An efficient healthcare chain design for resolving the patient scheduling problem: queuing theory and MILP-ASA optimization approach

Ali Ala, Morteza Yazdani (), Mohsen Ahmadi (), Aida Poorianasab and Mahdi Yousefi Nejad Attari
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Ali Ala: Shanghai Jiao Tong University
Morteza Yazdani: Universidad Internacional de Valencia-VIU
Mohsen Ahmadi: Urmia University of Technology
Aida Poorianasab: Islamic Azad University
Mahdi Yousefi Nejad Attari: Islamic Azad University

Annals of Operations Research, 2023, vol. 328, issue 1, No 2, 3-33

Abstract: Abstract The efficiency evaluation of the healthcare chain network becomes crucial as healthcare systems seek to enhance patient satisfaction and reduce costs during the health check. This study proposes a mixed-integer linear programming model that resolves the patient selection problem for influential diagnosis-related groups treatments by considering the approximate solution approach. This research's objective is to apply the minimum response time for the arrival time of two types of patients (regular and urgent) by presenting the Poisson distributed and queuing theory. Second, a process for resolving the optimal solutions for waiting time and the total number of patient arrival times to the hospital to achieve the target at a minimum supply chain cost. Furthermore, based on the obtained results applied for the patients with a first-come-first-serve policy and to meet overall arrival time on time, the percentage of patients waiting time at the healthcare center is reduced to under 30% for the emergency patient. At the same time, the percentage of regular patients who do not receive the treatment service time earlier and do not refer to the hospital punctuality is increased to more than 70%. Those theories determined that more healthcare costs and dissatisfaction for regular patients, while in contrast, for emergency patients, are decreased waiting time and healthcare cost services.

Keywords: Healthcare service chain network; Response time; Poisson distribution; Queuing theory; Diagnosis related groups (DRG) (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s10479-023-05287-5

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