Literature DB >> 23304316

Improving prediction of surgery duration using operational and temporal factors.

Enis Kayis1, Haiyan Wang, Meghna Patel, Tere Gonzalez, Shelen Jain, R J Ramamurthi, Cipriano Santos, Sharad Singhal, Jaap Suermondt, Karl Sylvester.   

Abstract

Inherent uncertainties in surgery durations impact many critical metrics about the performance of an operating room (OR) environment. OR schedules that are robust to natural variability in surgery durations require surgery duration estimates that are unbiased, with high accuracy, and with few cases with large absolute errors. Earlier studies have shown that factors such as patient severity, personnel, and procedure type greatly affect the accuracy of such estimations. In this paper we investigate whether operational and temporal factors can be used to improve these estimates further. We present an adjustment method based on a combination of these operational and temporal factors. We validate our method with two years of detailed operational data from an electronic medical record. We conclude that while improving estimates of surgery durations is possible, the inherent variability in such estimates remains high, necessitating caution in their use when optimizing OR schedules.

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Year:  2012        PMID: 23304316      PMCID: PMC3540440     

Source DB:  PubMed          Journal:  AMIA Annu Symp Proc        ISSN: 1559-4076


  9 in total

1.  Estimating times of surgeries with two component procedures: comparison of the lognormal and normal models.

Authors:  David P Strum; Jerrold H May; Allan R Sampson; Luis G Vargas; William E Spangler
Journal:  Anesthesiology       Date:  2003-01       Impact factor: 7.892

2.  Comparison of statistical methods to predict the time to complete a series of surgical cases.

Authors:  F Dexter; R D Traub; F Qian
Journal:  J Clin Monit Comput       Date:  1999-01       Impact factor: 2.502

3.  Estimating procedure times for surgeries by determining location parameters for the lognormal model.

Authors:  William E Spangler; David P Strum; Luis G Vargas; Jerrold H May
Journal:  Health Care Manag Sci       Date:  2004-05

4.  Factors that influence the expected length of operation: results of a prospective study.

Authors:  Brigid M Gillespie; Wendy Chaboyer; Nicole Fairweather
Journal:  BMJ Qual Saf       Date:  2011-10-14       Impact factor: 7.035

5.  Estimating the duration of a case when the surgeon has not recently scheduled the procedure at the surgical suite.

Authors:  A Macario; F Dexter
Journal:  Anesth Analg       Date:  1999-11       Impact factor: 5.108

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

Authors:  Franklin Dexter; Elisabeth U Dexter; Danielle Masursky; Nancy A Nussmeier
Journal:  Anesth Analg       Date:  2008-04       Impact factor: 5.108

7.  Surgical time independently affected by surgical team size.

Authors:  Maria A Cassera; Bin Zheng; Danny V Martinec; Christy M Dunst; Lee L Swanström
Journal:  Am J Surg       Date:  2009-03-12       Impact factor: 2.565

8.  Modeling procedure and surgical times for current procedural terminology-anesthesia-surgeon combinations and evaluation in terms of case-duration prediction and operating room efficiency: a multicenter study.

Authors:  Pieter S Stepaniak; Christiaan Heij; Guido H H Mannaerts; Marcel de Quelerij; Guus de Vries
Journal:  Anesth Analg       Date:  2009-10       Impact factor: 5.108

9.  Predicting the unpredictable: a new prediction model for operating room times using individual characteristics and the surgeon's estimate.

Authors:  Marinus J C Eijkemans; Mark van Houdenhoven; Tien Nguyen; Eric Boersma; Ewout W Steyerberg; Geert Kazemier
Journal:  Anesthesiology       Date:  2010-01       Impact factor: 7.892

  9 in total
  3 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.  Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study.

Authors:  N Hosseini; M Y Sir; C J Jankowski; K S Pasupathy
Journal:  AMIA Annu Symp Proc       Date:  2015-11-05

3.  Estimation of Surgery Durations Using Machine Learning Methods-A Cross-Country Multi-Site Collaborative Study.

Authors:  Sean Shao Wei Lam; Hamed Zaribafzadeh; Boon Yew Ang; Wendy Webster; Daniel Buckland; Christopher Mantyh; Hiang Khoon Tan
Journal:  Healthcare (Basel)       Date:  2022-06-25
  3 in total

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