Patient Flow Analysis Using Real-Time Locating System Data: A Case Study in an Outpatient Oncology Center.

Abstract

Electronic health records (EHRs) have been mainly used to analyze bottlenecks in care processes of outpatient oncology clinics. However, EHR data lead to some limitations in understanding patient flow because they are manually entered and not updated in real time. Data generated from a real-time location system (RTLS) can supplement EHR data. This study aims to demonstrate how RTLS data combined with EHR data can be used to evaluate potential interventions to improve patient flow in an outpatient cancer center. EHR and RTLS data obtained from a large cancer center in central Virginia were analyzed to estimate process times and determine the various patient paths patients follow during their visit for infusion. Using the input data, we developed a discrete-event simulation (DES) model and assessed 5 what-if scenarios involving changes in staff scheduling and care processes. Raw RTLS data including > 3.5 million observations were preprocessed to remove noise and extract meaningful information. The DES results showed that new nursing schedules for the infusion center and improved pharmacy processes have positive impacts on reducing patient waiting times by approximately 20% and overall length of stay by approximately 3.4% to 4.6%, compared with the current system. Combining EHR and RTLS data, we were able to capture dynamic aspects of patient flow more realistically. DES models that represent a complex system based on accurate input data can help decision making on determining operational changes to improve patient flow.