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Original Articles

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

ORCID Icon, ORCID Icon &
Pages 293-316 | Received 02 Jun 2018, Accepted 24 Dec 2018, Published online: 17 Feb 2019
 

ABSTRACT

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.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Dell Medical School [Texas Health Catalyst Program]; Simio Academic Software Licence [Institution Grant]; McCombs School of Business, The University of Texas at Austin [Research Grant].

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