TY - GEN
T1 - Itinerary predictive analytics
T2 - 2023 Design of Medical Devices Conference, DMD 2023
AU - Damani, Shivam
AU - Gopalakrishnan, Keerthy
AU - Aedma, Keirthana
AU - Muddaloor, Pratyusha
AU - Chandrasekhara, Vinay
AU - Ryu, Alexander J.
AU - Aakre, Christopher A.
AU - Arunachalam, Shivaram P.
N1 - Publisher Copyright:
© 2023 by ASME.
PY - 2023
Y1 - 2023
N2 - Majority of hospitals still utilize manual methods for patient scheduling and predicting future appointments, resulting in longer wait times, hospital burnout and inadequate use of resources. A variety of avenues have been explored, including priority patient routing, tele-health, neural networks for improving ER efficiency, predicting no-shows, consultation duration variations, and optimizing operating room utilization. Addressing this issue, a study was conducted using 700 pre-visit notes of pancreatic patients to determine the requirement of endoscopic or biliary procedure. Through natural language processing and traditional or transfer learning algorithms, data could directly be sent to EPIC for nurses to assess in further decision making. Performance of the models was above average with the transfer learning method outperforming the traditional method. Although limited by less dataset and fewer circumstances to test the models on, the results exposed potential for future development with the possibility of patients reporting their chief concerns, in turn analyzed by algorithms, ultimately creating a smooth and effective patient itinerary.
AB - Majority of hospitals still utilize manual methods for patient scheduling and predicting future appointments, resulting in longer wait times, hospital burnout and inadequate use of resources. A variety of avenues have been explored, including priority patient routing, tele-health, neural networks for improving ER efficiency, predicting no-shows, consultation duration variations, and optimizing operating room utilization. Addressing this issue, a study was conducted using 700 pre-visit notes of pancreatic patients to determine the requirement of endoscopic or biliary procedure. Through natural language processing and traditional or transfer learning algorithms, data could directly be sent to EPIC for nurses to assess in further decision making. Performance of the models was above average with the transfer learning method outperforming the traditional method. Although limited by less dataset and fewer circumstances to test the models on, the results exposed potential for future development with the possibility of patients reporting their chief concerns, in turn analyzed by algorithms, ultimately creating a smooth and effective patient itinerary.
KW - Deep Learning
KW - Itinerary Prediction
KW - Natural Language Processing
KW - Patient Scheduling
KW - Patient Visits
UR - http://www.scopus.com/inward/record.url?scp=85165027504&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85165027504&partnerID=8YFLogxK
U2 - 10.1115/DMD2023-1597
DO - 10.1115/DMD2023-1597
M3 - Conference contribution
AN - SCOPUS:85165027504
T3 - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
BT - Proceedings of the 2023 Design of Medical Devices Conference, DMD 2023
PB - American Society of Mechanical Engineers
Y2 - 17 April 2023 through 21 April 2023
ER -