Roth Caryn, Payne Philip R O, Weier Rory C, Shoben Abigail B, Fletcher Erica N, Lai Albert M, Kelley Marjorie M, Plascak Jesse J, Foraker Randi E
Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus, OH, USA.
Comprehensive Cancer Center, James Cancer Hospital and Solove Research Institute, The Ohio State University, Columbus, OH, USA; Division of Epidemiology, College of Public Health, The Ohio State University, Columbus, OH, USA.
J Biomed Inform. 2016 Apr;60:95-103. doi: 10.1016/j.jbi.2016.01.013. Epub 2016 Jan 29.
Community-level factors have been clearly linked to health outcomes, but are challenging to incorporate into medical practice. Increasing use of electronic health records (EHRs) makes patient-level data available for researchers in a systematic and accessible way, but these data remain siloed from community-level data relevant to health.
This study sought to link community and EHR data from an older female patient cohort participating in an ongoing intervention at the Ohio State University Wexner Medical Center to associate community-level data with patient-level cardiovascular health (CVH) as well as to assess the utility of this EHR integration methodology.
CVH was characterized among patients using available EHR data collected May through July of 2013. EHR data for 153 patients were linked to United States census-tract level data to explore feasibility and insights gained from combining these disparate data sources. Analyses were conducted in 2014.
Using the linked data, weekly per capita expenditure on fruits and vegetables was found to be significantly associated with CVH at the p<0.05 level and three other community-level attributes (median income, average household size, and unemployment rate) were associated with CVH at the p<0.10 level.
This work paves the way for future integration of community and EHR-based data into patient care as a novel methodology to gain insight into multi-level factors that affect CVH and other health outcomes. Further, our findings demonstrate the specific architectural and functional challenges associated with integrating decision support technologies and geographic information to support tailored and patient-centered decision making therein.
社区层面的因素已被明确与健康结果相关联,但将其纳入医疗实践具有挑战性。电子健康记录(EHR)的使用日益增加,使得研究人员能够以系统且可获取的方式获得患者层面的数据,但这些数据与健康相关的社区层面数据仍相互孤立。
本研究旨在将俄亥俄州立大学韦克斯纳医学中心正在进行的一项干预措施中参与的老年女性患者队列的社区和电子健康记录数据相链接,以便将社区层面的数据与患者层面的心血管健康(CVH)相关联,并评估这种电子健康记录整合方法的效用。
利用2013年5月至7月收集的可用电子健康记录数据对患者的心血管健康进行特征描述。153名患者的电子健康记录数据与美国人口普查区层面的数据相链接,以探索将这些不同数据源相结合的可行性和见解。分析于2014年进行。
使用链接数据发现,水果和蔬菜的每周人均支出在p<0.05水平上与心血管健康显著相关,其他三个社区层面的属性(中位数收入、平均家庭规模和失业率)在p<0.10水平上与心血管健康相关。
这项工作为未来将社区和基于电子健康记录的数据整合到患者护理中铺平了道路,这是一种新颖的方法,可深入了解影响心血管健康和其他健康结果的多层次因素。此外,我们的研究结果表明了在整合决策支持技术和地理信息以支持其中的个性化和以患者为中心的决策方面存在的具体架构和功能挑战。