operating room and ICU. A dynamic admission policy that looks at current waiting time and expected ICU length of stay allows for increased equity between patients with only minimum losses of efficiency. This dynamic admission policy would seem to be a superior in maximizing case-flow. These results may be generalized to other surgical ICUs.
authors.affiliation: Department of Operations and Supply Chain Management, University of St. Thomas, Saint Paul, MN, USA. yangmuer@stthomas.edu
authors.forename: Muer
authors.initials: M
authors.lastname: Yang
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Fry
authors.forename: Jayashree
authors.initials: J
authors.lastname: Raikhelkar
authors.forename: Cynthia
authors.initials: C
authors.lastname: Chin
authors.forename: Anelechi
authors.initials: A
authors.lastname: Anyanwu
authors.forename: Jordan
authors.initials: J
authors.lastname: Brand
authors.forename: Corey
authors.initials: C
authors.lastname: Scurlock
mesh_terms: D001071:Appointments and Schedules; D015331:Cohort Studies; D003198:Computer Simulation; D003325:Coronary Care Units / Q000458:organization & administration*; D017598:Efficiency, Organizational; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration*; D007902:Length of Stay; D008962:Models, Theoretical; D009519:New York City; D016134:Organizational Policy; D010343:Patient Admission; D058996:Quality Improvement; D012189:Retrospective Studies; D013997:Time Factors