Addressing healthcare operational deficiencies using stochastic and dynamic programming

Geng, Na; Xie, Xiaolan; Zhang, Zheng

doi:10.1080/00207543.2017.1397789

Cited by 12 publications

(6 citation statements)

References 47 publications

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“…The fundamental requirements for delivering interventional/surgical procedures are (a) availability of a mentor/expert ready to provide medical guidance and (b) reliable connectivity (110). Poor infrastructure and adverse conditions can easily impair the performance of telementoring systems (43).…”

Section: Artificial Intelligencementioning

confidence: 99%

Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives

Wachs

Kirkpatrick

Tisherman

2021

Annu. Rev. Biomed. Eng.

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Telemedicine is perhaps the most rapidly growing area in health care. Approximately 15 million Americans receive medical assistance remotely every year. Yet rural communities face significant challenges in securing subspecialist care. In the United States, 25% of the population resides in rural areas, where less than 15% of physicians work. Current surgery residency programs do not adequately prepare surgeons for rural practice. Telementoring, wherein a remote expert guides a less experienced caregiver, has been proposed to address this challenge. Nonetheless, existing mentoring technologies are not widely available to rural communities, due to a lack of infrastructure and mentor availability. For this reason, some clinicians prefer simpler and more reliable technologies. This article presents past and current telementoring systems, with a focus on rural settings, and proposes a set of requirements for such systems. We conclude with a perspective on the future telementoring systems and the integration of artificial intelligence within those systems. Expected final online publication date for the Annual Review of Biomedical Engineering, Volume 23 is June 2021. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Section: Artificial Intelligencementioning

confidence: 99%

Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives

Wachs

Kirkpatrick

Tisherman

2021

Annu. Rev. Biomed. Eng.

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“…In general, depending on how these patients arrive at the facility, they are classified as inpatient (hospitalized), outpatient, or emergency [ 1 , 2 ]. Imaging facilities are constrained resources in most healthcare systems [ 3 ], which rely on rapid examination to provide timely diagnosis and proper referral of patients to treatments. The gap between demand and capacity in those facilities results in increasing waiting times [ 4 ], and proper allocation of the available capacity is one of the most challenging activities for their managers [ 3 ].…”

Section: Introductionmentioning

confidence: 99%

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Silva

Fogliatto

Krindges

et al. 2021

BMC Health Serv Res

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Background We propose a mathematical model formulated as a finite-horizon Markov Decision Process (MDP) to allocate capacity in a radiology department that serves different types of patients. To the best of our knowledge, this is the first attempt at considering radiology resources with different capacities and individual no-show probabilities of ambulatory patients in an MDP model. To mitigate the negative impacts of no-show, overbooking rules are also investigated. Methods The model’s main objective is to identify an optimal policy for allocating the available capacity such that waiting, overtime, and penalty costs are minimized. Optimization is carried out using traditional dynamic programming (DP). The model was applied to real data from a radiology department of a large Brazilian public hospital. The optimal policy is compared with five alternative policies, one of which resembles the one currently used by the department. We identify among alternative policies the one that performs closest to the optimal. Results The optimal policy presented the best performance (smallest total daily cost) in the majority of analyzed scenarios (212 out of 216). Numerical analyses allowed us to recommend the use of the optimal policy for capacity allocation with a double overbooking rule and two resources available in overtime periods. An alternative policy in which outpatients are prioritized for service (rather than inpatients) displayed results closest to the optimal policy, being also recommended due to its easy implementation. Conclusions Based on such recommendation and observing the state of the system at any given period (representing the number of patients waiting for service), radiology department managers should be able to make a decision (i.e., define number and type of patients) that should be selected for service such that the system’s cost is minimized.

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“…Patient admission decision is directly related to inpatient bed management. As one key medical resource, inpatient bed management greatly affects the operation efficiency and service level of the whole hospital (Goldman et al., 1968; Kolesar, 1970; Geng et al., 2019). One main issue in patient admission decision for both outpatient department and emergency department (ED) is to develop effective rules for the allocation of inpatient beds upon arrival (Samiedaluie et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Admission control of hospitalization with patient gender by using Markov decision process

Jiang

Yang

Tang

et al. 2021

Int Trans Operational Res

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Admission control of hospitalization considering patient gender is an interesting issue in the study of hospital bed management. This paper addresses the decision on the admission of patients who should immediately be admitted into a same-gender room or rejected. Note that a patient is admitted depending on different conditions, such as his/her health condition, gender, the availability of beds, the length of stay, and the reward of hospitalization. Focusing on the key factor, patient gender, this paper sets up an infinite-horizon total discounted reward Markov decision process model with the purpose to maximize the total expected reward for the hospital, which leads to an optimal dynamic policy. Then, the structural properties of the optimal policy are analyzed. Additionally, a value iteration algorithm is proposed to find the optimal policy. Finally, some numerical experiments are used to discuss how the optimal dynamic policy depends on some key parameters of the system. Furthermore, the performance of the optimal policy is discussed though comparison with the three other policies by means of simulating different scenarios.

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Addressing healthcare operational deficiencies using stochastic and dynamic programming

Cited by 12 publications

References 47 publications

Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives

Procedural Telementoring in Rural, Underdeveloped, and Austere Settings: Origins, Present Challenges, and Future Perspectives

Dynamic capacity allocation in a radiology service considering different types of patients, individual no-show probabilities, and overbooking

Admission control of hospitalization with patient gender by using Markov decision process

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