Barriers and facilitators of obtaining SDoH of patients with cancer through the EHR using natural language processing technology: A qualitative study (Preprint)

Author:

Alpert JordanORCID,Kim Hyehyun (Julia),McDonnell Cara,Guo YiORCID,George Thomas J.ORCID,Bian JiangORCID,Wu Yonghui

Abstract

BACKGROUND

Social determinants of health (SDoH), such as geographic neighborhoods, access to healthcare, education, and social structure are important factors affecting people’s health and health outcomes. SDoH of patients are scarcely documented in a discrete format in electronic health records (EHRs) but are often available in free-text clinical narratives such as physician notes. Innovative methods like natural language processing (NLP) are being developed to identify and extract SDoH from EHRs, but it is imperative that the input of key stakeholders is included as NLP systems are designed.

OBJECTIVE

Understand the feasibility, challenges, and benefits of developing an NLP system to uncover SDoH from clinical narratives by conducting interviews with key stakeholders: 1) clinicians, 2) data analysts, 3) citizen scientists and 4) patient navigators.

METHODS

Individuals who frequently work with SDoH data were invited to participate in in-depth, semi-structured interviews. All interviews were recorded and subsequently transcribed. After coding transcripts and developing a codebook, the constant comparative method was used to generate themes.

RESULTS

A total of 16 participants were interviewed (five data analysts, four patient navigators, four physicians, and three citizen scientists). Two themes emerged related to collecting SDoH: 1) the importance of SDoH data and 2) SDoH arises during patient-clinician communication. The challenges of collecting SDoH data included: 1) informal communication and 2) the need for expertise and knowledge about SDoH. Ways of improving how SDoH data can be incorporated into health services research and patient care were to 1) empower patients and 2) make the data actionable.

CONCLUSIONS

Extracting SDoH from EHRs was considered valuable and necessary, but obstacles such as narrative data format can make the process difficult. NLP can be a potential solution, but as the technology is developed, it is important to consider how key stakeholders document SDoH, apply the NLP systems, and use the extracted SDoH in health outcome studies.natural language processing, qualitative, social determinants of health, electronic health records

Publisher

JMIR Publications Inc.

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