Paper Info
Title
Using Operative Features To Identify Surgical Complexity: A Case In Breast Surgery Practice
Abstract
Increasing workload is one of the main problems that surgical practices face. This increase is not only due to the increasing demand volume but also due to increasing case complexity. This raises the question on how to measure and predict the complexity to address this issue. Predicting surgical duration is critical to parametrize surgical complexity, improve surgeon satisfaction by avoiding unexpected overtime, and improve operation room utilization. Our objective is to utilize the historical data on surgical operations to obtain complexity groups and use this groups to improve practice.Our study first leverages expert opinion on the surgical complexity to identify surgical groups. Then, we use a tree-based method on a large retrospective dataset to identify similar complexity groups by utilizing the surgical features and using surgical duration as a response variable. After obtaining the surgical groups by using two methods, we statistically compare expert-based grouping with the data-based grouping. This comparison shows that a tree-based method can provide complexity groups similar to the ones generated by an expert by using features that are available at the time of surgical listing. These results suggest that one can take advantage of available data to provide surgical duration predictions that are data-driven, evidence-based, and practically relevant.
Year
DOI
Venue
2020
10.1109/EMBC44109.2020.9176030
42ND ANNUAL INTERNATIONAL CONFERENCES OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY: ENABLING INNOVATIVE TECHNOLOGIES FOR GLOBAL HEALTHCARE EMBC'20
DocType
Volume
ISSN
Conference
2020
1557-170X
Citations 
PageRank 
References 
0
0.34
0
Authors
7
Name
Order
Citations
PageRank
Derya Kilinc100.34
Narges Shahraki200.34
Esma S Gel300.34
Amy C Degnim400.34
Tanya Hoskin500.34
Mustafa Sir6429.57
Kalyan S. Pasupathy7219.19