Literature DB >> 9822012

Method to assist in the scheduling of add-on surgical cases--upper prediction bounds for surgical case durations based on the log-normal distribution.

J Zhou1, F Dexter.   

Abstract

BACKGROUND: A problem that operating room (OR) managers face in running an OR suite on the day of surgery is to identify "holes" in the OR schedule in which to assign "add-on" cases. This process necessitates knowing the typical and maximum amounts of time that the case is likely to require. The OR manager may know previous case durations for the particular surgeon performing a particular scheduled procedure. The "upper prediction bound" specifies with a certain probability that the duration of the surgeon's next case will be less than or equal to the bound.
METHODS: Prediction bounds were calculated by using methods that (1) do not assume that case durations follow a specific statistical distribution or (2) assume that case durations follow a log-normal distribution. These bounds were tested using durations of 48,847 cases based on 15,574 combinations of scheduled surgeon and procedure.
RESULTS: Despite having 3 yr of data, 80 or 90% prediction bounds would not be able to be calculated using the distribution-free method for 35 or 49% of future cases versus 22 or 22% for the log-normal method, respectively. Prediction bounds based on the log-normal distribution overestimated the desired value less often than did the distribution-free method. The chance that the duration of the next case would be less than or equal to its 90% bound based on the log-normal distribution was within 2% of the expected rate.
CONCLUSIONS: Prediction bounds classified by scheduled surgeon and procedure can be accurately calculated using a method that assumes that case durations follow a log-normal distribution.

Mesh:

Year:  1998        PMID: 9822012     DOI: 10.1097/00000542-199811000-00024

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


  7 in total

1.  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

2.  Optimal sequencing of urgent surgical cases. Scheduling cases using operating room information systems.

Authors:  F Dexter; A Macario; R D Traub
Journal:  J Clin Monit Comput       Date:  1999-05       Impact factor: 2.502

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

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Journal:  Health Care Manag Sci       Date:  2004-05

4.  Impact of surgical sequencing on post anesthesia care unit staffing.

Authors:  Eric Marcon; Franklin Dexter
Journal:  Health Care Manag Sci       Date:  2006-02

5.  Two-MILP models for scheduling elective surgeries within a private healthcare facility.

Authors:  Hejer Khlif Hachicha; Farah Zeghal Mansour
Journal:  Health Care Manag Sci       Date:  2016-11-05

6.  Continuous real-time prediction of surgical case duration using a modular artificial neural network.

Authors:  York Jiao; Bing Xue; Chenyang Lu; Michael S Avidan; Thomas Kannampallil
Journal:  Br J Anaesth       Date:  2022-01-26       Impact factor: 11.719

7.  Association of Reduced Delay in Care With a Dedicated Operating Room in Pediatric Otolaryngology.

Authors:  Andrew J Redmann; Kyle Robinette; Charles M Myer; Alessandro de Alarcón; Aimee Veid; Catherine K Hart
Journal:  JAMA Otolaryngol Head Neck Surg       Date:  2018-04-01       Impact factor: 6.223

  7 in total

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