title: Understanding Preprocedure Patient Flow in IR.
abstract: OBJECTIVE
  To quantify preprocedural patient flow in interventional radiology (IR) and to identify potential contributors to preprocedural delays.
  
  
  METHODS
  An administrative dataset was used to compute time intervals required for various preprocedural patient-flow processes. These time intervals were compared across on-time/delayed cases and inpatient/outpatient cases by Mann-Whitney U test. Spearman ρ was used to assess any correlation of the rank of a procedure on a given day and the procedure duration to the preprocedure time. A linear-regression model of preprocedure time was used to further explore potential contributing factors. Any identified reason(s) for delay were collated. P < .05 was considered statistically significant.
  
  
  RESULTS
  Of the total 1,091 cases, 65.8% (n = 718) were delayed. Significantly more outpatient cases started late compared with inpatient cases (81.4% vs 45.0%; P < .001, χ(2) test). The multivariate linear regression model showed outpatient status, length of delay in arrival, and longer procedure times to be significantly associated with longer preprocedure times. Late arrival of patients (65.9%), unavailability of physicians (18.4%), and unavailability of procedure room (13.0%) were the three most frequently identified reasons for delay. The delay was multifactorial in 29.6% of cases (n = 213).
  
  
  CONCLUSIONS
  Objective measurement of preprocedural IR patient flow demonstrated considerable waste and highlighted high-yield areas of possible improvement. A data-driven approach may aid efficient delivery of IR care.
authors.affiliation: Departments of Radiology University of Texas Health Sciences Center San Antonio, San Antonio, Texas; Department of Radiology (A.M.Z.) University Health System, San Antonio, Texas. Electronic address: amueed@gmail.com.
authors.forename: Abdul Mueed
authors.initials: AM
authors.lastname: Zafar
authors.affiliation: Departments of Radiology University of Texas Health Sciences Center San Antonio, San Antonio, Texas.
authors.forename: Rajeev
authors.initials: R
authors.lastname: Suri
authors.affiliation: School of Medicine, University of Texas Health Sciences Center San Antonio, San Antonio, Texas.
authors.forename: Tran Khanh
authors.initials: TK
authors.lastname: Nguyen
authors.affiliation: School of Medicine, University of Texas Health Sciences Center San Antonio, San Antonio, Texas.
authors.forename: Carson Cope
authors.initials: CC
authors.lastname: Petrash
authors.affiliation: Internal Medicine University of Texas Health Sciences Center San Antonio, San Antonio, Texas; Community Medicine Associates (Z.F.), University Health System, San Antonio, Texas.
authors.forename: Zanira
authors.initials: Z
authors.lastname: Fazal
mesh_terms: D000553:Ambulatory Care / Q000458:organization & administration; D001071:Appointments and Schedules; D016009:Chi-Square Distribution; D016208:Databases, Factual; D019033:Delivery of Health Care, Integrated / Q000458:organization & administration*; D017598:Efficiency, Organizational; D006785:Hospitals, University / Q000458:organization & administration; D006801:Humans; D007297:Inpatients; D016014:Linear Models; D017146:Models, Organizational; D015999:Multivariate Analysis; D009873:Operating Rooms / Q000458:organization & administration; D010045:Outpatients; D010561:Personnel Staffing and Scheduling / Q000458:organization & administration; D058996:Quality Improvement / Q000458:organization & administration; D019984:Quality Indicators, Health Care / Q000458:organization & administration; D015641:Radiography, Interventional; D011872:Radiology Department, Hospital / Q000458:organization & administration*; D015642:Radiology, Interventional / Q000458:organization & administration*; D012189:Retrospective Studies; D012307:Risk Factors; D013781:Texas; D013997:Time Factors; D013996:Time and Motion Studies