title: A mathematical model for simulating daily bed occupancy in an intensive care unit.
abstract: OBJECTIVE
  To develop a mathematical model for simulating the daily bed occupancy in an intensive care unit.
  
  
  METHODS
  Data collection and retrospective analysis to develop a mathematical model for simulating daily bed occupancy in an intensive care unit.
  
  
  METHODS
  We studied all admissions to the intensive care unit at the Hospital of Navarra over a 9-yr period.
  
  
  METHODS
  Six-thousand three-hundred adult patients consecutively admitted to intensive care units at a tertiary care hospital.
  
  
  METHODS
  None.
  
  
  RESULTS
  The large set of data collected comprises an arrivals file, a patient file, and a bed occupancy file. The arrival file records the number of patients admitted to the intensive care unit each day, by admission type, and by day of the week. The patient file contains records for all patients admitted to the intensive care unit during the study period: Admission type, admission and discharge dates, age, sex, Acute Physiology and Chronic Health Evaluation II score within the first 24 hrs, infections during hospitalization, and mortality. We used these two files to fit appropriate statistical models of arrival rates and length of stay by patient type. Based on this statistical analysis and the representation of the intensive care unit as a queuing problem, we built a simulation model. The bed occupancy file records the number of occupied beds at 4:00 PM each day. We used this file to validate the simulation model by testing the similarity of the real and simulated output data. The simulation model also includes bed management decisions related to patient discharge.
  
  
  RESULTS
  We obtained a valid simulation model that reproduced on a computer the patient flow through the intensive care unit at the Hospital of Navarra. This computerized simulation model can be used to study the intensive care unit bed occupancy profile and can be used as a reliable sizing and capacity analysis tool. As an example, we present the problem of estimating the number of beds needed to meet an increase in patient arrivals at the intensive care unit because of different causes.
  
  
  CONCLUSIONS
  It is possible to develop simulation models that can be used to predict future intensive care unit resource needs.
authors: Hospital of Navarra, Pamplona, Spain. jbaradoh@navarra.es
authors: Julio
authors: J
authors: Barado
authors: Juan María
authors: JM
authors: Guergué
authors: Laida
authors: L
authors: Esparza
authors: Crisitina
authors: C
authors: Azcárate
authors: Fermín
authors: F
authors: Mallor
authors: Susana
authors: S
authors: Ochoa
mesh_terms: D000046:Academic Medical Centers / Q000706:statistics & numerical data; D001509:Bed Occupancy / Q000706:statistics & numerical data*; D017052:Hospital Mortality; D006801:Humans; D007362:Intensive Care Units / Q000706:statistics & numerical data*; D007902:Length of Stay; D008962:Models, Theoretical; D013030:Spain