Bebek Erdebilli , Cigdem Sicakyuz , İbrahim Yilmaz
{"title":"An integrated multiple-criteria decision-making and data envelopment analysis framework for efficiency assessment in sustainable healthcare systems","authors":"Bebek Erdebilli , Cigdem Sicakyuz , İbrahim Yilmaz","doi":"10.1016/j.health.2024.100327","DOIUrl":null,"url":null,"abstract":"<div><p>Efficiency is critical in allocating sustainable healthcare resources to ensure that hospitals can effectively care for patients while maintaining high-quality care delivery. Hence, it is necessary to monitor efficiency carefully. This study aims to assess hospital unit effectiveness through a novel comprehensive approach integrating Multiple-Criteria Decision Making (MCDM) with Data Envelopment Analysis (DEA). The proposed MCDM-DEA framework involves allocating varying weights to distinct data categories. It harnesses the capabilities of the q-rung orthopair fuzzy (q-ROF) methodology to address the inherent uncertainties in healthcare performance assessment. The experimental results provide a comprehensively structured ranking system for specific hospital departments. This ranking system allows decision-makers to identify the strengths and weaknesses of each department, enabling them to make informed decisions regarding resource allocation and improvement strategies. Furthermore, the integration of MCDM-DEA provides a robust and objective assessment tool for monitoring and evaluating the performance of hospital departments over time. These rankings offer invaluable insights to decision-makers, equipping them with the strategic information needed to enhance the overall performance of hospital units.</p></div>","PeriodicalId":73222,"journal":{"name":"Healthcare analytics (New York, N.Y.)","volume":"5 ","pages":"Article 100327"},"PeriodicalIF":0.0000,"publicationDate":"2024-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2772442524000297/pdfft?md5=6c714f9d6778326d4cafac5a4ffb5c58&pid=1-s2.0-S2772442524000297-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Healthcare analytics (New York, N.Y.)","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2772442524000297","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
Efficiency is critical in allocating sustainable healthcare resources to ensure that hospitals can effectively care for patients while maintaining high-quality care delivery. Hence, it is necessary to monitor efficiency carefully. This study aims to assess hospital unit effectiveness through a novel comprehensive approach integrating Multiple-Criteria Decision Making (MCDM) with Data Envelopment Analysis (DEA). The proposed MCDM-DEA framework involves allocating varying weights to distinct data categories. It harnesses the capabilities of the q-rung orthopair fuzzy (q-ROF) methodology to address the inherent uncertainties in healthcare performance assessment. The experimental results provide a comprehensively structured ranking system for specific hospital departments. This ranking system allows decision-makers to identify the strengths and weaknesses of each department, enabling them to make informed decisions regarding resource allocation and improvement strategies. Furthermore, the integration of MCDM-DEA provides a robust and objective assessment tool for monitoring and evaluating the performance of hospital departments over time. These rankings offer invaluable insights to decision-makers, equipping them with the strategic information needed to enhance the overall performance of hospital units.