Paper Info
Title
Predictive Modeling To Identify Scheduled Radiology Appointments Resulting In Non-Attendance In A Hospital Setting
Abstract
No-show appointments are a troublesome, but frequent, occurrence in radiology hospital departments and private practice. Prior work in medical appointment no-show prediction has focused on general practice and has not considered features specific to the radiology environment. We collect data from 16 years of outpatient examinations in a multi-site hospital radiology department. Data from the radiology information system (RIS) are fused with patient income estimated from U.S. Census data. Features were categorized into three groups: Patient, Exam, and Scheduling. Models based on the total feature set and separately on each feature group were developed using logistic regression to assess the per-appointment likelihood of no-show. After five-fold cross-validation, no-show prediction using the total feature set from 554,611 appointments yielded an area under the curve (AUC) of 0.770 +/- 0.003. Feature groups that were most informative in the prediction of no-show appointments were those based on the type of exam and on scheduling attributes such as the lead time of scheduling the appointment. A data-driven no-show prediction model like the one presented here could be useful to schedulers in the implementation of an automated scheduling policy or the assignment of examinations with a high risk of no-show to lower impact appointment slots.
Year
DOI
Venue
2017
10.1109/EMBC.2017.8037394
2017 39TH ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY (EMBC)
Field
DocType
Volume
Scheduling (computing),Computer science,Radiology information systems,General practice,Feature set,Lead time,Radiology,Attendance,Logistic regression
Conference
2017
ISSN
Citations 
PageRank 
1094-687X
0
0.34
References 
Authors
1
4
Name
Order
Citations
PageRank
Rebecca J Mieloszyk132.22
Joshua I. Rosenbaum200.34
Puneet Bhargava300.68
Christopher S. Hall455.56