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Kwak, N. K.; McCarthy, Kevin; Parker, Gerald E.

doi:10.1023/a:1022812322966

Cited by 22 publications

(2 citation statements)

References 1 publication

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“…However, complex problems require a dedicated software since the correct estimation of priorities is an arduous task in this scenario [9]. Additional information about decision hierarchy and pairwise comparison could be found elsewhere [11][12][13].…”

Section: Decision Hierarchy and Pairwise Comparison Matricesmentioning

confidence: 99%

An Analytical Hierarchy Process to Decision Making in Human Blood, Cells, and Tissue Banks

Pereira¹,

Xavier²

2016

Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

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The decisions are critical in transfusion and transplantation principally because they could affect directly and indirectly the post-transfusion and post-transplantation safety. However, the blood, cells, and tissues banks should not be uniquely centered in the satisfaction of receptor (patient) requirements. All the decisions affecting the sustainability of the organization should also be considered. Despite not to be systematically used in these banks, the analytical hierarchy process could be useful for the improvement in the reliability of the decisions. This chapter reviews the basics of the analytical hierarchy process applied to the production of blood, cells, and tissues, and presents a case study that could be interpreted as applicable to other situations in these organizations.

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Section: Decision Hierarchy and Pairwise Comparison Matricesmentioning

confidence: 99%

An Analytical Hierarchy Process to Decision Making in Human Blood, Cells, and Tissue Banks

Pereira¹,

Xavier²

2016

Applications and Theory of Analytic Hierarchy Process - Decision Making for Strategic Decisions

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“…Multi-attribute methods are widely used in medical environments, providing trusted environments where decisions can be made once the criteria have been established. Thus, the analytical hierarchical process (AHP) [ 10 , 11 , 12 , 13 , 14 , 15 , 16 ], TOPSIS [ 17 , 18 , 19 ], MAUT [ 10 , 16 , 20 , 21 ], PROMETHEE [ 22 , 23 , 24 , 25 ] and VIKOR [ 26 , 27 , 28 ] methods, among others, are applied to decision management in medical–sanitary environments. However, the increasing need for incorporating and controlling inaccuracy in information and the judgments in decision making have popularized the use of methods that are capable of controlling such decision uncertainty.…”

Section: Introductionmentioning

confidence: 99%

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

et al. 2022

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The triage processes prior to the assignation of healthcare resources in hospitals are some of the decision-making processes that more severely affect patients. This effect gets even worse in health emergency situations and intensive care units (ICUs). Aiming to facilitate the decision-making process, in this work the use of vague fuzzy numbers is proposed, aiming to define a multi-attribute patient hierarchization method to be used in emergency situations at hospital ICUs. The incorporation of fuzzy models allows for modelling the vagueness and uncertainty associated with decision criteria evaluation, with which more efficient support is provided to the decision-making process. After defining the methodology, the effectiveness of this new system for patient hierarchization is shown in a case study. As a consequence of that, it is proved that the integration of decision-support systems into healthcare environments results to be efficient and productive, suggesting that if a part of the decision process is supported by these systems, then the errors associated with wrong interpretations and/or diagnoses might be reduced.

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Workforce forecasting models: A systematic review

Safarishahrbijari

2018

Journal of Forecasting

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Workforce analytics involves using models that integrate internal and external data to predict future workforce and help organizations in any industry examine factors that have a prognostic effect. This paper assesses workforce modeling and prediction methods by examining their rationale, strengths, and constraints. It aims to identify enhancements for further development of workforce forecasting models and compares the capacity and reliability of different forecasting methods. Past and present modeling trends are described and critiqued based on their relevance to current requirements. Several approaches are reviewed, such as time series modeling and system dynamics simulation. Sensitivity analysis in models is assessed. The models are decomposed into three modes: supply‐based, demand‐based, and need‐based, which in some cases provide substantially different estimates of future workforce need. The chronological progression of models' development is analyzed. The articles are also classified based on the countries and the sectors that have paid great attention to workforce prediction research. Consideration of the use of workforce models and the inputs into such models is not within the scope of this paper.

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Cited by 22 publications

References 1 publication

An Analytical Hierarchy Process to Decision Making in Human Blood, Cells, and Tissue Banks

An Analytical Hierarchy Process to Decision Making in Human Blood, Cells, and Tissue Banks

Design and Definition of a New Decision Support System Aimed to the Hierarchization of Patients Candidate to Be Admitted to Intensive Care Units

Workforce forecasting models: A systematic review

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