A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

Ardakani, Elham Shaker; Larimi, Niloofar Gilani; Nejad, Maryam Oveysi; Hosseini, Mahsa Madani; Zargoush, Manaf

doi:10.1016/j.omega.2022.102750

Cited by 13 publications

(7 citation statements)

References 57 publications

(66 reference statements)

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“…Findings from the aforementioned study showed higher social functioning among younger age and lower mental health scores among women and smokers (Sabbah et al, 2013). Medical students were recruited as alternative resources due to the lack of medical staff and increase in healthcare demands (Ardakani et al, 2023). The imposed changes could have affected their QoL and may have long-term consequences.…”

Section: Introductionmentioning

confidence: 99%

Health-related Quality of life of medical students during the COVID-19 pandemic: A cross-sectional study in Lebanon

Awada

Ahmad

Assi

et al. 2023

Power and Education

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Objectives: This study aims to assess (i) the health-related Quality of Life (QoL) of medical students in Lebanon, (ii) its association with their general characteristics, and (iii) to investigate the combined effects of the predictors affecting the quality of life of students. Methods: An observational cross-sectional study was performed over three months, targeting 401 medical students from six universities. The 36-item Short Form survey was used to quantify the different QoL scores. Results: The physical component (PC) domains had higher scores than the mental component (MC) ones (70.2 vs. 55.9). Students aged 30 years or less had higher scores in all the PC domains than older students (70.9 vs. 59.3; p<0.010) but comparable scores in the MC domains (55.6 vs. 59.8; p>0.050). Single students had a significantly higher role-emotional score (63.5 vs. 49.0; p<0.050), and those studying in public universities had greater general health scores (57.7 vs. 53.7; p<0.050). Younger age significantly increased most of the health-related PC scores, and having chronic diseases decreased all the QoL scores except the mental health score. Conclusion: The QoL of medical students was negatively affected during the pandemic, particularly their mental health. Informed educational and health decisions are required to minimize this impact on their academic performance.

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Section: Introductionmentioning

confidence: 99%

Health-related Quality of life of medical students during the COVID-19 pandemic: A cross-sectional study in Lebanon

Awada

Ahmad

Assi

et al. 2023

Power and Education

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“…As the final step, they employed data on the COVID-19 epidemic disease in Wuhan, China, to confirm the validity of the presented technique. Ardakani et al ( 2023 ) constructed a location-allocation model with multiple objectives to boost healthcare systems resilience using alternative sources, such as trainee nurses and field and backup hospitals, aiming for system cost minimization and rate maximization of satisfaction among patients of COVID-19 and medical employees. They also developed a powerful method to encounter the uncertainty of data.…”

Section: Related Literaturementioning

confidence: 99%

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

2023

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With the severe outbreak of the novel coronavirus (COVID-19), researchers are motivated to develop efficient methods to face related issues. The present study aims to design a resilient health system to offer medical services to COVID-19 patients and prevent further disease outbreaks by social distancing, resiliency, cost, and commuting distance as decisive factors. It incorporated three novel resiliency measures (i.e., health facility criticality, patient dissatisfaction level, and dispersion of suspicious people) to promote the designed health network against potential infectious disease threats. Also, it introduced a novel hybrid uncertainty programming to resolve a mixed degree of the inherent uncertainty in the multi-objective problem, and it adopted an interactive fuzzy approach to address it. The actual data obtained from a case study in Tehran province in Iran proved the strong performance of the presented model. The findings show that the optimum use of medical centers’ potential and the corresponding decisions result in a more resilient health system and cost reduction. A further outbreak of the COVID-19 pandemic is also prevented by shortening the commuting distance for patients and avoiding the increasing congestion in the medical centers. Also, the managerial insights show that establishing and evenly distributing camps and quarantine stations within the community and designing an efficient network for patients with different symptoms result in the optimum use of the potential capacity of medical centers and a decrease in the rate of bed shortage in the hospitals. Another insight drawn is that an efficient allocation of the suspect and definite cases to the nearest screening and care centers makes it possible to prevent the disease carriers from commuting within the community and increase the coronavirus transmission rate.

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“…To address this challenge, a robust optimization technique can be used as a capable technique that is independent of any assumption on the probability distribution function of the non-deterministic situation (Ben-Tal et al, 2009). Researchers focused on applying robust optimization techniques for healthcare network design problems to manage the inherent randomness in the key variables of the model (Ardakani et al 2023, Hosseini-Motlagh et al, 2016, Vahdani, 2016, Kouchaki Tajani et al, 2021. Developing a hybrid optimization model by implementing a distributionally robust optimization technique, we mitigate the intrinsic weakness of stochastic optimization (i.e., dependence on complete precise distributions) (Huang et al, 2020;Ma et al, 2020;Jia et al, 2020).…”

Section: Literature Reviewmentioning

confidence: 99%

“…In recent years, reforms in national healthcare networks have been inspired mainly by social justice goals despite the conflicting aims amongst involved regional stakeholders (Han, 2012). It is desired to consider the preferences and requirements of the involved stakeholders in the healthcare network design process (Ardakani et al, 2023;Angelis et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Data-Driven Robust Healthcare Network Design under Disruption Considering Conditional Value at Risk

Hasani,

Darayi,

Haseli

2023

Preprint

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This study proposes a distributionally robust two-stage risk-aversion model for comprehensive healthcare network design. The proposed model considers the weighted average of the expected losses to handle the disruption uncertainty affecting the network performance (i.e., service capacity, service demand, and route capacity) with conditional value-at-risk as a risk assessment measure. The proposed model is implemented in a stylized case study of healthcare network design in Iran. The results indicate the efficiency of the proposed distributionally robust optimization model compared to the traditional two-stage stochastic model. In case of a high level of uncertainty, the proposed distributionally robust network design tends to be more decentralized by establishing more new mobile facilities, selecting more backup facilities at low-risk regions, and assigning more inventory to established facilities. Results provide decision-makers with managerial insights about cost-effective decisions to handle uncertainty. Also, the sensitivity analysis shows that the type of ambiguous set and levels of confidence, risk aversion, and adjustment parameters affect the network performance.

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A resilient, robust transformation of healthcare systems to cope with COVID-19 through alternative resources

Cited by 13 publications

References 57 publications

Health-related Quality of life of medical students during the COVID-19 pandemic: A cross-sectional study in Lebanon

Health-related Quality of life of medical students during the COVID-19 pandemic: A cross-sectional study in Lebanon

Resilient and social health service network design to reduce the effect of COVID-19 outbreak

Data-Driven Robust Healthcare Network Design under Disruption Considering Conditional Value at Risk

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