Helping the Captive Audience: Advance Notice of Diagnostic Service for Hospital Inpatients

Miao Bai (), Nan Liu () and Zheng Zhang ()
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Miao Bai: Department of Operations and Information Management, University of Connecticut, Storrs, Connecticut 06269
Nan Liu: Carroll School of Management, Boston College, Chestnut Hill, Massachusetts 02467
Zheng Zhang: School of Management, Zhejiang University, Hangzhou 310058, China

Manufacturing & Service Operations Management, 2025, vol. 27, issue 4, 1313-1331

Abstract: Problem definition : Inpatients are often treated as the “captive audience” for hospital diagnostic services, and they are notified only when service capacity is available. This arrangement causes significant chaos and inefficiencies in hospital operations. Methodology/results : We propose an innovative scheduling approach called “advance notice” to manage hospital diagnostic practices. Advance notice is a brand-new scheduling paradigm in between classic allocation scheduling and advance scheduling. Patients are placed in a common queue waiting to be called for service, and they will be provided both a fixed preparation time and a guaranteed service time window in advance. The advance notice policy enjoys the benefit of allocation scheduling (giving the provider flexibility in using her capacity) and that of advance scheduling (reducing patient online waiting). It calls for two decisions: who to serve now and who to send advance notices to. We formulate a Markov decision process model to optimize these decisions dynamically. Via a novel variable transformation, we reveal the hidden anti-multimodular structure of the problem and show how the optimal decisions should be adjusted in response to changes in the system load. Beyond solving the model for daily operations, we further investigate how the service and preparation time windows as system-level controls can be used to manage system performance. Our numerical study, populated by real data from a large academic medical center in the United States, demonstrates significant improvement in operational efficiency by switching from current practice to our proposed advance notice policy. Managerial implications : The advance notice policy strikes a fine balance between the two classic scheduling paradigms; it gives the provider flexibility in using her capacity as in allocation scheduling, and it reduces patient online waiting as in advance scheduling. Although it is motivated by healthcare applications, the advance notice policy offers a promising cutting-edge approach to improving general appointment-based services.

Keywords: advance notice; Markov decision process; data-driven modeling; healthcare management (search for similar items in EconPapers)
Date: 2025
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