Extracting social determinants of health from electronic health records using natural language processing: a systematic review

Author:

Patra Braja G1ORCID,Sharma Mohit M1ORCID,Vekaria Veer1ORCID,Adekkanattu Prakash2,Patterson Olga V34ORCID,Glicksberg Benjamin5ORCID,Lepow Lauren A5,Ryu Euijung6,Biernacka Joanna M6,Furmanchuk Al’ona7,George Thomas J8ORCID,Hogan William9ORCID,Wu Yonghui8,Yang Xi8,Bian Jiang8ORCID,Weissman Myrna10,Wickramaratne Priya10,Mann J John10,Olfson Mark10,Campion Thomas R12ORCID,Weiner Mark1ORCID,Pathak Jyotishman1ORCID

Affiliation:

1. Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA

2. Information Technologies and Services, Weill Cornell Medicine, New York, New York, USA

3. Department of Internal Medicine, Division of Epidemiology, University of Utah, Salt Lake City, Utah, USA

4. US Department of Veterans Affairs, Salt Lake City, Utah, USA

5. Icahn School of Medicine at Mount Sinai, New York, New York, USA

6. Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA

7. Northwestern University, Chicago, Illinois, USA

8. Department of Health Outcomes and Biomedical Informatics, University of Florida, Gainesville, Florida, USA

9. Division of Hematology & Oncology, Department of Medicine, College of Medicine, University of Florida, Gainesville, Florida, USA, and

10. Vagelos College of Physicians and Surgeons, Columbia University, New York, New York, USA

Abstract

Abstract Objective Social determinants of health (SDoH) are nonclinical dispositions that impact patient health risks and clinical outcomes. Leveraging SDoH in clinical decision-making can potentially improve diagnosis, treatment planning, and patient outcomes. Despite increased interest in capturing SDoH in electronic health records (EHRs), such information is typically locked in unstructured clinical notes. Natural language processing (NLP) is the key technology to extract SDoH information from clinical text and expand its utility in patient care and research. This article presents a systematic review of the state-of-the-art NLP approaches and tools that focus on identifying and extracting SDoH data from unstructured clinical text in EHRs. Materials and Methods A broad literature search was conducted in February 2021 using 3 scholarly databases (ACL Anthology, PubMed, and Scopus) following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. A total of 6402 publications were initially identified, and after applying the study inclusion criteria, 82 publications were selected for the final review. Results Smoking status (n = 27), substance use (n = 21), homelessness (n = 20), and alcohol use (n = 15) are the most frequently studied SDoH categories. Homelessness (n = 7) and other less-studied SDoH (eg, education, financial problems, social isolation and support, family problems) are mostly identified using rule-based approaches. In contrast, machine learning approaches are popular for identifying smoking status (n = 13), substance use (n = 9), and alcohol use (n = 9). Conclusion NLP offers significant potential to extract SDoH data from narrative clinical notes, which in turn can aid in the development of screening tools, risk prediction models, and clinical decision support systems.

Funder

NIH

Publisher

Oxford University Press (OUP)

Subject

Health Informatics

Cited by 105 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3