title: Can We Predict Patient Wait Time?
abstract: OBJECTIVE
  The importance of patient wait-time management and predictability can hardly be overestimated: For most hospitals, it is the patient queues that drive and define every bit of clinical workflow. The objective of this work was to study the predictability of patient wait time and identify its most influential predictors.
  
  
  METHODS
  To solve this problem, we developed a comprehensive list of 25 wait-related parameters, suggested in earlier work and observed in our own experiments. All parameters were chosen as derivable from a typical Hospital Information System dataset. The parameters were fed into several time-predicting models, and the best parameter subsets, discovered through exhaustive model search, were applied to a large sample of actual patient wait data.
  
  
  RESULTS
  We were able to discover the most efficient wait-time prediction factors and models, such as the line-size models introduced in this work. Moreover, these models proved to be equally accurate and computationally efficient. Finally, the selected models were implemented in our patient waiting areas, displaying predicted wait times on the monitors located at the front desks. The limitations of these models are also discussed.
  
  
  CONCLUSIONS
  Optimal regression models based on wait-line sizes can provide accurate and efficient predictions for patient wait time.
authors.affiliation: Department of Radiology, Harvard Medical School; Radiology Department, Massachusetts General Hospital, Boston, Massachusetts. Electronic address: opianykh@mgh.harvard.edu.
authors.forename: Oleg S
authors.initials: OS
authors.lastname: Pianykh
authors.affiliation: Department of Radiology, Harvard Medical School; Radiology Department, Massachusetts General Hospital, Boston, Massachusetts.
authors.forename: Daniel I
authors.initials: DI
authors.lastname: Rosenthal
mesh_terms: D001900:Boston / Q000453:epidemiology; D003198:Computer Simulation; D006751:Hospital Information Systems / Q000706:statistics & numerical data*; D006760:Hospitalization / Q000706:statistics & numerical data*; D006801:Humans; D015233:Models, Statistical; D011859:Radiography / Q000706:statistics & numerical data*; D011872:Radiology Department, Hospital / Q000706:statistics & numerical data*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013997:Time Factors; D014850:Waiting Lists
keywords: Hospital Information System; Queueing Theory; predictive models; regression; time series