title: Decreasing extremes in patient waiting time.
abstract: BACKGROUND
  We present a conceptual framework for approaching reducing excessive patient wait time in an outpatient setting. We hypothesized that statistical process control techniques can be used to identify extremes in waiting time; root cause analysis can be used to identify specific delay causes; and minimizing the contribution of the root causes will lead to an improvement in system performance.
  
  
  METHODS
  We conducted a prospective study of waiting times in a private outpatient clinic providing high-risk obstetrical care. The baseline period consisted of 55 clinic sessions, and the intervention period consisted of 101 clinic sessions.
  
  
  RESULTS
  Mean waiting time was prolonged during 9 (16.4%) baseline clinic sessions. The root cause analysis determined that appointment schedule, physician tardiness, and patient complexity contributed to clinic delays. After making changes to minimize root causes, there was a significant reduction in prolonged waiting times (16.4% vs 4.9%, Yates chi(2) = 4.37, P = .037); a significant decrease in mean waiting time (32.7 +/- 23.6 minutes vs 29.3 +/- 21.2 minutes, t = 3.42, P < .001); and a significant improvement in the waiting time distribution (Kruskal-Wallis test of homogeneity, P = .003).
  
  
  CONCLUSIONS
  Our methodology was successful in identifying and reducing factors associated with prolonged wait times. However, although system operation was improved, as defined by a decrease in the occurrence of excessive clinic delays, effecting a large and sustained decrease in patient waiting times was challenging.
authors.affiliation: Department of Obstetrics and Gynecology, Louisiana State University Health Sciences Center, Shreveport, LA 71130, USA. lgroom@lsuhsc.edu
authors.forename: Lynn J
authors.initials: LJ
authors.lastname: Groome
authors.forename: E J
authors.initials: EJ
authors.lastname: Mayeaux
mesh_terms: D000465:Algorithms; D000554:Ambulatory Care Facilities / Q000458:organization & administration*; D015984:Causality; D017598:Efficiency, Organizational; D005260:Female; D006801:Humans; D009774:Obstetrics; D011247:Pregnancy; D011446:Prospective Studies; D013997:Time Factors; D014850:Waiting Lists