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Patients enrolled in Medicaid have significantly higher social needs (SNs) than others. Using claims and electronic health records (EHRs) data, managed care organizations (MCOs) could systemically identify high-risk patients with SNs and develop population health management interventions. Impact of SNs on models predicting health care utilization and costs was assessed. This retrospective study included claims and EHRs data on 39,267 patients younger than age 65 years who were continuously enrolled during 2018-2019 in a Medicaid-managed care plan. SN marker was developed suggesting presence of International Classification of Diseases, 10th revision codes in any of the 5 SN domains. Impact of SN marker was compared across demographic and 2 diagnosis-based (ie, Charlson and Adjusted Clinical Groups risk score) prediction models of emergency department (ED) visit and hospitalizations, and total, medical, and pharmacy costs. After combining data sources, prevalence of documented SN marker increased from 11% and 13% to 18% of the study population across claims, EHRs, and both combined, respectively. SN marker improved predictions of demographic models for all utilization and total costs outcomes (area under the curve [AUC] of ED model increased from 0.57 to 0.61 and R 2 of total cost model increased from 10.9 to 12.2). In both diagnosis-based models, adding SN marker marginally improved outcomes prediction (AUC of ED model increased from 0.65 to 0.66). This study demonstrated feasibility of using claims and EHRs data to systematically capture SNs and incorporate in prediction models that could enable MCOs and policy makers to adjust and develop effective population health interventions.

Introduction: Despite the growing efforts to standardize coding for social determinants of health (SDOH), they are infrequently captured in electronic health records (EHRs). Most SDOH variables are still captured in the unstructured fields (i.e., free-text) of EHRs. In this study we attempt to evaluate a practical text mining approach (i.e., advanced pattern matching techniques) in identifying phrases referring to housing issues, an important SDOH domain affecting value-based healthcare providers, using EHR of a large multispecialty medical group in the New England region, United States. To present how this approach would help the health systems to address the SDOH challenges of their patients we assess the demographic and clinical characteristics of patients with and without housing issues and briefly look into the patterns of healthcare utilization among the study population and for those with and without housing challenges. Methods: We identified five categories of housing issues [i.e., homelessness current (HC), homelessness history (HH), homelessness addressed (HA), housing instability (HI), and building quality (BQ)] and developed several phrases addressing each one through collaboration with SDOH experts, consulting the literature, and reviewing existing coding standards. We developed pattern-matching algorithms (i.e., advanced regular expressions), and then applied them in the selected EHR. We assessed the text mining approach for recall (sensitivity) and precision (positive predictive value) after comparing the identified phrases with manually annotated free-text for different housing issues. Results: The study dataset included EHR structured data for a total of 20,342 patients and 2,564,344 free-text clinical notes. The mean (SD) age in the study population was 75.96 (7.51). Additionally, 58.78% of the cohort were female. BQ and HI were the most frequent housing issues documented in EHR free-text notes and HH was the least frequent one. The regular expression methodology, when compared to manual annotation, had a high level of precision (positive predictive value) at phrase, note, and patient levels (96.36, 95.00, and 94.44%, respectively) across different categories of housing issues, but the recall (sensitivity) rate was relatively low (30.11, 32.20, and 41.46%, respectively). Conclusion: Results of this study can be used to advance the research in this domain, to assess the potential value of EHR's free-text in identifying patients with a high risk of housing issues, to improve patient care and outcomes, and to eventually mitigate socioeconomic disparities across individuals and communities., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2021 Hatef, Singh Deol, Rouhizadeh, Li, Eibensteiner, Monsen, Bratslaver, Senese and Kharrazi.)

Purpose: Social and behavioral determinants of health (SBDH) are important factors that affect the health of individuals but are not routinely captured in a structured and systematic manner in electronic health records (EHRs). The purpose of this study is to generate recommendations for systematic implementation of SBDH data collection in EHRs through (1) reviewing SBDH conceptual and theoretical frameworks and (2) eliciting stakeholder perspectives on barriers to and facilitators of using SBDH information in the EHR and priorities for data collection., Method: The authors reviewed SBDH frameworks to identify key social and behavioral variables and conducted focus groups and interviews with 17 clinicians and researchers at Johns Hopkins Health System between March and May 2018. Transcripts were coded and common themes were extracted to understand the barriers to and facilitators of accessing SBDH information., Results: The authors found that although the frameworks agreed that SBDH affect health outcomes, the lack of model consensus complicates the development of specific recommendations for the prioritization of SBDH data collection. Study participants recognized the importance of SBDH information and individual health and agreed that patient-reported information should be captured, but clinicians and researchers cited different priorities for which variables are most important. For the few SBDH variables that are captured, participants reported that data were often incomplete, unclear, or inconsistent, affecting both researcher and clinician responses to SBDH barriers to health., Conclusions: Health systems need to identify and prioritize the systematic implementation of collection of a high-impact but limited list of SBDH variables in the EHR. These variables should affect care and be amenable to change and collection should be integrated into clinical workflows. Improved data collection of SBDH variables can lead to a better understanding of how SBDH affect health outcomes and ways to better address underlying health disparities that need urgent action., (Copyright © 2021 by the Association of American Medical Colleges.)

