A decision support system to solve the problem of health care priority-setting

Pinho, Micaela; Moura, Ana

doi:10.1108/jstpm-01-2021-0008

Cited by 2 publications

(5 citation statements)

References 31 publications

(46 reference statements)

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“…The scarcity of resources and endless demands restrict HC services to be supplied to all individuals in need. This situation became increasingly evident during the COVID-19 pandemic [ 71 ]. The COVID-19 pandemic has placed a significant burden on global HC systems, with developing countries bearing the brunt of the damage owing to their underdeveloped HC infrastructure and limited financial resources [ 72 ].…”

Section: Challengesmentioning

confidence: 99%

“…Additionally, prioritizing access to care treatment during extremely difficult times is needed [ 59 ]. Determining priorities is a difficult and seemingly unsolvable problem because it includes balancing efficiency with equality across several different criteria [ 71 ].…”

Section: Challengesmentioning

confidence: 99%

“…Prioritizing options for complete catastrophe preparation and response is rendered easier with MCDM use [ 78 ]. A DM tool has been established to address the issue of defining priorities among patients contending for minimal health facilities [ 71 , 82 ]. Consequently, providing decision assistance may aid in improving decision quality, reliability, and clarity.…”

Section: Motivationsmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

“…Various studies are recommended to be extended to other MCDM methods [ 53 – 55 , 68 , 71 , 79 , 80 , 83 , 84 ]. For example, a recommendation is to deal with the issue of uncertainty in HC setting prioritization using fuzzy, intuitionistic fuzzy, hesitant fuzzy, neutrosophic fuzzy, or probabilistic information [ 71 ]. Moreover, a study for evaluating strategies on enhancing HC resilience to combat COVID-19 recommended to be extended to other MCDM methods (e.g., AHP, TOPSIS and VIKOR) [ 39 , 72 ].…”

Section: Recommendationsmentioning

confidence: 99%

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Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Alamoodi

Zaidan

Albahri

et al. 2023

Complex Intell. Syst.

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When COVID-19 spread in China in December 2019, thousands of studies have focused on this pandemic. Each presents a unique perspective that reflects the pandemic’s main scientific disciplines. For example, social scientists are concerned with reducing the psychological impact on the human mental state especially during lockdown periods. Computer scientists focus on establishing fast and accurate computerized tools to assist in diagnosing, preventing, and recovering from the disease. Medical scientists and doctors, or the frontliners, are the main heroes who received, treated, and worked with the millions of cases at the expense of their own health. Some of them have continued to work even at the expense of their lives. All these studies enforce the multidisciplinary work where scientists from different academic disciplines (social, environmental, technological, etc.) join forces to produce research for beneficial outcomes during the crisis. One of the many branches is computer science along with its various technologies, including artificial intelligence, Internet of Things, big data, decision support systems (DSS), and many more. Among the most notable DSS utilization is those related to multicriterion decision making (MCDM), which is applied in various applications and across many contexts, including business, social, technological and medical. Owing to its importance in developing proper decision regimens and prevention strategies with precise judgment, it is deemed a noteworthy topic of extensive exploration, especially in the context of COVID-19-related medical applications. The present study is a comprehensive review of COVID-19-related medical case studies with MCDM using a systematic review protocol. PRISMA methodology is utilized to obtain a final set of (n = 35) articles from four major scientific databases (ScienceDirect, IEEE Xplore, Scopus, and Web of Science). The final set of articles is categorized into taxonomy comprising five groups: (1) diagnosis (n = 6), (2) safety (n = 11), (3) hospital (n = 8), (4) treatment (n = 4), and (5) review (n = 3). A bibliographic analysis is also presented on the basis of annual scientific production, country scientific production, co-occurrence, and co-authorship. A comprehensive discussion is also presented to discuss the main challenges, motivations, and recommendations in using MCDM research in COVID‐19-related medial case studies. Lastly, we identify critical research gaps with their corresponding solutions and detailed methodologies to serve as a guide for future directions. In conclusion, MCDM can be utilized in the medical field effectively to optimize the resources and make the best choices particularly during pandemics and natural disasters.

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Section: Challengesmentioning

confidence: 99%

Section: Challengesmentioning

confidence: 99%

Section: Motivationsmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

Section: Recommendationsmentioning

confidence: 99%

See 3 more Smart Citations

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Alamoodi

Zaidan

Albahri

et al. 2023

Complex Intell. Syst.

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Computational complexity in explainable decision support system: A review

Ezeji,

Adigun,

Oki

2024

IFS

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The rise of decision processes in various sectors has led to the adoption of decision support systems (DSSs) to support human decision-makers but the lack of transparency and interpretability of these systems has led to concerns about their reliability, accountability and fairness. Explainable Decision Support Systems (XDSS) have emerged as a promising solution to address these issues by providing explanatory meaning and interpretation to users about their decisions. These XDSSs play an important role in increasing transparency and confidence in automated decision-making. However, the increasing complexity of data processing and decision models presents computational challenges that need to be investigated. This review, therefore, focuses on exploring the computational complexity challenges associated with implementing explainable AI models in decision support systems. The motivations behind explainable AI were discussed, explanation methods and their computational complexities were analyzed, and trade-offs between complexity and interpretability were highlighted. This review provides insights into the current state-of-the-art computational complexity within explainable decision support systems and future research directions.

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A decision support system to solve the problem of health care priority-setting

Cited by 2 publications

References 31 publications

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Systematic review of MCDM approach applied to the medical case studies of COVID-19: trends, bibliographic analysis, challenges, motivations, recommendations, and future directions

Computational complexity in explainable decision support system: A review

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