TY - GEN
T1 - A systems approach to improving patient flow at UVA Cancer Center using Real-Time Locating System
AU - Ewing, Anna
AU - Rogus, Jordan
AU - Chintagunta, Prathibha
AU - Kraus, Logan
AU - Sabol, Morgan
AU - Kang, Hyojung
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/5/31
Y1 - 2017/5/31
N2 - Long wait times not only indicate costly inefficiencies for healthcare facilities, but they also influence patient satisfaction and outcomes. As healthcare systems transition from provider centric care to patient centric care, increasing efforts have been made to reduce patient waiting times. At the University of Virginia, the Emily Couric Clinical Cancer Center (ECCCC) has experienced a 30% growth in patients over the past 3 years, resulting in a visible increase in wait times. In an effort to reduce wait times, the ECCCC has recently adopted a Real-Time Locating System (RTLS) that monitors patients' and providers' transient locations throughout the facility. The objectives of this project were to 1) to develop a framework to utilize RTLS data with other electronic medical records (EMR), and 2) to demonstrate how combined data can be used to better understand the flow of patients, bottlenecks, and patient-provider interactions in order to improve ECCCC operations. We combined data sets from multiple sources and statistically analyzed the data from patient and provider perspectives. Results indicate that the East Waiting and first floor Waiting areas have the highest average wait times and thus were identified as bottlenecks. Other locations at the ECCCC such as the Registration area were found to have significantly high average dwell times. A regression model indicated that patients visiting the ECCCC in the mid-morning, 9 a.m. - 12 p.m., experienced longer length of stay than patients visiting at other times. Analysis of patient-provider interactions showed that providers are on average 48 minutes late to appointments. Recommendations include tailoring scheduling to prevent appointment delays and investigating processes such as registration. Future work includes intervention strategy testing through simulation of the entire multi-clinic ECCCC.
AB - Long wait times not only indicate costly inefficiencies for healthcare facilities, but they also influence patient satisfaction and outcomes. As healthcare systems transition from provider centric care to patient centric care, increasing efforts have been made to reduce patient waiting times. At the University of Virginia, the Emily Couric Clinical Cancer Center (ECCCC) has experienced a 30% growth in patients over the past 3 years, resulting in a visible increase in wait times. In an effort to reduce wait times, the ECCCC has recently adopted a Real-Time Locating System (RTLS) that monitors patients' and providers' transient locations throughout the facility. The objectives of this project were to 1) to develop a framework to utilize RTLS data with other electronic medical records (EMR), and 2) to demonstrate how combined data can be used to better understand the flow of patients, bottlenecks, and patient-provider interactions in order to improve ECCCC operations. We combined data sets from multiple sources and statistically analyzed the data from patient and provider perspectives. Results indicate that the East Waiting and first floor Waiting areas have the highest average wait times and thus were identified as bottlenecks. Other locations at the ECCCC such as the Registration area were found to have significantly high average dwell times. A regression model indicated that patients visiting the ECCCC in the mid-morning, 9 a.m. - 12 p.m., experienced longer length of stay than patients visiting at other times. Analysis of patient-provider interactions showed that providers are on average 48 minutes late to appointments. Recommendations include tailoring scheduling to prevent appointment delays and investigating processes such as registration. Future work includes intervention strategy testing through simulation of the entire multi-clinic ECCCC.
KW - Patient flow
KW - Patient tracking
KW - Real-Time Locating System
KW - Statistical analysis
UR - http://www.scopus.com/inward/record.url?scp=85025631900&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85025631900&partnerID=8YFLogxK
U2 - 10.1109/SIEDS.2017.7937727
DO - 10.1109/SIEDS.2017.7937727
M3 - Conference contribution
AN - SCOPUS:85025631900
T3 - 2017 Systems and Information Engineering Design Symposium, SIEDS 2017
SP - 259
EP - 264
BT - 2017 Systems and Information Engineering Design Symposium, SIEDS 2017
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2017 Systems and Information Engineering Design Symposium, SIEDS 2017
Y2 - 28 April 2017
ER -