Development of an evidence- and consensus-based Digital Healthcare Equity Framework.

Objective: To develop an evidence- and consensus-based Digital Healthcare Equity Framework (the Framework) that guides users in intentionally considering equity in healthcare solutions that involve digital technologies.

Materials And Methods: We conducted an environmental scan including a scoping review of the literature and key informant interviews with health equity and digital healthcare technology thought leaders and convened a technical expert panel (TEP).

Results: We grouped similar concepts from the scoping review and key informant interviews, synthesized them into several primary domains and subdomains, and presented the composite list of domains and subdomains to the TEP for their input.

Higher Percentage of Virtual Primary Care Associated With Minimal Differences in Achievement of Quality Metrics.

Objective: To test the impact of virtual care usage on quality metrics used for performance measurement.

Background: Virtual care improves access to primary care; however, the quality of care must not be adversely impacted by its use.

Methods: This is a mixed-design etiologic study using data from patients receiving primary care in a large, regional health system from January 2020 through December 2021.

Enhancement of a social risk score in the electronic health record to identify social needs among medically underserved patients: using structured data and free-text provider notes.

Objective: To improve the performance of a social risk score (a predictive risk model) using electronic health record (EHR) structured and unstructured data.

Materials And Methods: We used EPIC-based EHR data from July 2016 to June 2021 and linked it to community-level data from the US Census American Community Survey. We identified predictors of interest within the EHR structured data and applied natural language processing (NLP) techniques to identify patients' social needs in the EHR unstructured data.

Piloting a Clinical Decision Support Tool to Identify Patients With Social Needs and Provide Navigation Services and Referral to Community-Based Organizations: Protocol for a Randomized Controlled Trial.

Background: Social needs and social determinants of health (SDOH) significantly outrank medical care when considering the impact on a person's length and quality of life, resulting in poor health outcomes and worsening life expectancy. Integrating social needs and SDOH data along with clinical risk information within operational clinical decision support (CDS) systems built into electronic health records (EHRs) is an effective approach to addressing health-related social needs. To achieve this goal, applied research is needed to develop EHR-integrated CDS tools and closed-loop referral systems and implement and test them in the digital and clinical workflows at health care systems and collaborating community-based organizations (CBOs).

Effectiveness of telehealth versus in-person care during the COVID-19 pandemic: a systematic review.

In this systematic review, we compared the effectiveness of telehealth with in-person care during the pandemic using PubMed, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials from March 2020 to April 2023. We included English-language, U.S.

Development of a Social Risk Score in the Electronic Health Record to Identify Social Needs Among Underserved Populations: Retrospective Study.

Background: Patients with unmet social needs and social determinants of health (SDOH) challenges continue to face a disproportionate risk of increased prevalence of disease, health care use, higher health care costs, and worse outcomes. Some existing predictive models have used the available data on social needs and SDOH challenges to predict health-related social needs or the need for various social service referrals. Despite these one-off efforts, the work to date suggests that many technical and organizational challenges must be surmounted before SDOH-integrated solutions can be implemented on an ongoing, wide-scale basis within most US-based health care organizations.

Latent Class Analysis of Social Needs in Medicaid Population and Its Impact on Risk Adjustment Models.

Background: A growing number of US states are implementing programs to address the social needs (SNs) of their Medicaid populations through managed care contracts. Incorporating SN might also improve risk adjustment methods used to reimburse Medicaid providers.

Objectives: Identify classes of SN present within the Medicaid population and evaluate the performance improvement in risk adjustment models of health care utilization and cost after incorporating SN classes.

Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system.

Objectives: To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs).

Materials And Methods: We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development.

Assessing Patient and Community-Level Social Factors; The Synergistic Effect of Social Needs and Social Determinants of Health on Healthcare Utilization at a Multilevel Academic Healthcare System.

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.

Impact of Social Needs in Electronic Health Records and Claims on Health Care Utilization and Costs Risk-Adjustment Models Within Medicaid Population.

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.

Outcomes of In-Person and Telehealth Ambulatory Encounters During COVID-19 Within a Large Commercially Insured Cohort.

Importance: Since the start of the COVID-19 pandemic, few studies have assessed the association of telehealth with outcomes of care, including patterns of health care use after the initial encounter.

Objective: To assess the association of telehealth and in-person visits with outcomes of care during the COVID-19 pandemic.