We investigated the role of both individual-level social needs and community-level social determinants of health (SDOH) in explaining emergency department (ED) utilization rates. We also assessed the potential synergies between the two levels of analysis and their combined effect on patterns of ED visits. We extracted electronic health record (EHR) data between July 2016 and June 2020 for 1,308,598 unique Maryland residents who received care at Johns Hopkins Health System, of which 28,937 (2.2%) patients had at least one documented social need. There was a negative correlation between median household income in a neighborhood with having a social need such as financial resource strain, food insecurity, and residential instability (correlation coefficient: -0.05, -0.01, and − 0.06, p = 0, respectively). In a multilevel model with random effects after adjusting for other factors, living in a more disadvantaged neighborhood was found to be significantly associated with ED utilization statewide and within Baltimore City (OR: 1.005, 95% CI: 1.003–1.007 and 1.020, 95% CI: 1.017–1.022, respectively). However, individual-level social needs appeared to enhance the statewide effect of living in a more disadvantaged neighborhood with the OR for the interaction term between social needs and SDOH being larger, and more positive, than SDOH alone (OR: 1.012, 95% CI: 1.011–1.014). No such moderation was found in Baltimore City. To our knowledge, this study is one of the first attempts by a major academic healthcare system to assess the combined impact of patient-level social needs in association with community-level SDOH on healthcare utilization and can serve as a baseline for future studies using EHR data linked to population-level data to assess such synergistic association. [ABSTRACT FROM AUTHOR]

As the US health care system moves to expand access to and quality of medical care, the importance of addressing patient-level social needs and community-level social determinants of health (SDOH) is increasingly being recognized. This study evaluates individual- and community-level needs of housing (one of the SDOH domains) across the patient population of an academic medical center and explores how the level of housing needs impacts health care utilization. The authors performed a descriptive analysis of housing issues identified in both structured and unstructured (eg, clinical notes) data extracted from the electronic health record (EHR) and compared this to community-level characteristics of patients' neighborhood as measured by the Area Deprivation Index. Multivariate analyses were performed to assess the association between these and other factors on the frequency of service encounters. Among the 1,034,683 study participants, 59,703 (5.8%) had at least 1 housing issue identified in their EHR from structured or unstructured data combined. After adjusting for other factors, patients with housing instability and homelessness had 49% and 34% more encounters with the health care system compared to patients without housing issues (P < 0.00001). Patients living in the most disadvantaged neighborhoods had 55% more encounters with the health care system compared to those living in the most advantaged neighborhoods (P < 0.00001). This data collection approach and findings can inform health care systems aiming to make use of their EHRs and community-level SDOH information to provide a full assessment of patients' social needs and challenges. [ABSTRACT FROM AUTHOR]

Research Objective: International Classification of Diseases (ICD) coding system have codes for recording of social determinants of health (SDOH); however, documentation of non‐clinical issues in electronic health records (EHRs) is infrequent compared to medical conditions. ICD codes in EHRs for SDOH identification, therefore, may under‐report patients with social needs and risks, which makes it difficult for healthcare systems to target "high risk" patients for interventions addressing social needs. SDOH may be discussed with healthcare providers during visits and, therefore, recorded in EHR free‐text notes (a.k.a, providers' notes). These notes might provide a more accurate accounting of SDOH; however, traditional approaches for review and abstraction of patient information from medical record notes is laborious, expensive, and slow. Recent developments in text mining and natural language processing (NLP) of digitized text allows for reliable, low cost, and rapid extraction of information from EHRs. In this pilot project we evaluated whether an NLP algorithm could extract valid measures of SDOH from Epic‐based EHRs in three healthcare systems: Johns Hopkins Health System (JHHS), Kaiser Permanente Mid‐Atlantic States (KPMAS), and KP Southern California (KPSCcal). The focus of our study was residential instability (i.e., homelessness and housing insecurity). Study Design: The study was conducted independently, in a parallel and coordinated framework across sites. The validation assessment and NLP algorithm logic were identical across sites; however, the "gold standard" for assessment of algorithm validity differed according to data availability. Using the EntityRuler module of spaCy 2.3 Python toolkit, we created a rule‐based NLP system made up of 61 expert‐developed patterns that, if present, would represent residential instability. Our patterns included word 'lemmas' and base forms to account for morphological variations (e.g., singular and plural forms) as well as substitutions of different prepositions (e.g., about and for), and synonym words (e.g., house, apartment, and home). We calibrated and then validated the algorithm using a split sample approach. Validity was assessed at each site by measures of sensitivity and specificity. Population Studied: Beneficiaries ≥18 years of age during 2016 through 2019 who received care at JHHS, KPMAS, KPSCal. Principal Findings: The following table presents the characteristics of the study population and performance of the NLP algorithm at each study site. JHHSKPMASKPScalStudy Population (Patient No.)~1,200,000~1,600,000~4,700,000NLP ValidationGold Standard MethodSDOH QuestionnaireSDOH QuestionnaireSDOH ICD codes Manual AnnotationSample SizePatients/ Response No. (with/without residential Instability)1000(500+/ 500‐)8197(833+,7364‐)300(150+/150‐)Clinical Note No.134,06278,8259575NLP Algorithm PerformanceSensitivity0.840.610.96Specificity0.960.870.97 Conclusions: The consistent performance of this NLP algorithm to identify residential instability in three different healthcare systems suggests the algorithm is generalizable. The consistent and relatively high sensitivity and specificity demonstrates the algorithm's validity. Implications for Policy or Practice: Development of generalizable NLP algorithms with promising performance will enhance the value of EHRs to identify at risk patients across different health systems, to improve patient care and outcomes, and to mitigate socioeconomic disparities across individuals and communities. Primary Funding Source: Johns Hopkins and Kaiser Permanente Research Collaboration Committee Pilot Awards. [ABSTRACT FROM AUTHOR]