Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2021 Aug 24:2021:9759016.
doi: 10.34133/2021/9759016. eCollection 2021.

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

Anusha Bompelli  1 , Yanshan Wang  2 , Ruyuan Wan  3 , Esha Singh  3 , Yuqi Zhou  4 , Lin Xu  5 , David Oniani  6 , Bhavani Singh Agnikula Kshatriya  7 , Joyce Joy E Balls-Berry  8 , Rui Zhang  9
Affiliations

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

Anusha Bompelli et al. Health Data Sci. .

Abstract

Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches.Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided.Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes.Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues.Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

PubMed Disclaimer

Conflict of interest statement

We declare no competing interests.

Figures

Figure 1
Figure 1
The flowchart of the article selection process.
Figure 2
Figure 2
Year trend of 79 reviewed studies.
Figure 3
Figure 3
Summary of number of reviewed studies by country.
Figure 4
Figure 4
Overview of the relationship schema between SBDH and disease.
Figure 5
Figure 5
Overview of SBDH data source and AI methods.
See this image and copyright information in PMC

References

    1. , “Social Determinants of Health - Healthy People 2030| http://health.gov,”, January 2021,https://health.gov/healthypeople/objectives-and-data/social-determinants.... - PMC - PubMed
    1. Sterling M. R., Ringel J. B., Pinheiro L. C., Safford M. M., Levitan E. B., Phillips E., Brown T. M., and Goyal P., “Social determinants of health and 90-day mortality after hospitalization for heart failure in the REGARDS study,” Journal of the American Heart Association, vol. 9, no. 9, article e014836, 2020 - PMC - PubMed
    1. Singh G. K., Daus G., Allender M., Ramey C., Martin E., Perry C., Reyes A., and Vedamuthu I., “Social determinants of health in the United States: addressing major health inequality trends for the nation, 1935-2016,” International Journal of MCH and AIDS (IJMA), vol. 6, no. 2, pp. 139-164, 2017 - PMC - PubMed
    1. Eppes C., Salahuddin M., Ramsey P. S., Davidson C., and Patel D. A., “Social determinants of health and severe maternal morbidity during delivery hospitalizations in texas [36L],” Obstetrics and Gynecology, vol. 135, p. 133S, 2020 - PubMed
    1. Bush M., “Addressing the Root Cause,” North Carolina Medical Journal, vol. 79, no. 1, pp. 26-29, 2018 - PubMed

Publication types

LinkOut - more resources