School of Nursing, Johns Hopkins University, Baltimore, Maryland, USA.
Institute for Clinical and Translational Research, Johns Hopkins University, Baltimore, Maryland, USA.
J Am Med Inform Assoc. 2019 Nov 1;26(11):1209-1217. doi: 10.1093/jamia/ocz168.
The study sought to characterize institution-wide participation in secure messaging (SM) at a large academic health network, describe our experience with electronic medical record (EMR)-based cohort selection, and discuss the potential roles of SM for research recruitment.
Study teams defined eligibility criteria to create a computable phenotype, structured EMR data, to identify and recruit participants. Patients with SM accounts matching this phenotype received recruitment messages. We compared demographic characteristics across SM users and the overall health system. We also tabulated SM activation and use, characteristics of individual studies, and efficacy of the recruitment methods.
Of the 1 308 820 patients in the health network, 40% had active SM accounts. SM users had a greater proportion of white and non-Hispanic patients than nonactive SM users id. Among the studies included (n = 13), 77% recruited participants with a specific disease or condition. All studies used demographic criteria for their phenotype, while 46% (n = 6) used demographic, disease, and healthcare utilization criteria. The average SM response rate was 2.9%, with higher rates among condition-specific (3.4%) vs general health (1.4%) studies. Those studies with a more inclusive comprehensive phenotype had a higher response rate.
Target population and EMR queries (computable phenotypes) affect recruitment efficacy and should be considered when designing an EMR-based recruitment strategy.
SM guided by EMR-based cohort selection is a promising approach to identify and enroll research participants. Efforts to increase the number of active SM users and response rate should be implemented to enhance the effectiveness of this recruitment strategy.
本研究旨在描述一个大型学术医疗网络内全院范围参与安全信息系统(SM)的情况,描述我们在基于电子病历(EMR)的队列选择方面的经验,并探讨 SM 在研究招募方面的潜在作用。
研究团队定义了符合计算表型的入选标准,对 EMR 数据进行结构化处理,以识别和招募参与者。与该表型匹配的 SM 账户的患者会收到招募信息。我们比较了 SM 用户和整个医疗系统的人口统计学特征。我们还列出了 SM 的激活和使用情况、个别研究的特征以及招募方法的效果。
在该医疗网络的 1 308 820 名患者中,有 40%的患者拥有活跃的 SM 账户。SM 用户中白人患者和非西班牙裔患者的比例高于非活跃 SM 用户。在所纳入的研究中(n=13),77%的研究招募了特定疾病或病症的参与者。所有研究都使用人口统计学标准来确定表型,而 46%(n=6)的研究同时使用人口统计学、疾病和医疗保健利用标准。SM 的平均回复率为 2.9%,特定疾病(3.4%)研究的回复率高于一般健康(1.4%)研究。那些使用更全面的综合表型的研究具有更高的回复率。
目标人群和 EMR 查询(可计算的表型)会影响招募效果,在设计基于 EMR 的招募策略时应予以考虑。
基于 EMR 的队列选择指导的 SM 是识别和招募研究参与者的一种很有前途的方法。应努力增加活跃 SM 用户的数量并提高回复率,以提高这种招募策略的有效性。