Literature DB >> 10781292

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

D P Strum1, A R Sampson, J H May, L G Vargas.   

Abstract

BACKGROUND: Variability in surgical procedure times increases the cost of healthcare delivery by increasing both the underutilization and overutilization of expensive surgical resources. To reduce variability in surgical procedure times, we must identify and study its sources.
METHODS: Our data set consisted of all surgeries performed over a 7-yr period at a large teaching hospital, resulting in 46,322 surgical cases. To study factors associated with variability in surgical procedure times, data mining techniques were used to segment and focus the data so that the analyses would be both technically and intellectually feasible. The data were subdivided into 40 representative segments of manageable size and variability based on headers adopted from the common procedural terminology classification. Each data segment was then analyzed using a main-effects linear model to identify and quantify specific sources of variability in surgical procedure times.
RESULTS: The single most important source of variability in surgical procedure times was surgeon effect. Type of anesthesia, age, gender, and American Society of Anesthesiologists risk class were additional sources of variability. Intrinsic case-specific variability, unexplained by any of the preceding factors, was found to be highest for shorter surgeries relative to longer procedures. Variability in procedure times among surgeons was a multiplicative function (proportionate to time) of surgical time and total procedure time, such that as procedure times increased, variability in surgeons' surgical time increased proportionately.
CONCLUSIONS: Surgeon-specific variability should be considered when building scheduling heuristics for longer surgeries. Results concerning variability in surgical procedure times due to factors such as type of anesthesia, age, gender, and American Society of Anesthesiologists risk class may be extrapolated to scheduling in other institutions, although specifics on individual surgeons may not. This research identifies factors associated with variability in surgical procedure times, knowledge of which may ultimately be used to improve surgical scheduling and operating room utilization.

Mesh:

Year:  2000        PMID: 10781292     DOI: 10.1097/00000542-200005000-00036

Source DB:  PubMed          Journal:  Anesthesiology        ISSN: 0003-3022            Impact factor:   7.892


  25 in total

1.  A data mining approach to characterizing medical code usage patterns.

Authors:  William E Spangler; Jerrold H May; David P Strum; Luis G Vargas
Journal:  J Med Syst       Date:  2002-06       Impact factor: 4.460

2.  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

3.  The Impact of Overestimations of Surgical Control Times Across Multiple Specialties on Medical Systems.

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Journal:  J Med Syst       Date:  2016-02-10       Impact factor: 4.460

4.  Mean operating room times differ by 50% among hospitals in different countries for laparoscopic cholecystectomy and lung lobectomy.

Authors:  Franklin Dexter; Melinda Davis; Christoph B Egger Halbeis; Christoph E Halbeis; Riita Marjamaa; Jean Marty; Catherine McIntosh; Yoshinori Nakata; Kokila N Thenuwara; Tomohiro Sawa; Michael Vigoda
Journal:  J Anesth       Date:  2006       Impact factor: 2.078

5.  How do strategic decisions and operative practices affect operating room productivity?

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Journal:  Health Care Manag Sci       Date:  2011-08-04

6.  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

7.  Accuracy of patient's turnover time prediction using RFID technology in an academic ambulatory surgery center.

Authors:  Florence Marchand-Maillet; Claire Debes; Fanny Garnier; Nicolas Dufeu; Didier Sciard; Marc Beaussier
Journal:  J Med Syst       Date:  2015-01-31       Impact factor: 4.460

8.  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

9.  Observational study of operating room times for knee and hip replacement surgery at nine U.S. community hospitals.

Authors:  Franklin Dexter; Lori S Weih; Ross K Gustafson; Linda F Stegura; Mary J Oldenkamp; Ruth E Wachtel
Journal:  Health Care Manag Sci       Date:  2006-11

10.  Anticipation, teamwork and cognitive load: chasing efficiency during robot-assisted surgery.

Authors:  Kevin Sexton; Amanda Johnson; Amanda Gotsch; Ahmed A Hussein; Lora Cavuoto; Khurshid A Guru
Journal:  BMJ Qual Saf       Date:  2017-07-08       Impact factor: 7.035

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