Barriers and Facilitators of Obtaining Social Determinants of Health of Patients With Cancer Through the Electronic Health Record Using Natural Language Processing Technology: Qualitative Feasibility Study With Stakeholder Interviews

Abstract

Background Social determinants of health (SDoH), such as geographic neighborhoods, access to health care, education, and social structure, are important factors affecting people’s health and health outcomes. The 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 This study aims to understand the feasibility, challenges, and benefits of developing an NLP system to uncover SDoH from clinical narratives by conducting interviews with key stakeholders: (1) oncologists, (2) data analysts, (3) citizen scientists, and (4) patient navigators. Methods Individuals who frequently work with SDoH data were invited to participate in semistructured 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 (5 data analysts, 4 patient navigators, 4 physicians, and 3 citizen scientists). Three main themes emerged, accompanied by subthemes. The first theme, importance and approaches to obtaining SDoH, describes how every participant (n=16, 100%) regarded SDoH as important. In particular, proximity to the hospital and income levels were frequently relied upon. Communication about SDoH typically occurs during the initial conversation with the oncologist, but more personal information is often acquired by patient navigators. The second theme, SDoH exists in numerous forms, exemplified how SDoH arises during informal communication and can be difficult to enter into the EHR. The final theme, incorporating SDoH into health services research, addresses how more informed SDoH can be collected. One strategy is to empower patients so they are aware about the importance of SDoH, as well as employing NLP techniques to make narrative data available in a discrete format, which can provide oncologists with actionable data summaries. 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.

A Text Analysis Method for Student Learning Feedback on Network Teaching Platform Based on Natural Language Processing

“…Alpert Jordan conducts text screening and categorization of patient electronic health records to summarize the social determinants of health, and then explores the use of this factor to guide patient health management [1]. Jie Hu conducted a case study based on the "Voice of Users" dataset of SAIC-GM Wuling.…”

Large-scale identification of social and behavioral determinants of health from clinical notes: Comparison of Latent Semantic Indexing and Generative Pretrained Transformer (GPT) models

“…4 However, screening tools add a significant burden on the healthcare staff at a time when provider burnout is a major concern. 5…”

“…4 However, screening tools add a significant burden on the healthcare staff at a time when provider burnout is a major concern. 5 SBDH topics may arise during informal communications between the patient and healthcare provider, which are often documented in the clinical notes rather than the structured fields in the EHR. 5 As an alternative strategy to screening questionnaires and diagnosis codes, several groups have evaluated SBDH documented in the clinical notes in the EHR.…”

“…5 SBDH topics may arise during informal communications between the patient and healthcare provider, which are often documented in the clinical notes rather than the structured fields in the EHR. 5 As an alternative strategy to screening questionnaires and diagnosis codes, several groups have evaluated SBDH documented in the clinical notes in the EHR. Navathe et al reported that the highest rates of social characteristics were found in physician notes and that the frequency of six out of the seven social characteristics increased when comparing data from physician notes with billing codes.…”