{"title":"Fuzzy Model for Evaluating the Quality of Medical Care","authors":"S. V. Begicheva","doi":"10.1109/CBI.2019.10088","DOIUrl":null,"url":null,"abstract":"The quality of medical care can be evaluated by three main components, which were proposed by Avedis Donabedian: the quality of structure, the quality of process and the quality of outcome. Donabedian's idea was that all three components are connected by sequential progression, i.e. the quality of structure provides for the quality of process and the quality of process provides for the quality of outcome. There are some papers on the study of causal model of structure - process - outcome, but they do not consider the assessment and analysis of the changes in the quality of care observed after certain changes in the administrative structure of a healthcare delivery unit. This paper proposes a model for evaluating the impact of changes in the structure of a healthcare delivery unit on the quality of medical care provided. The proposed method for the development and analysis of the model includes four steps: (1) sample determination and data collection; (2) data reduction by exploratory factor analysis to define the indicators for each of the dimensions of the Donabedian Model; (3) studying the indirect influence of structure changes using the apparatus of fuzzy binary relations; (4) calculating the change in the quality measures after those structure changes and modeling management scenarios. The model combines the apparatus of fuzzy binary relations with the analysis based on fuzzy cognitive modeling. The fusion of the two approaches is justified by the what-if analysis and allows define the optimal management strategy. The model is realized with data obtained by surveying ambulance patients.","PeriodicalId":193238,"journal":{"name":"2019 IEEE 21st Conference on Business Informatics (CBI)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2019-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"6","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2019 IEEE 21st Conference on Business Informatics (CBI)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/CBI.2019.10088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 6
Abstract
The quality of medical care can be evaluated by three main components, which were proposed by Avedis Donabedian: the quality of structure, the quality of process and the quality of outcome. Donabedian's idea was that all three components are connected by sequential progression, i.e. the quality of structure provides for the quality of process and the quality of process provides for the quality of outcome. There are some papers on the study of causal model of structure - process - outcome, but they do not consider the assessment and analysis of the changes in the quality of care observed after certain changes in the administrative structure of a healthcare delivery unit. This paper proposes a model for evaluating the impact of changes in the structure of a healthcare delivery unit on the quality of medical care provided. The proposed method for the development and analysis of the model includes four steps: (1) sample determination and data collection; (2) data reduction by exploratory factor analysis to define the indicators for each of the dimensions of the Donabedian Model; (3) studying the indirect influence of structure changes using the apparatus of fuzzy binary relations; (4) calculating the change in the quality measures after those structure changes and modeling management scenarios. The model combines the apparatus of fuzzy binary relations with the analysis based on fuzzy cognitive modeling. The fusion of the two approaches is justified by the what-if analysis and allows define the optimal management strategy. The model is realized with data obtained by surveying ambulance patients.