电子健康记录中的社会和行为变量：提高数据质量和实用性的途径。
Social and Behavioral Variables in the Electronic Health Record: A Path Forward to Increase Data Quality and Utility.
机构信息
E.C. Lasser is research associate, Johns Hopkins Center for Population Health Information Technology, Department of Health Policy and Management, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland; ORCID: https://orcid.org/0000-0002-1758-9822 .
J.M. Kim is assistant professor, Department of Pediatrics, and faculty, Armstrong Institute for Patient Safety and Quality, Johns Hopkins University School of Medicine, Baltimore, Maryland; ORCID: https://orcid.org/0000-0001-5678-6629 .
出版信息
Acad Med. 2021 Jul 1;96(7):1050-1056. doi: 10.1097/ACM.0000000000004071.
PURPOSE
Social and behavioral determinants of health (SBDH) are important factors that affect the health of individuals but are not routinely captured in a structured and systematic manner in electronic health records (EHRs). The purpose of this study is to generate recommendations for systematic implementation of SBDH data collection in EHRs through (1) reviewing SBDH conceptual and theoretical frameworks and (2) eliciting stakeholder perspectives on barriers to and facilitators of using SBDH information in the EHR and priorities for data collection.
METHOD
The authors reviewed SBDH frameworks to identify key social and behavioral variables and conducted focus groups and interviews with 17 clinicians and researchers at Johns Hopkins Health System between March and May 2018. Transcripts were coded and common themes were extracted to understand the barriers to and facilitators of accessing SBDH information.
RESULTS
The authors found that although the frameworks agreed that SBDH affect health outcomes, the lack of model consensus complicates the development of specific recommendations for the prioritization of SBDH data collection. Study participants recognized the importance of SBDH information and individual health and agreed that patient-reported information should be captured, but clinicians and researchers cited different priorities for which variables are most important. For the few SBDH variables that are captured, participants reported that data were often incomplete, unclear, or inconsistent, affecting both researcher and clinician responses to SBDH barriers to health.
CONCLUSIONS
Health systems need to identify and prioritize the systematic implementation of collection of a high-impact but limited list of SBDH variables in the EHR. These variables should affect care and be amenable to change and collection should be integrated into clinical workflows. Improved data collection of SBDH variables can lead to a better understanding of how SBDH affect health outcomes and ways to better address underlying health disparities that need urgent action.
目的
社会和行为决定健康因素（SBDH）是影响个体健康的重要因素，但在电子健康记录（EHR）中通常无法以结构化和系统的方式进行捕捉。本研究的目的是通过（1）审查 SBDH 概念和理论框架，以及（2）征求利益相关者对在 EHR 中使用 SBDH 信息的障碍和促进因素以及数据收集重点的看法，为系统地在 EHR 中收集 SBDH 数据提出建议。
方法
作者回顾了 SBDH 框架，以确定关键的社会和行为变量，并于 2018 年 3 月至 5 月在约翰霍普金斯卫生系统（Johns Hopkins Health System）与 17 名临床医生和研究人员进行了焦点小组和访谈。对转录本进行了编码，并提取了共同主题，以了解获取 SBDH 信息的障碍和促进因素。
结果
作者发现，尽管这些框架都认为 SBDH 会影响健康结果，但缺乏模型共识使得为 SBDH 数据收集的优先级制定具体建议变得复杂。研究参与者认识到 SBDH 信息和个人健康的重要性，并同意应捕获患者报告的信息，但临床医生和研究人员对哪些变量最重要有不同的优先级。对于少数被捕获的 SBDH 变量，参与者报告说数据通常不完整、不清楚或不一致，这影响了研究人员和临床医生对 SBDH 健康障碍的反应。
结论
卫生系统需要确定并优先考虑在 EHR 中系统地收集影响重大但数量有限的 SBDH 变量。这些变量应该影响护理并易于改变，并且应该整合到临床工作流程中。改善 SBDH 变量的数据收集可以更好地了解 SBDH 如何影响健康结果，并找到更好的方法来解决需要紧急行动的潜在健康差异。
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