Department of Medicine, Emory University School of Medicine, Atlanta, Georgia.
Department of Biomedical Informatics, Emory University School of Medicine, Atlanta, Georgia.
Surg Infect (Larchmt). 2019 Oct;20(7):530-534. doi: 10.1089/sur.2019.155. Epub 2019 Aug 29.
A landscape analysis of mobile health (mHealth) applications and published literature related to their use in surgical site infection (SSI) detection and surveillance was conducted by the Assessing Surgical Site Infection Surveillance Technologies (ASSIST) investigators. The literature review focused on post-discharge SSI detection or tracking by caregivers or patients using mHealth technology. This report is unique in its review across both commercial and research-based mHealth apps. Apps designed for long-term wound tracking and those focused on care coordination and scheduling were excluded. A structured evaluation framework was used to assess the operational, technical, and policy features of the apps. Of the 10 apps evaluated, only two were in full clinical use. A variety of data were captured by the apps including wound photographs (eight apps), wound measurements (three apps), dressing assessments (two apps), physical activity metrics (three apps), medication adherence (three apps) as well as structured surveys, signs, and symptoms. Free-text responses were permitted by at least two apps. The extent of integration with the native electronic health record system was variable. The examination of rapidly evolving technologies is challenged by lack of standard evaluative methods, such as those more commonly used in clinical research. This review is unique in its application of a structured evaluation framework across both commercial and research-based mHealth apps.
ASSIST 研究人员对移动医疗 (mHealth) 应用程序及其在手术部位感染 (SSI) 检测和监测方面的应用相关文献进行了景观分析。文献综述侧重于护理人员或患者使用 mHealth 技术对出院后 SSI 的检测或跟踪。本报告的独特之处在于对商业和基于研究的 mHealth 应用程序进行了审查。排除了专为长期伤口跟踪以及专注于护理协调和日程安排的应用程序。使用结构化评估框架评估应用程序的操作、技术和政策特征。在所评估的 10 个应用程序中,只有两个在临床中完全使用。这些应用程序可以捕获各种数据,包括伤口照片(8 个应用程序)、伤口测量值(3 个应用程序)、敷料评估(2 个应用程序)、身体活动指标(3 个应用程序)、药物依从性(3 个应用程序)以及结构化调查、体征和症状。至少有两个应用程序允许使用自由文本回复。与本地电子健康记录系统的集成程度各不相同。由于缺乏标准评估方法(例如临床研究中更常用的方法),快速发展的技术的检查受到挑战。本综述的独特之处在于在商业和基于研究的 mHealth 应用程序中应用了结构化评估框架。