A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery.
Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durati...
| Published in: | Journal of Medical Systems Vol. 43; no. 2; pp. 1 - 2 |
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| Main Authors: | , , , |
| Format: | research tables/charts Journal Article |
| Published: |
Springer Nature
Feb2019
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=134561917&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 134561917 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01485598 4N0 jtl: Journal of Medical Systems issn: 01485598 maglogo: N pubinfo: dt: Feb2019 vid: 43 iid: 2 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 134561917 134561917 134561917 10.1007/s10916-018-1151-y 134561917 ppf: 1 ppct: 1 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A Machine Learning Approach to Predicting Case Duration for Robot-Assisted Surgery. aug: au: Zhao, Beiqun Waterman, Ruth S. Urman, Richard D. Gabriel, Rodney A. affil: Department of Surgery, University of California, San Diego, 9300 Campus Point Drive, #7220, 92037, La Jolla, CA, USA sug: subj: Machine Learning Robotic Surgical Procedures Utilization Human Time Factors Operating Rooms Random Sample Linear Regression Decision Trees Confidence Intervals Data Analysis Software Male Female Case Studies Funding Source Male Female ab: Robot-assisted surgery (RAS) requires a large capital investment by healthcare organizations. The cost of a robotic unit is fixed, so institutions must maximize use of each unit by utilizing all available operating room block time. One way to increase utilization is to accurately predict case durations. In this study, we sought to use machine learning to develop an accurate predictive model for RAS case duration. We analyzed a random sample of robotic cases at our institution from January 2014 to June 2017. We compared the machine learning models to the baseline model, which is the scheduled case duration (determined by previous case duration averages and surgeon adjustments). Specifically, we used: 1) multivariable linear regression, 2) ridge regression, 3) lasso regression, 4) random forest, 5) boosted regression tree, and 6) neural network. We found that all machine learning models decreased the average root-mean-squared error (RMSE) as compared to the baseline model. The average RMSE was lowest with the boosted regression tree (80.2 min, 95% CI 74.0-86.4), which was significantly lower than the baseline model (100.4 min, 95% CI 90.5-110.3). Using boosted regression tree, we can increase the number of accurately booked cases from 148 to 219 (34.9% to 51.7%, p < 0.001). This study shows that using various machine learning approaches can improve the accuracy of RAS case length predictions, which will increase utilization of this limited resource. Further work is needed to operationalize these findings. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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