Offering transportation services to economically disadvantaged patients at a family health center: a case study.

It has been established that high no-show rates of publicly supported health systems in economically depressed areas are largely due to a lack of inexpensive, reliable transportation. The purpose of this paper is to determine the financial feasibility of offering transportation and investigate the net cost savings by reducing no-show rates. The approach starts with a data analysis on 636 patients at the Family Health Center (FHC) in San Antonio, Texas, followed by logistic regression to determine the impact of various transportation factors on cancellations/no-shows and late arrivals. We then investigate the costs savings that could be realised by reducing the no-show rate from 24.3% by up to 60%. Finally, we analyse the expenses that would be incurred should the FHC provide transportation. The full analysis indicates a cost reduction of more than $15,000 per month can be achieved when the no-show rate is reduced by 25% down to 18.2%.

Designing and scheduling a multi-disciplinary integrated practice unit for patient-centred care.

This paper presents the design and analysis of a newly proposed form of care delivery called an integrated practice unit (IPU) in which a multi-disciplinary team of providers and staff work together to cover the full care cycle for a given condition. In an IPU, the different providers circulate among the patients, according to the need for their expertise, while patients remain in a single location once they check-in. From the patient's perspective, the benefits of such an arrangement should be self-evident. For payers and providers there will also be benefits as the fee-for-service market gives way to structured payments for each episode of care. Before setting up an IPU, it is necessary to gain an understanding of how available resources will limit patient flow and system performance. Treating resources such as providers, imaging equipment, and rooms parametrically, the primary goal of our work is to determine the number of patients that can be seen per day in an IPU while trying to constrain overtime, length of stay, and waiting time to best practice targets. Discrete-event simulation serves as our analytic tool. While we are involved in the design of a comprehensive suite of musculoskeletal IPUs, we illustrate our approach with an extensive computational study of one: a Lower Extremity Joint Pain IPU. Using the simulation methodology, we are not only able to determine the number of patients that can be scheduled for an in-clinic visit each day, but also the daily number of follow-up patients that can be served virtually through telemedicine with no additional resources and minimal impact on IPU performance. These results assisted the Department of Surgery at the Dell Medical School at The University of Texas in the optimal design of its first IPU, which opened in the fall of 2017.