Game Theory and Information Economics

Self Cite

The role of artificial intelligence (AI) in augmenting healthcare is expected to grow substantially in future decades. Current research in medical AI focuses on developing, validating, and implementing point‐level AI applications in an ad hoc manner. To harness the full power of AI to improve the patient experience and outcomes at a societal scale, however, requires a gestalt shift—with a systematic understanding of AI in the context of healthcare—and so results in its widespread adoption. This translates to four pillars of incorporating AI into healthcare workflow, including physician buy‐in, patient acceptance, provider investment, and payer support (the “4Ps”). To achieve these 4Ps, it is imperative to design AI‐augmented healthcare delivery systems in view of (1) how physicians integrate AI into their clinical practice and (2) how patients perceive the role of AI in healthcare delivery. This will in turn boost provider investment and payer support. In this paper, we draw from the literature to discuss a series of research questions, including barriers to physician buy‐in and patient acceptance, transparency and disclosure, service design, and strategies for increasing AI uptake. We shed light on the principles of purposeful design for AI‐augmented healthcare delivery systems and propose a research agenda for operations management scholars to consider as they continue to strengthen their engagement with both healthcare professionals and AI developers.

Section: Introductionmentioning

confidence: 78%

Designing AI‐augmented healthcare delivery systems for physician buy‐in and patient acceptance

Dai

Tayur

2022

Self Cite

“…Note, this literature review is by no means comprehensive; readers are referred to more references in Armony et al. (2015) and Dai and Tayur (2018).…”

Section: Beyond the Single‐pool Model And Future Researchmentioning

confidence: 99%

“…Finally, we discuss present and future research that intends to integrate patient outcomes and behavior with patient flow modeling. Note, this literature review is by no means comprehensive; readers are referred to more references in Armony et al (2015) and Dai and Tayur (2018).…”

Section: Beyond the Single-pool Model And Future Researchmentioning

confidence: 99%

Recent Modeling and Analytical Advances in Hospital Inpatient Flow Management

Dai

Shi

2021

I npatient flow management plays a critical role in care delivery, patient outcomes, and hospital operational and financial costs. Modeling and performance analysis of inpatient flow present unique features and challenges that differ from operations in other service industries. In this study, we review recent modeling and analytical advances in the setting of inpatient flow management, with a particular focus on service time models motivated from the observations of inpatient discharges. We first compare two new service time models developed to capture the time-of-day inpatient flow dynamics, and reveal interesting connections between the two models. We then review analytical methods developed to analyze systems with the new service time models. Based on one method, which is amenable to a one-dimensional exact analysis under certain conditions, we further introduce its approximations that have explicit analytical forms and enable efficient computations in large systems. In particular, we showcase how to leverage a powerful tool, Stein's method framework, in the hospital setting for steady-state approximations and characterizing error bounds. We conclude this study by a literature review on other important aspects in inpatient flow management and propose future research directions, from both the modeling and analytical perspectives.

“…MDP is also widely used for optimizing patient screening/triage and routing in resource-limited healthcare environments; for example, Argon and Ziya (2009), € Ormeci et al (2015), Sun et al (2017), Hu et al (2019). Also see Chapter 14 in Dai and Tayur (2018) for a detailed review of MDP applications in healthcare operations.…”

Section: Literature Reviewmentioning

confidence: 99%

An Operational Framework for the Adoption and Integration of New Diagnostic Tests

Shi

Helm

Heese

et al. 2021

The gap between medical research on diagnostic testing and clinical workflow can lead to rejection of valuable medical research in a busy clinical environment due to increased workloads, or rejection of medical research in the laboratory that may be valuable in practice due to a misunderstanding of the system‐level benefits of the new test. This has implications for research organizations, diagnostic test manufacturers, and hospital managers among others. To bridge this gap, we develop a Markov decision process (MDP) from which we create “adoption regions” that specify the combination of test characteristics medical research must achieve for the test to be feasible for adoption in practice. To address the curse of dimensionality from patient risk stratification, we develop a decomposition algorithm along with structural properties that shed light on which patients and when a new diagnostic test should be used. In a case study of a partner Emergency Department, we show that the conventional myopic medical criterion can lead to poor decision making in both research development and clinical practice. In particular, we find that specificity—long a secondary consideration and often overlooked in the research process—is, in fact, the key to effective implementation of new tests into clinical environments. This myopic approach can lead to overvaluing or undervaluing new medical research. This mismatch is accentuated when a simple (current) policy is used to integrate research into the clinical environment compared with our MDP’s policy—poor implementation of a new test can also lead to unnecessary rejection. Our framework provides easily interpretable guidelines for medical research development and clinical adoption decisions that can guide medical research as to which test characteristics to focus on to improve the chances of adoption.