In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community ca...In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.展开更多
目的:系统评价基于互联网的院外管理模式对食管癌术后病人的干预效果。方法:系统检索中国知网、维普数据库、万方数据库、中国生物医学文献数据库、EMbase、PubMed、Web of Science、MedLine、the Cochrane Library中有关基于互联网的...目的:系统评价基于互联网的院外管理模式对食管癌术后病人的干预效果。方法:系统检索中国知网、维普数据库、万方数据库、中国生物医学文献数据库、EMbase、PubMed、Web of Science、MedLine、the Cochrane Library中有关基于互联网的院外管理模式干预食管癌术后病人的随机对照试验和类试验研究,检索时限为建库至2022年12月1日,由2名研究员完成文献筛选、质量评价、数据提取,采用RevMan 5.3软件进行Meta分析。结果:共纳入9篇文献,涉及720例病人。Meta分析结果显示,基于互联网的院外管理模式可提高病人的生活质量[MD=9.24,95%CI(7.55,10.92),P<0.00001],降低术后吻合口狭窄发生率[RR=0.31,95%CI(0.17,0.59),P=0.0003],提高病人自我护理能力[MD=5.39,95%CI(4.23,6.55),P<0.00001]、护理满意度[RR=1.37,95%CI(1.18,1.59),P<0.0001)]。结论:现有证据表明,基于互联网的院外管理模式能提高病人生活质量,降低吻合口狭窄发生率,增强自我护理能力,提高护理满意度,促进病人康复。展开更多
随着三级医院的功能需求和安全管理要求不断提高,传统的消防系统和监控系统已经无法满足要求。为了提升三级综合医院的整体安全管理水平,厦门大学附属第一医院采用建筑信息模型(Building Information Modeling,BIM)逆向建模,建立医院建...随着三级医院的功能需求和安全管理要求不断提高,传统的消防系统和监控系统已经无法满足要求。为了提升三级综合医院的整体安全管理水平,厦门大学附属第一医院采用建筑信息模型(Building Information Modeling,BIM)逆向建模,建立医院建筑数字化模型,搭建一体化管理平台,对消防系统与安防系统进行集成改造,实现综合安全管理系统三维可视化,各系统信息互联互通、实时联动、融合共享,医院的整体安全管理水平、智能化水平得以提升,以实时监控和预警为基础的安全管理模式得以建立。展开更多
文摘In a prior practice and policy article published in Healthcare Science,we introduced the deployed application of an artificial intelligence(AI)model to predict longer‐term inpatient readmissions to guide community care interventions for patients with complex conditions in the context of Singapore's Hospital to Home(H2H)program that has been operating since 2017.In this follow on practice and policy article,we further elaborate on Singapore's H2H program and care model,and its supporting AI model for multiple readmission prediction,in the following ways:(1)by providing updates on the AI and supporting information systems,(2)by reporting on customer engagement and related service delivery outcomes including staff‐related time savings and patient benefits in terms of bed days saved,(3)by sharing lessons learned with respect to(i)analytics challenges encountered due to the high degree of heterogeneity and resulting variability of the data set associated with the population of program participants,(ii)balancing competing needs for simpler and stable predictive models versus continuing to further enhance models and add yet more predictive variables,and(iii)the complications of continuing to make model changes when the AI part of the system is highly interlinked with supporting clinical information systems,(4)by highlighting how this H2H effort supported broader Covid‐19 response efforts across Singapore's public healthcare system,and finally(5)by commenting on how the experiences and related capabilities acquired from running this H2H program and related community care model and supporting AI prediction model are expected to contribute to the next wave of Singapore's public healthcare efforts from 2023 onwards.For the convenience of the reader,some content that introduces the H2H program and the multiple readmissions AI prediction model that previously appeared in the prior Healthcare Science publication is repeated at the beginning of this article.
文摘随着三级医院的功能需求和安全管理要求不断提高,传统的消防系统和监控系统已经无法满足要求。为了提升三级综合医院的整体安全管理水平,厦门大学附属第一医院采用建筑信息模型(Building Information Modeling,BIM)逆向建模,建立医院建筑数字化模型,搭建一体化管理平台,对消防系统与安防系统进行集成改造,实现综合安全管理系统三维可视化,各系统信息互联互通、实时联动、融合共享,医院的整体安全管理水平、智能化水平得以提升,以实时监控和预警为基础的安全管理模式得以建立。
