Social and behavioral determinants of health in the era of artificial intelligence with electronic health records: A scoping review

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 little research into how to make the most of SBDH information from EHRs. 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, and several NLP approaches for extracting SDOH from clinical literature. Discussion: The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using Natural Language Processing (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 disease, 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, and ultimately promoting health and health equity. Keywords: Social and Behavioral Determinants of Health, Artificial Intelligence, Electronic Health Records, Natural Language Processing, Predictive ModelComment: 32 pages, 5 figure

Assessing document section heterogeneity across multiple electronic health record systems for computational phenotyping: A case study of heart-failure phenotyping algorithm.

BackgroundThe incorporation of information from clinical narratives is critical for computational phenotyping. The accurate interpretation of clinical terms highly depends on their associated context, especially the corresponding clinical section information. However, the heterogeneity across different Electronic Health Record (EHR) systems poses challenges in utilizing the section information.ObjectivesLeveraging the eMERGE heart failure (HF) phenotyping algorithm, we assessed the heterogeneity quantitatively through the performance comparison of machine learning (ML) classifiers which map clinical sections containing HF-relevant terms across different EHR systems to standard sections in Health Level 7 (HL7) Clinical Document Architecture (CDA).MethodsWe experimented with both random forest models with sentence-embedding features and bidirectional encoder representations from transformers models. We trained MLs using an automated labeled corpus from an EHR system that adopted HL7 CDA standard. We assessed the performance using a blind test set (n = 300) from the same EHR system and a gold standard (n = 900) manually annotated from three other EHR systems.ResultsThe F-measure of those ML models varied widely (0.00-0.91%), indicating MLs with one tuning parameter set were insufficient to capture sections across different EHR systems. The error analysis indicates that the section does not always comply with the corresponding standardized sections, leading to low performance.ConclusionsWe presented the potential use of ML techniques to map the sections containing HF-relevant terms in multiple EHR systems to standard sections. However, the findings suggested that the quality and heterogeneity of section structure across different EHRs affect applications due to the poor adoption of documentation standards

The overview of assessing heterogeneity of clinical sections across three electronic health records using embedding-based machine learning approaches.

EHR = Electronic health records; HL7-CDA = Health Level 7—Clinical Document Architecture; ML = Machine Learning; RF = Random Forest; BERT = Bidirectional Encoder Representations from Transformers; GEC = General Electronic Centricity EHR.</p

Corresponding sections and subsections among test corpora.

Corresponding sections and subsections among test corpora.</p

Case^a and non-case^b for train set in GEC and the heart failure relevant clinical section in test sets.

Casea and non-caseb for train set in GEC and the heart failure relevant clinical section in test sets.</p

Differentiation of corresponding sections and subsections among corpora.

aOne clinical document standard of IC Chart Electronic Health Record (EHR) consists of sections ‘S,’ ‘O’, ‘A,’ and ‘P.’; The “Impression and Plan” section of the Cerner EHR, corresponds to “the Assessment and Plan” section of the General Electronic Centricity (GEC) EHR, which contains information on the “Problem,” “Medications,” and other sections; “Chief Complaint and Reason for Visit” sections of the Epic EHR are similar to “Problem” sections of the GEC EHR. (DOCX)</p

The algorithm of section classifiers.

BackgroundThe incorporation of information from clinical narratives is critical for computational phenotyping. The accurate interpretation of clinical terms highly depends on their associated context, especially the corresponding clinical section information. However, the heterogeneity across different Electronic Health Record (EHR) systems poses challenges in utilizing the section information.ObjectivesLeveraging the eMERGE heart failure (HF) phenotyping algorithm, we assessed the heterogeneity quantitatively through the performance comparison of machine learning (ML) classifiers which map clinical sections containing HF-relevant terms across different EHR systems to standard sections in Health Level 7 (HL7) Clinical Document Architecture (CDA).MethodsWe experimented with both random forest models with sentence-embedding features and bidirectional encoder representations from transformers models. We trained MLs using an automated labeled corpus from an EHR system that adopted HL7 CDA standard. We assessed the performance using a blind test set (n = 300) from the same EHR system and a gold standard (n = 900) manually annotated from three other EHR systems.ResultsThe F-measure of those ML models varied widely (0.00–0.91%), indicating MLs with one tuning parameter set were insufficient to capture sections across different EHR systems. The error analysis indicates that the section does not always comply with the corresponding standardized sections, leading to low performance.ConclusionsWe presented the potential use of ML techniques to map the sections containing HF-relevant terms in multiple EHR systems to standard sections. However, the findings suggested that the quality and heterogeneity of section structure across different EHRs affect applications due to the poor adoption of documentation standards.</div

The distribution of the sections and subsections in the test.

The distribution of the sections and subsections in the test.</p

Evaluation of binary embedding-based ML models regarding the section.

Evaluation of binary embedding-based ML models regarding the section.</p