Literature DB >> 27063614

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

Elizabeth van Veen-Berkx1, Menno V van Dijk2, Diederich C Cornelisse3, Geert Kazemier4, Fleur C Mokken5.   

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.
Copyright © 2016 American College of Surgeons. Published by Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 27063614     DOI: 10.1016/j.jamcollsurg.2016.03.038

Source DB:  PubMed          Journal:  J Am Coll Surg        ISSN: 1072-7515            Impact factor:   6.113


  5 in total

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Authors:  Michael T Milone; Heero Hacquebord; Louis W Catalano; Steven Z Glickel; Jacques H Hacquebord
Journal:  Hand (N Y)       Date:  2019-02-27

2.  Neural Networks Modeling for Prediction of Required Resources for Personalized Endourologic Treatment of Urolithiasis.

Authors:  Clemens Huettenbrink; Wolfgang Hitzl; Sascha Pahernik; Jens Kubitz; Valentin Popeneciu; Jascha Ell
Journal:  J Pers Med       Date:  2022-05-12

3.  Reasons for operation cancellations at a teaching hospital: prioritizing areas of improvement.

Authors:  Mahmoud Abu Abeeleh; Tareq M Tareef; Amjad Bani Hani; Nader Albsoul; Omar Q Samarah; M S ElMohtaseb; Musa Alshehabat; Zuhair Bani Ismail; Omar Alnoubani; Salameh S Obeidat; Sami Abu Halawa
Journal:  Ann Surg Treat Res       Date:  2017-07-28       Impact factor: 1.859

4.  Elective Case Cancellation on the Day of Surgery at a General Hospital in Sarajevo: Causes and Possible Solutions.

Authors:  Amina Krupalija Solak; Haris Pandza; Edin Beciragic; Amila Husic; Ida Tursunovic; Harun Djozic
Journal:  Mater Sociomed       Date:  2019-03

5.  Quo Vadis Anesthesiologist? The Value Proposition of Future Anesthesiologists Lies in Preserving or Restoring Presurgical Health after Surgical Insult.

Authors:  Krzysztof Laudanski
Journal:  J Clin Med       Date:  2022-02-21       Impact factor: 4.241

  5 in total

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