Articles
International Journal of Healthcare Management (20479700)17(1)pp. 63-75
Nowadays proper management of hospital resources has played an important role in reducing the economic pressure on hospitals. Unprecedented challenges have arisen for healthcare systems including further resource constraints due to the Covid-19 pandemic. So different countries faced the low capacity of hospitals' resources to admit and care for patients during the crisis. Medical staff, beds and personal protective equipment are among the scarce resources in this period. In this study, the patient flow in public hospitals has been modeled using System Dynamics (SD) simulation to manage the available capacity of intensive care units (ICU) and wards during the COVID-19 period. The model has been implemented in Vensim PLE and verified for public hospitals in Amol, Iran. The results have shown that hospitals face bed shortages in the period coinciding with the growing incidence of COVID-19, being forced to cancel or delay the admission of selected patients and nonemergency surgeries. Different scenarios based on possible strategies for managing hospital bed capacities have been also evaluated. The best strategy is the one in which the allocated bed capacities to COVID-19 patients are altered based on infection rates during different stages of the pandemic, resulting the shortage of beds is postponed the most. © 2022 Informa UK Limited, trading as Taylor & Francis Group.
Annals of Operations Research (02545330)328(1)pp. 727-754
This study considers a combination of strategic and tactical levels of operating room planning where two types of full-day and half-day blocks are considered. Constraints on available beds in hospital wards and different durations of stay for elective patients in the ward are the main assumptions of the problem. The aim is to minimize overtime and idleness of operating rooms, maximize surgeons' satisfaction and minimize the number of unscheduled surgeries in the master surgery schedule. We propose a mixed integer programming model as well as a novel heuristic algorithm by combining simulated annealing meta-heuristic and linear programming models. Real data from a teaching-educational hospital with 20 operating rooms and 47 surgeons’ groups as well as some random problem instances are used in the experiments. The results indicate high performance of the proposed heuristic algorithm in generating near-optimal Pareto solutions compared with the mathematical model and a local search algorithm from the literature. Sensitivity analysis is done on some parameters of the problem like overtime cost of operating rooms, maximum allowable overtime, available beds in the ward, and the number of attendance days preferred by each surgeon. The results from our case study show that 6% of operating room costs are related to the fact that some surgeons are unwilling to perform surgeries on some days of a week. Also, adding 20% to the capacity of ward beds results in 3% and 10% decrease in unscheduled surgeries and operating rooms idleness, respectively. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
International Journal of Industrial and Systems Engineering (17485037)43(4)pp. 435-463
In this research a master surgical scheduling problem in conditions of uncertainty of demand, duration of surgery and length of patients' stay is studied. First, an MIP model is developed in which the length of patients' stay is considered probabilistic. Then, allowing for uncertainty in demand, a robust model is presented. Finally, a simulation-optimisation approach is developed in which three parameters are considered as uncertain. In this approach, the Grey Wolf and genetic algorithms are designed as the optimisation, and the Mont Carlo simulation is used in the simulation module. The results show that the maximum gap in the comparison of the simulation-optimisation algorithms and the lower-bound solution of the mathematical models in small-scale problems is only 9.36% while the algorithms are much faster. In larger-scale problems, the average improvement percentage of the proposed approach with the Grey Wolf optimisation module as compared to the genetic algorithm module is 2.93%. Copyright © 2023 Inderscience Enterprises Ltd.
Lecture Notes on Data Engineering and Communications Technologies (23674512)181pp. 299-308
This study aims to identify the application of process mining techniques in health centres for the visualisation of healthcare activities. As a scoping review, this research was used and divided into three phases: literature collection, assessment, and selection. A literature search had done on Google Scholar, Web of Science, PubMed, Elsevier, and ProQuest, along with the impact of inclusion and exclusion criteria. Keywords have been addressed as follows: process mining, visualising, mapping, workflow mining, automated business process, discovery, process discovery, performance mining, healthcare, hospital, emergency department, emergency medical service, and apply. The findings showed that process mining can be used to analyse different activities in the field of healthcare, including workflow in healthcare, clinical and administrative processes, data analysis in information systems, events data in patients’ infectious, creation of dashboards, the discovery of unexpected, and hidden relationships. Finally, as the significance of this research, it has been argued that the use of process mining in healthcare allows health professionals to understand the actual implementation of processes. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.
