J. R. Warren, A. Davidovic, S. Spenceley, P. Bolton
{"title":"Mediface:面向全科医生的预期数据输入界面","authors":"J. R. Warren, A. Davidovic, S. Spenceley, P. Bolton","doi":"10.1109/OZCHI.1998.732214","DOIUrl":null,"url":null,"abstract":"The paper describes an effort to make computer interfaces more intelligent in facilitating the coding of clinical information. We believe the interface should be sufficiently efficient and easy-to-use that a physician can code information during the consultation without detracting from doctor-patient interaction. In this way the benefits of a \"clinical workstation\" setting, such as best practices guidance and drug interaction detection, are maximised. We pursue the strategy of applying machine learning to existing databases of electronic medical records to develop probabilistic models of general practice. Based on this model, we have simulated and prototyped data entry interfaces with \"hot lists\" (short pick list menus of relevant items) and dynamic graphical depictions of contextually likely clinical data.","PeriodicalId":322019,"journal":{"name":"Proceedings 1998 Australasian Computer Human Interaction Conference. OzCHI'98 (Cat. No.98EX234)","volume":"103 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"1998-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"13","resultStr":"{\"title\":\"Mediface: anticipative data entry interface for general practitioners\",\"authors\":\"J. R. Warren, A. Davidovic, S. Spenceley, P. Bolton\",\"doi\":\"10.1109/OZCHI.1998.732214\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The paper describes an effort to make computer interfaces more intelligent in facilitating the coding of clinical information. We believe the interface should be sufficiently efficient and easy-to-use that a physician can code information during the consultation without detracting from doctor-patient interaction. In this way the benefits of a \\\"clinical workstation\\\" setting, such as best practices guidance and drug interaction detection, are maximised. We pursue the strategy of applying machine learning to existing databases of electronic medical records to develop probabilistic models of general practice. Based on this model, we have simulated and prototyped data entry interfaces with \\\"hot lists\\\" (short pick list menus of relevant items) and dynamic graphical depictions of contextually likely clinical data.\",\"PeriodicalId\":322019,\"journal\":{\"name\":\"Proceedings 1998 Australasian Computer Human Interaction Conference. OzCHI'98 (Cat. No.98EX234)\",\"volume\":\"103 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"1998-11-29\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"13\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings 1998 Australasian Computer Human Interaction Conference. OzCHI'98 (Cat. No.98EX234)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/OZCHI.1998.732214\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings 1998 Australasian Computer Human Interaction Conference. OzCHI'98 (Cat. No.98EX234)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/OZCHI.1998.732214","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Mediface: anticipative data entry interface for general practitioners
The paper describes an effort to make computer interfaces more intelligent in facilitating the coding of clinical information. We believe the interface should be sufficiently efficient and easy-to-use that a physician can code information during the consultation without detracting from doctor-patient interaction. In this way the benefits of a "clinical workstation" setting, such as best practices guidance and drug interaction detection, are maximised. We pursue the strategy of applying machine learning to existing databases of electronic medical records to develop probabilistic models of general practice. Based on this model, we have simulated and prototyped data entry interfaces with "hot lists" (short pick list menus of relevant items) and dynamic graphical depictions of contextually likely clinical data.