Performance Analysis of Hospital Managers Using Fuzzy AHP and Fuzzy TOPSIS: Iranian Experience

Shafii, Milad; Hosseini, Seyed Mostafa; Arab, Mohammad; Asgharizadeh, Ezzatollah; Farzianpour, Fereshteh

doi:10.5539/gjhs.v8n2p137

Cited by 21 publications

(14 citation statements)

References 27 publications

(32 reference statements)

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“…In another example, researchers used expert systems and administrative data to design a system for measuring the performance of hospital managers. 62 Researchers are also using machine learning and data mining methods to improve road safety in LMICs. In one study, researchers used street imagery available online and machine learning to estimate helmet use prevalence.…”

Section: Diagnosismentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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Concurrent advances in information technology infrastructure and mobile computing power in many low and middle-income countries (LMICs) have raised hopes that artificial intelligence (AI) might help to address challenges unique to the field of global health and accelerate achievement of the health-related sustainable development goals. A series of fundamental questions have been raised about AI-driven health interventions, and whether the tools, methods, and protections traditionally used to make ethical and evidence-based decisions about new technologies can be applied to AI. Deployment of AI has already begun for a broad range of health issues common to LMICs, with interventions focused primarily on communicable diseases, including tuberculosis and malaria. Types of AI vary, but most use some form of machine learning or signal processing. Several types of machine learning methods are frequently used together, as is machine learning with other approaches, most often signal processing. AI-driven health interventions fit into four categories relevant to global health researchers: (1) diagnosis, (2) patient morbidity or mortality risk assessment, (3) disease outbreak prediction and surveillance, and (4) health policy and planning. However, much of the AI-driven intervention research in global health does not describe ethical, regulatory, or practical considerations required for widespread use or deployment at scale. Despite the field remaining nascent, AI-driven health interventions could lead to improved health outcomes in LMICs. Although some challenges of developing and deploying these interventions might not be unique to these settings, the global health community will need to work quickly to establish guidelines for development, testing, and use, and develop a user-driven research agenda to facilitate equitable and ethical use.

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Section: Diagnosismentioning

confidence: 99%

Artificial intelligence and the future of global health

Schwalbe¹,

2020

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“…IRI = Here IRI (Random inconsistency index) is the value that is get from Table 5. The values of this table is based on the simulation of a large number of paired comparison n\n matrices obtained [8].…”

Section: Steps Measurement Inconsistency Ratementioning

confidence: 99%

“…Among other points that should be considered very, is the managers overall strategy. As an example If the overall goal is to minimize the annual and periodic costs of power plants instead initial investment has no restriction, can have a dramatic effect on the rate and also in contrast to the case and the factors influencing the selection process and decisions of senior managers to type of power plants it is true [8].…”

Section: Steps Measurement Inconsistency Ratementioning

confidence: 99%

Fore Sighting and Estimating the Risk of Investing in the Construction of Power Plants Using AHP

Hosseini¹,

Gharehpetian

Farzianpour

2015

JSSM

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Today, with advances that have occurred in electricity industry and technology of manufacturing all kinds of power plants whether renewable or perishable, making decision to choose the type and kind of an ideal plant is very important and strategic. For any weakness in determining short, medium and long term parameters affecting deciding whether technical, economical, environmental, social, political , and so on may cause irreparable damage. Also timing and fore sighting factors should be taken into account in decision-making equations. Selecting the type suitable for use in power plants connected to the network or independent sector is the main part of task. Therefore, because there are many variables and factors in the text and the margin of such a task, bed and plant kind selection is very difficult and time consuming. This choice is ultimately influenced by many technical and non-technical measures that are each divided into further subcategories. Due to repetition of this operation in the discussion of issues, finding an efficient way in this area would be very useful. In this paper, a hierarchical decision-making procedure for the selection of the ideal power for productivity and satisfaction in the operation of taking is introduced. That can be generalized to other types of construction and operation concepts in technologies of power plants.

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“…Because a hospital is a social organization and is one of the important units for providing health services, the poor performance of managers leads to delayed treatment and disease progression or even death. When there are incompetent managers in hospitals, resources could be wasted and ultimately productivity will be reduced (14).…”

Section: Introductionmentioning

confidence: 99%

Future competencies for hospital management in developing countries: Systematic review

Malmoon

Tourani

maleki

et al. 2020

mjiri

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Background: This was a systematic review presenting the future competencies for hospital managers. Methods: Participants, interventions, comparisons and outcomes (PICO) strategy with MeSH terms were used for searching. Databases used were Web of Science, PsycINFO and Medline, EBSCO, ScienceDirect, Emerald, ProQuest, Social Sciences Research Network, Embase, and some Iranian database such as IRANDOC and SID. All publications were accepted if they had been published in English or Persian language and fulltexts were accessible. The study was carried out using a systematic literature search published between January 1995 and December 2018. The Endnote v.17 software was used to facilitate reference management. Quality assessment of publications were carried out independently by two reviewers using the Critical Appraisal Skills Programme (CASP) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. A descriptive summary with data tables produced to summarize the literature. Data extraction was conducted by two researchers.Results: Forty seven studies were analyzed to identify the future competencies required for hospital managers. In all, 3190 publications were retrieved. 1934 citations were excluded because of irrelevant (n=2600), duplicates (n=316), and unavailable full text (n=18). After studying the 256 remaining publications, 33 citations were extracted and presented. After extracting the future competencies of hospital managers, competencies were classified into four main categories based on the qualitative meta-synthesis process. Conclusion:The most important competencies in the future are global knowledge, Key Eligibility Criteria, wide attitude and core skills that must be addressed in the face of ambiguity.

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Performance Analysis of Hospital Managers Using Fuzzy AHP and Fuzzy TOPSIS: Iranian Experience

Cited by 21 publications

References 27 publications

Artificial intelligence and the future of global health

Artificial intelligence and the future of global health

Fore Sighting and Estimating the Risk of Investing in the Construction of Power Plants Using AHP

Future competencies for hospital management in developing countries: Systematic review

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