Grando Adela, Sottara Davide, Singh Ripudaman, Murcko Anita, Soni Hiral, Tang Tianyu, Idouraine Nassim, Todd Michael, Mote Mike, Chern Darwyn, Dye Christy, Whitfield Mary Jo
Biomedical Informatics, College of Health Solutions, Arizona State University, Scottsdale, AZ, United States.
Mayo Clinic, Rochester, MN, United States.
Int J Med Inform. 2020 Jun;138:104121. doi: 10.1016/j.ijmedinf.2020.104121. Epub 2020 Mar 19.
Consent2Share (C2S) is an open source software created by the Office of the National Coordinator Data Segmentation for Privacy initiative to support electronic health record (EHR) granular segmentation. To date, there are no published formal evaluations of Consent2Share.
Structured data (e.g. medications) codified using standard clinical terminologies (e.g. RxNorm) was extracted from the EHR of 36 patients with behavioral health conditions from study sites. EHRs were available through a health information exchange and two sites. The EHR data was already classified into data types (e.g. procedures and services). Both Consent2Share and health providers classified EHR data based on value sets (e.g. mental health) and sensitivity (e.g. not sensitive. Descriptive statistics and Chi-square analysis were used to compare differences between data categorizations.
From the resulting 1,080 medical records items, 584 were distinct. Significant differences were found between sensitivity classifications by Consent2Share and providers (χ2 (2, N = 584) = 114.74, p = <0.0001). Sensitivity comparisons led to 56.0 % of agreements, 31.2 % disagreements, and 12.8 % partial agreements. Most (97.8 %) disagreements resulted from information classified as not sensitive by Consent2Share, but sensitive by provider (e.g. behavioral health prevention education service). In terms of data types, most disagreements (57.1 %) focused on procedures and services information (e.g. ligation of fallopian tube). When considering value sets, most disagreements focused on genetic data (100.0 %), followed by sexual and reproductive health (88.9 %).
There is a need to further validate Consent2Share before broad use in health care settings. The outcomes from this pilot study will help guide improvements in segmentation logic of tools like Consent2Share and may set the stage for a new generation of personalized consent engines.
“同意共享”（C2S）是由国家协调员办公室为隐私倡议进行数据分割而创建的开源软件，旨在支持电子健康记录（EHR）的细粒度分割。迄今为止，尚未有关于“同意共享”的正式评估发表。
从研究地点的36名患有行为健康疾病患者的电子健康记录中提取使用标准临床术语（如RxNorm）编码的结构化数据（如药物）。电子健康记录可通过健康信息交换平台和两个站点获取。电子健康记录数据已被分类为数据类型（如程序和服务）。“同意共享”和医疗服务提供者均根据值集（如心理健康）和敏感度（如不敏感）对电子健康记录数据进行分类。使用描述性统计和卡方分析来比较数据分类之间的差异。
在生成的1080份医疗记录项目中，有584项是不同的。发现“同意共享”和提供者在敏感度分类上存在显著差异（χ2（2，N = 584）= 114.74，p = <0.0001）。敏感度比较导致56.0%的一致性、31.2%的不一致性和12.8%的部分一致性。大多数（97.8%）不一致是由于“同意共享”分类为不敏感但提供者分类为敏感的信息（如行为健康预防教育服务）。就数据类型而言，大多数不一致（57.1%）集中在程序和服务信息（如输卵管结扎）。在考虑值集时，大多数不一致集中在基因数据（100.0%），其次是性健康和生殖健康（88.9%）。
在医疗保健环境中广泛使用之前，需要进一步验证“同意共享”。这项试点研究的结果将有助于指导改进“同意共享”等工具的分割逻辑，并可能为新一代个性化同意引擎奠定基础。
