{"title":"选择最佳外科多专科医院的最佳多标准决策框架","authors":"Hemant Petwal, Rinkle Rani","doi":"10.1109/PDGC50313.2020.9315760","DOIUrl":null,"url":null,"abstract":"A multispecialty hospital (MSH) is a healthcare facility that provides medical and surgical services to patients. Multispecialty hospitals providing surgical care differ in their performance, such as patient care and satisfaction, success rate, mortality rate, surgical complication rate, waiting time, etc. Since multispecialty hospitals vary in large numbers, it becomes challenging for a patient to select the best MSH providing quality surgical services. In this paper, the challenge of selecting the best MSH is addressed as a problem of multicriteria decision-making (MCDM). This paper proposes an optimal MCDM framework for selecting the best and quality MSH for surgery. The proposed framework is divided into two phases: The optimization phase and the decision-making phase. In the optimization phase, the multi-objective water cycle algorithm (MOWCA) is used to obtain Pareto-optimal MSHs. Subsequently, in the decision-making phase, AHP is utilized to select the best MSH from the obtained Pareto-optimal MSHs. The proposed framework is compared with existing MCDM methods in terms of accuracy. Finally, the proposed framework is validated through a case study of a real multispecialty hospital dataset obtained from the Dehradun district of Uttarakhand, India. The results show that the proposed framework obtained more accurate results and outperforms the existing MCDM method.","PeriodicalId":347216,"journal":{"name":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","volume":"61 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-11-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":"{\"title\":\"An optimal Multi-Criteria Decision-Making Framework to select best Multispecialty Hospital for surgery\",\"authors\":\"Hemant Petwal, Rinkle Rani\",\"doi\":\"10.1109/PDGC50313.2020.9315760\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A multispecialty hospital (MSH) is a healthcare facility that provides medical and surgical services to patients. Multispecialty hospitals providing surgical care differ in their performance, such as patient care and satisfaction, success rate, mortality rate, surgical complication rate, waiting time, etc. Since multispecialty hospitals vary in large numbers, it becomes challenging for a patient to select the best MSH providing quality surgical services. In this paper, the challenge of selecting the best MSH is addressed as a problem of multicriteria decision-making (MCDM). This paper proposes an optimal MCDM framework for selecting the best and quality MSH for surgery. The proposed framework is divided into two phases: The optimization phase and the decision-making phase. In the optimization phase, the multi-objective water cycle algorithm (MOWCA) is used to obtain Pareto-optimal MSHs. Subsequently, in the decision-making phase, AHP is utilized to select the best MSH from the obtained Pareto-optimal MSHs. The proposed framework is compared with existing MCDM methods in terms of accuracy. Finally, the proposed framework is validated through a case study of a real multispecialty hospital dataset obtained from the Dehradun district of Uttarakhand, India. The results show that the proposed framework obtained more accurate results and outperforms the existing MCDM method.\",\"PeriodicalId\":347216,\"journal\":{\"name\":\"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)\",\"volume\":\"61 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-11-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"1\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/PDGC50313.2020.9315760\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 Sixth International Conference on Parallel, Distributed and Grid Computing (PDGC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/PDGC50313.2020.9315760","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
An optimal Multi-Criteria Decision-Making Framework to select best Multispecialty Hospital for surgery
A multispecialty hospital (MSH) is a healthcare facility that provides medical and surgical services to patients. Multispecialty hospitals providing surgical care differ in their performance, such as patient care and satisfaction, success rate, mortality rate, surgical complication rate, waiting time, etc. Since multispecialty hospitals vary in large numbers, it becomes challenging for a patient to select the best MSH providing quality surgical services. In this paper, the challenge of selecting the best MSH is addressed as a problem of multicriteria decision-making (MCDM). This paper proposes an optimal MCDM framework for selecting the best and quality MSH for surgery. The proposed framework is divided into two phases: The optimization phase and the decision-making phase. In the optimization phase, the multi-objective water cycle algorithm (MOWCA) is used to obtain Pareto-optimal MSHs. Subsequently, in the decision-making phase, AHP is utilized to select the best MSH from the obtained Pareto-optimal MSHs. The proposed framework is compared with existing MCDM methods in terms of accuracy. Finally, the proposed framework is validated through a case study of a real multispecialty hospital dataset obtained from the Dehradun district of Uttarakhand, India. The results show that the proposed framework obtained more accurate results and outperforms the existing MCDM method.