Literature DB >> 26958199

Surgical Duration Estimation via Data Mining and Predictive Modeling: A Case Study.

N Hosseini1, M Y Sir1, C J Jankowski2, K S Pasupathy1.   

Abstract

Operating rooms (ORs) are one of the most expensive and profitable resources within a hospital system. OR managers strive to utilize these resources in the best possible manner. Traditionally, surgery durations are estimated using a moving average adjusted by the scheduler (adjusted system prediction or ASP). Other methods based on distributions, regression and data mining have also been proposed. To overcome difficulties with numerous procedure types and lack of sufficient sample size, and avoid distributional assumptions, the main objective is to develop a hybrid method of duration prediction and demonstrate using a case study.

Keywords:  Classification; hybrid method; prediction; regression; surgery times

Mesh:

Year:  2015        PMID: 26958199      PMCID: PMC4765628     

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


  13 in total

1.  Surgeon and type of anesthesia predict variability in surgical procedure times.

Authors:  D P Strum; A R Sampson; J H May; L G Vargas
Journal:  Anesthesiology       Date:  2000-05       Impact factor: 7.892

2.  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

3.  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

4.  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

5.  Bayesian prediction bounds and comparisons of operating room times even for procedures with few or no historic data.

Authors:  Franklin Dexter; Johannes Ledolter
Journal:  Anesthesiology       Date:  2005-12       Impact factor: 7.892

6.  Estimating the duration of common elective operations: implications for operating list management.

Authors:  J J Pandit; A Carey
Journal:  Anaesthesia       Date:  2006-08       Impact factor: 6.955

7.  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

8.  Operating room scheduling data base analysis for scheduling.

Authors:  W M Hancock; P F Walter; R A More; N D Glick
Journal:  J Med Syst       Date:  1988-12       Impact factor: 4.460

9.  Scheduling a multiple operating room system: a simulation approach.

Authors:  S Barnoon; H Wolfe
Journal:  Health Serv Res       Date:  1968       Impact factor: 3.402

10.  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

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  5 in total

1.  An Evaluation of the Hybrid Model for Predicting Surgery Duration.

Authors:  K W Soh; C Walker; M O'Sullivan; J Wallace
Journal:  J Med Syst       Date:  2020-01-02       Impact factor: 4.460

2.  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

3.  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

4.  Machine Learning-Based Models Predicting Outpatient Surgery End Time and Recovery Room Discharge at an Ambulatory Surgery Center.

Authors:  Rodney A Gabriel; Bhavya Harjai; Sierra Simpson; Nicole Goldhaber; Brian P Curran; Ruth S Waterman
Journal:  Anesth Analg       Date:  2022-04-07       Impact factor: 6.627

5.  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
  5 in total

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