Shin-Ping Tu, Brittany Garcia, Xi Zhu, Daniel Sewell, Vimal Mishra, Khalid Matin, Alan Dow
{"title":"复杂社会技术生态系统和学习型医疗系统中的病人护理","authors":"Shin-Ping Tu, Brittany Garcia, Xi Zhu, Daniel Sewell, Vimal Mishra, Khalid Matin, Alan Dow","doi":"10.1002/lrh2.10427","DOIUrl":null,"url":null,"abstract":"<p>The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.</p>","PeriodicalId":43916,"journal":{"name":"Learning Health Systems","volume":"8 S1","pages":""},"PeriodicalIF":2.6000,"publicationDate":"2024-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10427","citationCount":"0","resultStr":"{\"title\":\"Patient care in complex Sociotechnological ecosystems and learning health systems\",\"authors\":\"Shin-Ping Tu, Brittany Garcia, Xi Zhu, Daniel Sewell, Vimal Mishra, Khalid Matin, Alan Dow\",\"doi\":\"10.1002/lrh2.10427\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.</p>\",\"PeriodicalId\":43916,\"journal\":{\"name\":\"Learning Health Systems\",\"volume\":\"8 S1\",\"pages\":\"\"},\"PeriodicalIF\":2.6000,\"publicationDate\":\"2024-05-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/lrh2.10427\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Learning Health Systems\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10427\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"HEALTH POLICY & SERVICES\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Learning Health Systems","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/lrh2.10427","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"HEALTH POLICY & SERVICES","Score":null,"Total":0}
引用次数: 0
摘要
学习型医疗系统(LHS)模式的提出是为了利用医疗信息技术系统获取的数据提供实时、双向的学习流,从而在医疗保健服务中实现快速学习。正如具有里程碑意义的美国国家医学院报告《跨越质量鸿沟》所强调的那样,美国医疗保健服务行业是一个复杂的自适应系统,迫切需要开发创新方法,通过利用电子健康记录(EHR)中的真实医疗服务数据来确定高效的团队结构。我们将围绕团队沟通的复杂性以及如何在多团队系统(MTS)框架和沟通网络多理论多层次框架等理论指导下解决问题展开讨论。为了推动医疗保健服务科学的发展并促进长效医疗系统,我们的团队一直在利用电子病历数据建立一条新的研究路线,以研究在复杂的癌症护理服务现实世界中的多团队系统。我们正在开发研究 MTS 的新网络指标,并将分析电子病历通信网络结构对患者预后的影响。这项研究将为患者护理提供干预措施/工具,医疗保健领导者和医疗保健专业人员可以有效地利用健康 IT 数据,实施最循证的协作方法,以实现最佳的 LHS 和患者疗效。
Patient care in complex Sociotechnological ecosystems and learning health systems
The learning health system (LHS) model was proposed to provide real-time, bi-directional flow of learning using data captured in health information technology systems to deliver rapid learning in healthcare delivery. As highlighted by the landmark National Academy of Medicine report “Crossing the Quality Chasm,” the U.S. healthcare delivery industry represents complex adaptive systems, and there is an urgent need to develop innovative methods to identify efficient team structures by harnessing real-world care delivery data found in the electronic health record (EHR). We offer a discussion surrounding the complexities of team communication and how solutions may be guided by theories such as the Multiteam System (MTS) framework and the Multitheoretical Multilevel Framework of Communication Networks. To advance healthcare delivery science and promote LHSs, our team has been building a new line of research using EHR data to study MTS in the complex real world of cancer care delivery. We are developing new network metrics to study MTSs and will be analyzing the impact of EHR communication network structures on patient outcomes. As this research leads to patient care delivery interventions/tools, healthcare leaders and healthcare professionals can effectively use health IT data to implement the most evidence-based collaboration approaches in order to achieve the optimal LHS and patient outcomes.