{"title":"从业者对早期预警系统的态度:从专业干扰到关系支持","authors":"L. Campbell, Amrita George, Shion Guha","doi":"10.1109/procomm52174.2021.00028","DOIUrl":null,"url":null,"abstract":"As the general population ages and life expectancy increases in the United States, demand for virtual health care is on the rise. Undoubtedly, the next several decades will see increases in automated patient care and use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future.","PeriodicalId":278101,"journal":{"name":"2021 IEEE International Professional Communication Conference (ProComm)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2021-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Practitioner Attitudes towards an Early Warning System : From Professional Distraction to Relational Support\",\"authors\":\"L. Campbell, Amrita George, Shion Guha\",\"doi\":\"10.1109/procomm52174.2021.00028\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"As the general population ages and life expectancy increases in the United States, demand for virtual health care is on the rise. Undoubtedly, the next several decades will see increases in automated patient care and use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future.\",\"PeriodicalId\":278101,\"journal\":{\"name\":\"2021 IEEE International Professional Communication Conference (ProComm)\",\"volume\":\"24 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2021-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2021 IEEE International Professional Communication Conference (ProComm)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/procomm52174.2021.00028\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2021 IEEE International Professional Communication Conference (ProComm)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/procomm52174.2021.00028","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Practitioner Attitudes towards an Early Warning System : From Professional Distraction to Relational Support
As the general population ages and life expectancy increases in the United States, demand for virtual health care is on the rise. Undoubtedly, the next several decades will see increases in automated patient care and use of data-driven warning systems, trends which have already accelerated in the wake of Covid-19. Thus, understanding how traditionally trained healthcare practitioners respond to predictive analytics, like early warning systems, is vital for their successful implementation in the future.