{"title":"构建支持网络的复杂性概要文件,用于检查医疗保健专业人员的决策责任","authors":"K. S. Chung, Jane M. Young, K. White","doi":"10.1145/2492517.2500324","DOIUrl":null,"url":null,"abstract":"Complexity is generally accepted to be the interrelatedness of components within a system. Treating the general practitioner (GP)-patient encounter as a complex system, we argue that complexity (resulting from the degree of interactions between GP, colleagues, patient) determines the performance of GPs, measured by attitudes to responsibility for their decisions about patient treatment. In this paper, we propose the use of social network measures of `density' and `inclusiveness' for computing the `interrelatedness' of components within a complex system. We also suggest the use of `number of components' (NoC) and `degree of interrelatedness' (DoI) to plot the complexity profiles for each GP. Results from a sample of 107 GPs show that GPs with simple profiles (i.e. low NoC & low DoI), compared to those in non-simple profiles, indicate a higher responsibility for the decisions they make in medical care. In conclusion, we argue that social networks-based complexity profiles are useful for understanding responsibility-taking in primary care. We highlight a number of interesting insights and practical implications for healthcare professionals.","PeriodicalId":442230,"journal":{"name":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","volume":"1217 44","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2013-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Towards a networks-enabled complexity profile for examining responsibility for decision-making by healthcare professionals\",\"authors\":\"K. S. Chung, Jane M. Young, K. White\",\"doi\":\"10.1145/2492517.2500324\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Complexity is generally accepted to be the interrelatedness of components within a system. Treating the general practitioner (GP)-patient encounter as a complex system, we argue that complexity (resulting from the degree of interactions between GP, colleagues, patient) determines the performance of GPs, measured by attitudes to responsibility for their decisions about patient treatment. In this paper, we propose the use of social network measures of `density' and `inclusiveness' for computing the `interrelatedness' of components within a complex system. We also suggest the use of `number of components' (NoC) and `degree of interrelatedness' (DoI) to plot the complexity profiles for each GP. Results from a sample of 107 GPs show that GPs with simple profiles (i.e. low NoC & low DoI), compared to those in non-simple profiles, indicate a higher responsibility for the decisions they make in medical care. In conclusion, we argue that social networks-based complexity profiles are useful for understanding responsibility-taking in primary care. We highlight a number of interesting insights and practical implications for healthcare professionals.\",\"PeriodicalId\":442230,\"journal\":{\"name\":\"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)\",\"volume\":\"1217 44\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2013-08-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/2492517.2500324\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2013 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM 2013)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/2492517.2500324","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Towards a networks-enabled complexity profile for examining responsibility for decision-making by healthcare professionals
Complexity is generally accepted to be the interrelatedness of components within a system. Treating the general practitioner (GP)-patient encounter as a complex system, we argue that complexity (resulting from the degree of interactions between GP, colleagues, patient) determines the performance of GPs, measured by attitudes to responsibility for their decisions about patient treatment. In this paper, we propose the use of social network measures of `density' and `inclusiveness' for computing the `interrelatedness' of components within a complex system. We also suggest the use of `number of components' (NoC) and `degree of interrelatedness' (DoI) to plot the complexity profiles for each GP. Results from a sample of 107 GPs show that GPs with simple profiles (i.e. low NoC & low DoI), compared to those in non-simple profiles, indicate a higher responsibility for the decisions they make in medical care. In conclusion, we argue that social networks-based complexity profiles are useful for understanding responsibility-taking in primary care. We highlight a number of interesting insights and practical implications for healthcare professionals.