Application of Real-Time Clinic Data and Patient Surveys to Patient Flow Modeling in an Outpatient Oncology Center 424 views

Even simple outpatient health clinics present operational challenges, and patient flow generally involves projecting and coordinating scheduled and unscheduled patient arrivals. Outpatient oncology centers are even more complex, as patients generally utilize a number of services during a single visit. Previous studies in this domain have focused their efforts on improving patient waiting times while reducing physician idle time. This study uses stakeholder interviews to identify a number of relevant performance indicators. Responses suggest that, while waiting times and utilization are still important to stakeholders, other indicators are needed to fully reflect the concerns of patients. In addition, previous studies that developed patient flow and scheduling models for a cancer center have mainly used the electronic health record (EHR) as an input data source. Recently, an increasing number of healthcare facilities have adopted a real-time locating system (RTLS). This study shows how RTLS data can help better capture dynamic patient flow and system states and be utilized to develop more accurate models. In combination with other data, it is used as input in a linear programming model for staff schedules and a discrete event simulation (DES) of patient flow. A number of improvement scenarios are tested using the DES, and the results are assessed using the indicators identified as important in the surveys of patients and staff. The DES results suggests that the facility can improve patient flow and patient satisfaction by reviewing nurse staffing schedules and identifying efficiency-improving changes to the pharmacy. The patient survey showed that doing so would also improve patient satisfaction. The use of data from stakeholder surveys and RTLS supplemented the EHR at all stages, and permitted the study to more directly measure clinic processes and create more accurate models and recommendations than would otherwise be possible.

MS (Master of Science)

outpatient oncology center; discrete event simulation; linear programming; systems engineering

English

2018-11-27