Literature DB >> 18349199

Systematic review of general thoracic surgery articles to identify predictors of operating room case durations.

Franklin Dexter1, Elisabeth U Dexter, Danielle Masursky, Nancy A Nussmeier.   

Abstract

BACKGROUND: Previous studies of operating room (OR) information systems data over the past two decades have shown how to predict case durations using the combination of scheduled procedure(s), individual surgeon and assistant(s), and type of anesthetic(s). We hypothesized that the accuracy of case duration prediction could be improved by the use of other electronic medical record data (e.g., patient weight or surgeon notes using standardized vocabularies).
METHODS: General thoracic surgery was used as a model specialty because much of its workload is elective (scheduled) and many of its cases are long. PubMed was searched for thoracic surgery papers reporting operative time, surgical time, etc. The systematic literature review identified 48 papers reporting statistically significant differences in perioperative times.
RESULTS: There were multiple reports of differences in OR times based on the procedure(s), perioperative team including primary surgeon, and type of anesthetic, in that sequence of importance. All such detail may not be known when the case is originally scheduled and thus may require an updated duration the day before surgery. Although the use of these categorical data from OR systems can result in few historical data for estimating each case's duration, bias and imprecision of case duration estimates are unlikely to be affected. There was a report of a difference in case duration based on additional information. However, the incidence of the procedure for the diagnosis was so uncommon as to be unlikely to affect OR management.
CONCLUSIONS: Matching findings of prior studies using OR information system data, multiple case series show that it is important to rely on the precise procedure(s), surgical team, and type of anesthetic when estimating case durations. OR information systems need to incorporate the statistical methods designed for small numbers of prior surgical cases. Future research should focus on the most effective methods to update the prediction of each case's duration as these data become available. The case series did not reveal additional data which could be cost-effectively integrated with OR information systems data to improve the accuracy of predicted durations for general thoracic surgery cases.

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Year:  2008        PMID: 18349199     DOI: 10.1213/ane.0b013e318164f0d5

Source DB:  PubMed          Journal:  Anesth Analg        ISSN: 0003-2999            Impact factor:   5.108


  11 in total

1.  A robust estimation model for surgery durations with temporal, operational, and surgery team effects.

Authors:  Enis Kayış; Taghi T Khaniyev; Jaap Suermondt; Karl Sylvester
Journal:  Health Care Manag Sci       Date:  2014-12-14

2.  Single versus multi-specialty operative teams: association with perioperative mortality after endovascular abdominal aortic aneurysm repair.

Authors:  Laura M Mazer; Elliot L Chiakof; Philip P Goodney; Matthew S Edwards; Matthew A Corriere
Journal:  Am Surg       Date:  2012-02       Impact factor: 0.688

3.  Improving prediction of surgery duration using operational and temporal factors.

Authors:  Enis Kayis; Haiyan Wang; Meghna Patel; Tere Gonzalez; Shelen Jain; R J Ramamurthi; Cipriano Santos; Sharad Singhal; Jaap Suermondt; Karl Sylvester
Journal:  AMIA Annu Symp Proc       Date:  2012-11-03

4.  Improving Operating Room Efficiency: Machine Learning Approach to Predict Case-Time Duration.

Authors:  Matthew A Bartek; Rajeev C Saxena; Stuart Solomon; Christine T Fong; Lakshmana D Behara; Ravitheja Venigandla; Kalyani Velagapudi; John D Lang; Bala G Nair
Journal:  J Am Coll Surg       Date:  2019-07-13       Impact factor: 6.113

5.  Speed and quality in coronary artery bypass graft (CABG) surgery: is there a connection?

Authors:  Juha-Matti Lehtonen; Mikko Hippeläinen; Eija Kattainen; Juhani Kouri; Jaakko Kujala
Journal:  Health Care Manag Sci       Date:  2009-06

6.  The use of patient factors to improve the prediction of operative duration using laparoscopic cholecystectomy.

Authors:  Cornelius A Thiels; Denny Yu; Amro M Abdelrahman; Elizabeth B Habermann; Susan Hallbeck; Kalyan S Pasupathy; Juliane Bingener
Journal:  Surg Endosc       Date:  2016-07-06       Impact factor: 4.584

7.  Event-based knowledge elicitation of operating room management decision-making using scenarios adapted from information systems data.

Authors:  Franklin Dexter; Ruth E Wachtel; Richard H Epstein
Journal:  BMC Med Inform Decis Mak       Date:  2011-01-07       Impact factor: 2.796

8.  Determinants of operative time in thyroid surgery: A prospective multicenter study of 3454 thyroidectomies.

Authors:  Arnaud Patoir; Cécile Payet; Jean-Louis Peix; Cyrille Colin; Léa Pascal; Jean-Louis Kraimps; Fabrice Menegaux; François Pattou; Frédéric Sebag; Sandrine Touzet; Stéphanie Bourdy; Jean-Christophe Lifante; Antoine Duclos
Journal:  PLoS One       Date:  2017-07-27       Impact factor: 3.240

9.  Clinical and Nonclinical Effects on Operative Duration: Evidence from a Database on Thoracic Surgery.

Authors:  Jin Wang; Javier Cabrera; Kwok-Leung Tsui; Hainan Guo; Monique Bakker; John B Kostis
Journal:  J Healthc Eng       Date:  2020-02-10       Impact factor: 2.682

10.  The development and validation of a scoring tool to predict the operative duration of elective laparoscopic cholecystectomy.

Authors:  Reshma Bharamgoudar; Aniket Sonsale; James Hodson; Ewen Griffiths
Journal:  Surg Endosc       Date:  2018-01-16       Impact factor: 4.584

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