Identification of Social Risk-Related Referrals in Discrete Primary Care Electronic Health Record Data: Lessons Learned From a Novel Methodology

IF 3.2 2区 医学 Q2 HEALTH CARE SCIENCES & SERVICES Health Services Research Pub Date : 2025-02-06 DOI:10.1111/1475-6773.14443
Jenine Dankovchik, Rachel Gold, Aileen Ochoa, Jenna Donovan, Rose Gunn, Suzanne Morrissey, Cristina Huebner Torres, Ned Mossman, Seth A. Berkowitz
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Abstract

Objective

To assess the utility of using discrete primary care electronic health record (EHR) data to identify social risk referrals in a national network of community-based clinics.

Data Sources and Study Setting

Primary data were abstracted from the OCHIN network EHR (June 2016 to February 2022) of 1459 community-based clinics across the United States.

Study Design

Structured data elements included 913 commonly used smartphrases and 53 procedure codes that were considered potential indicators of social risk referrals. Using stratified purposive sampling, we compared these discrete data with clinical notes to assess concordance of social risk referral documentation, and of the prevalence, types, and outcomes of such referrals.

Data Collection/Extraction Methods

Smartphrases were classified into three categories (likely, possible, or unlikely to indicate a social risk referral); 50 chart notes were sampled for each of the 25 most frequently used smartphrases in each category, and for 53 of the most frequently used procedure codes. A total of 6104 chart notes were reviewed.

Principal Findings

In 59% of chart notes where discrete data suggested a social risk referral occurred, there was no documentation of this in the note. Primary domains addressed were food insecurity (38%), financial stress (18%) and housing needs (18%). Common referral activities included providing contact information (26%), help with assistance applications (17%), and direct provision of resources (16%). Documentation indicated the patient received resources in 29% of notes.

Conclusions

EHR documentation of social risk referrals in structured data fields is inconsistent. Further work should establish best practices, especially given emerging policies that tie payments to documentation of social risk screening and intervention provision. Community health centers may struggle to use data elements such as smartphrases and procedure codes to monitor and report on their social risk referrals until standardized coding practices are established and effectively implemented.

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离散初级保健电子健康记录数据中社会风险相关转诊的识别:从一种新方法中吸取的教训。
目的:评估使用离散初级保健电子健康记录(EHR)数据在全国社区诊所网络中识别社会风险转诊的效用。数据来源和研究背景:主要数据摘自美国1459个社区诊所的OCHIN网络EHR(2016年6月至2022年2月)。研究设计:结构化数据元素包括913个常用的智能短语和53个程序代码,被认为是社会风险转介的潜在指标。采用分层有目的抽样,我们将这些离散数据与临床记录进行比较,以评估社会风险转诊文件的一致性,以及此类转诊的患病率、类型和结果。数据收集/提取方法:将智能短语分为三类(可能、可能或不太可能表明社会风险转介);每个类别中25个最常用的智能短语和53个最常用的程序代码分别抽取了50个图表注释。共审查了6104份图表说明。主要发现:在59%的图表注释中,离散数据表明发生了社会风险转诊,但在注释中没有这方面的文件。解决的主要领域是粮食不安全(38%)、财务压力(18%)和住房需求(18%)。常见的转介活动包括提供联系信息(26%)、协助申请(17%)和直接提供资源(16%)。文件显示,29%的病历中患者获得了资源。结论:结构化数据域的社会风险转诊EHR记录不一致。进一步的工作应确立最佳做法,特别是考虑到将付款与社会风险筛查和干预提供的文件联系起来的新政策。在建立和有效实施标准化编码实践之前,社区卫生中心可能难以使用智能短语和程序代码等数据元素来监测和报告其社会风险转诊情况。
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来源期刊
Health Services Research
Health Services Research 医学-卫生保健
CiteScore
4.80
自引率
5.90%
发文量
193
审稿时长
4-8 weeks
期刊介绍: Health Services Research (HSR) is a peer-reviewed scholarly journal that provides researchers and public and private policymakers with the latest research findings, methods, and concepts related to the financing, organization, delivery, evaluation, and outcomes of health services. Rated as one of the top journals in the fields of health policy and services and health care administration, HSR publishes outstanding articles reporting the findings of original investigations that expand knowledge and understanding of the wide-ranging field of health care and that will help to improve the health of individuals and communities.
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