Design, Setting, And Participants: This cohort study assessed continuously enrolled members in private health plans of the Blue Cross and Blue Shield Association from July 1, 2019, to December 31, 2020.

Development and assessment of a natural language processing model to identify residential instability in electronic health records' unstructured data: a comparison of 3 integrated healthcare delivery systems.

Objective: To evaluate whether a natural language processing (NLP) algorithm could be adapted to extract, with acceptable validity, markers of residential instability (ie, homelessness and housing insecurity) from electronic health records (EHRs) of 3 healthcare systems.

Materials And Methods: We included patients 18 years and older who received care at 1 of 3 healthcare systems from 2016 through 2020 and had at least 1 free-text note in the EHR during this period. We conducted the study independently; the NLP algorithm logic and method of validity assessment were identical across sites.

Internet Access, Social Risk Factors, and Web-Based Social Support Seeking Behavior: Assessing Correlates of the "Digital Divide" Across Neighborhoods in The State of Maryland.

We aimed to empirically measure the degree to which there is a "digital divide" in terms of access to the internet at the small-area community level within the State of Maryland and the City of Baltimore and to assess the relationship and association of this divide with community-level SDOH risk factors, community-based social service agency location, and web-mediated support service seeking behavior. To assess the socio-economic characteristics of the neighborhoods across the state, we calculated the Area Deprivation Index (ADI) using the U.S.

Measuring the Value of a Practical Text Mining Approach to Identify Patients With Housing Issues in the Free-Text Notes in Electronic Health Record: Findings of a Retrospective Cohort Study.

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.

Assessing the association between area deprivation index on COVID-19 prevalence: a contrast between rural and urban U.S. jurisdictions.

Background: The COVID-19 pandemic has impacted communities differentially, with poorer and minority populations being more adversely affected. Prior rural health research suggests such disparities may be exacerbated during the pandemic and in remote parts of the U.S.

Differential impact of mitigation policies and socioeconomic status on COVID-19 prevalence and social distancing in the United States.

Background: The spread of COVID-19 has highlighted the long-standing health inequalities across the U.S. as neighborhoods with fewer resources were associated with higher rates of COVID-19 transmission.

In-Person and Telehealth Ambulatory Contacts and Costs in a Large US Insured Cohort Before and During the COVID-19 Pandemic.

Importance: This study assesses the role of telehealth in the delivery of care at the start of the COVID-19 pandemic.

Objectives: To document patterns and costs of ambulatory care in the US before and during the initial stage of the pandemic and to assess how patient, practitioner, community, and COVID-19-related factors are associated with telehealth adoption.

Design, Setting, And Participants: This is a cohort study of working-age persons continuously enrolled in private health plans from March 2019 through June 2020.

Social and Behavioral Variables in the Electronic Health Record: A Path Forward to Increase Data Quality and Utility.

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.

Early relaxation of community mitigation policies and risk of COVID-19 resurgence in the United States.

This study aimed to assess the impact of coronavirus disease (COVID-19) prevalence in the United States in the week leading to the relaxation of the stay-at-home orders (SAH) on future prevalence across states that implemented different SAH policies. We used data on the number of confirmed COVID-19 cases as of August 21, 2020 on county level. We classified states into four groups based on the 7-day change in prevalence and the state's approach to SAH policy.

Assessing the Impact of Neighborhood Socioeconomic Characteristics on COVID-19 Prevalence Across Seven States in the United States.

The spread of Coronavirus Disease 2019 (COVID-19) across the United States has highlighted the long-standing nationwide health inequalities with socioeconomically challenged communities experiencing a higher burden of the disease. We assessed the impact of neighborhood socioeconomic characteristics on the COVID-19 prevalence across seven selected states (i.e.

Including Social and Behavioral Determinants in Predictive Models: Trends, Challenges, and Opportunities.

In an era of accelerated health information technology capability, health care organizations increasingly use digital data to predict outcomes such as emergency department use, hospitalizations, and health care costs. This trend occurs alongside a growing recognition that social and behavioral determinants of health (SBDH) influence health and medical care use. Consequently, health providers and insurers are starting to incorporate new SBDH data sources into a wide range of health care prediction models, although existing models that use SBDH variables have not been shown to improve health care predictions more than models that use exclusively clinical variables.

Assessing the Impact of Social Needs and Social Determinants of Health on Health Care Utilization: Using Patient- and Community-Level Data.

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.