title: Statistical process control as a tool for monitoring nonoperative time.
abstract: BACKGROUND
  Administrators need simple tools to quickly identify even small changes in the performance of perioperative systems. This applies both to established systems and to impact assessments of deliberate perioperative system design changes.
  
  
  METHODS
  Statistical process control was originally developed to detect nonrandom variation in manufacturing processes by continuous comparison to previous performance. The authors applied the technique to assess the nonoperative time performance between successive cases for same surgeon following themselves in a redesigned operating room. This operating room specifically implemented a new patient care pathway that improves throughput by reducing the nonoperative time. The authors tested how quickly statistical process control detected reductions in nonoperative time. They also tested the ability of statistical process control to detect successively smaller performance changes and investigated its utility for longitudinal process monitoring.
  
  
  RESULTS
  Statistical process control detected a clear reduction in nonoperative time after the new operating room had been used for only 2 days. The method could detect nonoperative time changes of between 5 and 10 min per case for a single operating room within one fiscal quarter. Nonoperative time for the new process was globally stable over the 31 months analyzed, but late in the analysis period, the authors detected small performance decrements, mostly attributable to factors external to the new operating room.
  
  
  CONCLUSIONS
  Statistical process control is useful for detecting changes in perioperative system performance, represented in this study by nonoperative time. The technique is able to detect changes quickly and to detect small changes over time.
authors.affiliation: Department of Production and Quality Engineering, Norwegian University of Science and Technology, Trondheim, Norway.
authors.forename: Andreas
authors.initials: A
authors.lastname: Seim
authors.forename: Bjørn
authors.initials: B
authors.lastname: Andersen
authors.forename: Warren S
authors.initials: WS
authors.lastname: Sandberg
mesh_terms: D000465:Algorithms; D003627:Data Interpretation, Statistical; D016208:Databases, Factual; D008137:Longitudinal Studies; D009873:Operating Rooms / Q000458:organization & administration* / Q000706:statistics & numerical data*; D017064:Process Assessment, Health Care; D012189:Retrospective Studies; D013647:Task Performance and Analysis; D017748:Time Management