2017
DOI: 10.3389/fmed.2017.00085
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Improving the Prediction of Total Surgical Procedure Time Using Linear Regression Modeling

Abstract: For efficient utilization of operating rooms (ORs), accurate schedules of assigned block time and sequences of patient cases need to be made. The quality of these planning tools is dependent on the accurate prediction of total procedure time (TPT) per case. In this paper, we attempt to improve the accuracy of TPT predictions by using linear regression models based on estimated surgeon-controlled time (eSCT) and other variables relevant to TPT. We extracted data from a Dutch benchmarking database of all surgeri… Show more

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Cited by 35 publications
(38 citation statements)
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“…Master et al compared multiple machine learning (ML) techniques including decision tree regression, random forest regression, and gradient boosted regression trees as well as hybrid combinations to predict case durations. (10) However, the models were trained on only 10 operations within a single specialty, thus limiting their generalizability (22) The majority of studies aimed at improving surgical case-time estimates have focused on a single subspecialty, which provides limited utility for a clinical administrator managing the entire set of operating room suites. Moreover, many of the models did not restrict the model inputs to only pre-operatively available information, potentially leading to lower accuracy in a prospective implementation.…”
Section: Introductionmentioning
confidence: 99%
“…Master et al compared multiple machine learning (ML) techniques including decision tree regression, random forest regression, and gradient boosted regression trees as well as hybrid combinations to predict case durations. (10) However, the models were trained on only 10 operations within a single specialty, thus limiting their generalizability (22) The majority of studies aimed at improving surgical case-time estimates have focused on a single subspecialty, which provides limited utility for a clinical administrator managing the entire set of operating room suites. Moreover, many of the models did not restrict the model inputs to only pre-operatively available information, potentially leading to lower accuracy in a prospective implementation.…”
Section: Introductionmentioning
confidence: 99%
“…For example, Devi et al 3 considered only three types of elective surgeries in the ophthalmology department of a hospital, namely, cataract surgery, corneal transplant surgery and oculoplastic surgery. However, Edelman et al 5 considered all elective and emergency surgeries occurring across six hospitals.…”
mentioning
confidence: 99%
“…There are various examples of how linear regression has been used in the healthcare setting. A study titled "Improving the prediction of total surgical procedure time using linear regression modeling, " [16] also used linear regression to create a predictive model to optimize service TAT in a clinical setting. In that study the linear regression model gave the most accurate value for the predicted procedure time.…”
Section: Discussionmentioning
confidence: 99%
“…With a 77% accuracy for the predicted time, the result of that study is similar to the accuracy of the predicted TAT in this study. This can be used as an effective reference to use similar linear regression models for the prediction of TAT of similar administrative and clinical processes and procedures [16].…”
Section: Discussionmentioning
confidence: 99%