Scheduling Anesthesia Time Reduces Case Cancellations and Improves Operating Room Workflow in a University Hospital Setting

Elizabeth van Veen-Berkx, Menno V van Dijk, Diederich C Cornelisse, Geert Kazemier, Fleur C Mokken

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

BACKGROUND: A new method of scheduling anesthesia-controlled time (ACT) was implemented on July 1, 2012 in an academic inpatient operating room (OR) department. This study examined the relationship between this new scheduling method and OR performance. The new method comprised the development of predetermined time frames per anesthetic technique based on historical data of the actual time needed for anesthesia induction and emergence. Seven "anesthesia scheduling packages" (0 to 6) were established. Several options based on the quantity of anesthesia monitoring and the complexity of the patient were differentiated in time within each package.

STUDY DESIGN: This was a quasi-experimental time-series design. Relevant data were divided into 4 equal periods of time. These time periods were compared with ANOVA with contrast analysis: an intervention, pre-intervention, and post-intervention contrast were tested. All emergency cases were excluded. A total of 34,976 inpatient elective cases performed from January 1, 2010 to December 31, 2014 were included for statistical analyses.

RESULTS: The intervention contrast showed a significant decrease (p < 0.001) of 4.5% in the prediction error. The total number of cancellations decreased to 19.9%. The ANOVA with contrast analyses showed no significant differences with respect to under- and over-used OR time and raw use. Unanticipated results derived from this study, allowing for a smoother workflow: eg anesthesia nurses know exactly which medical equipment and devices need to be assembled and tested beforehand, based on the scheduled anesthesia package.

CONCLUSIONS: Scheduling the 2 major components of a procedure (anesthesia- and surgeon-controlled time) more accurately leads to fewer case cancellations, lower prediction errors, and smoother OR workflow in a university hospital setting.

Original languageEnglish
Pages (from-to)343-51
Number of pages9
JournalJournal of the American College of Surgeons
Volume223
Issue number2
DOIs
Publication statusPublished - Aug 2016

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