title: Modelling patient flows as an aid to decision making for critical care capacities and organisation.
abstract: Using real data from a number of hospitals, we predicted the patient flows following a capacity or organisational change. Clinically recognisable patient groups obtained through classification and regression tree analysis were used to tune a simulation model for the flow of patients in critical care units. A tuned model which accurately reflected the base case of the flow of patients was used to predict alterations in service provision in a number of scenarios which included increases in bed numbers, alterations in patients' lengths of stay, fewer delayed discharges, caring for long stay patients outside the formal intensive care unit and amalgamating small units. Where available the predictions' accuracy was checked by comparison with real hospital data collected after an actual capacity change. The model takes variability and uncertainty properly into account and it provides the necessary information for making better decisions about critical care capacity and organisation.
authors.affiliation: School of Mathematics, GeoData Institute, University of Southampton, Southampton, UK. akshahani2@googlemail.com
authors.forename: A K
authors.initials: AK
authors.lastname: Shahani
authors.forename: S A
authors.initials: SA
authors.lastname: Ridley
authors.forename: M S
authors.initials: MS
authors.lastname: Nielsen
mesh_terms: D003422:Critical Care / Q000458:organization & administration*; D003659:Decision Making, Organizational; D003661:Decision Support Techniques; D004739:England; D006302:Health Services Research / Q000379:methods; D006742:Hospital Bed Capacity / Q000706:statistics & numerical data; D006801:Humans; D007362:Intensive Care Units / Q000458:organization & administration / Q000706:statistics & numerical data; D007902:Length of Stay / Q000706:statistics & numerical data; D017146:Models, Organizational; D010343:Patient Admission / Q000706:statistics & numerical data; D014852:Wales