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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.-healthcare relevant studies comparing telehealth with in-person care conducted after the onset of the pandemic. Two reviewers independently screened search results, serially extracted data, and independently assessed the risk of bias and strength of evidence. We identified 77 studies, the majority of which (47, 61%) were judged to have a serious or high risk of bias. Differences, if any, in healthcare utilization and clinical outcomes between in-person and telehealth care were generally small and/or not clinically meaningful and varied across the type of outcome and clinical area. For process outcomes, there was a mostly lower rate of missed visits and changes in therapy/medication and higher rates of therapy/medication adherence among patients receiving an initial telehealth visit compared with those receiving in-person care. However, the rates of up-to-date labs/paraclinical assessment were also lower among patients receiving an initial telehealth visit compared with those receiving in-person care. Most studies lacked a standardized approach to assessing outcomes. While we refrain from making an overall conclusion about the performance of telehealth versus in-person visits the use of telehealth is comparable to in-person care across a variety of outcomes and clinical areas. As we transition through the COVID-19 era, models for integrating telehealth with traditional care become increasingly important, and ongoing evaluations of telehealth will be particularly valuable. [ABSTRACT FROM AUTHOR]

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]

There is growing recognition that social and behavioral risk factors impact population health outcomes. Interventions that target these risk factors can improve health outcomes. This study presents a review of existing literature and proposes a conceptual framework for the integration of social and behavioral data into population health analytics platforms. The authors describe several use cases for these platforms at the patient, health system, and community levels, and align these use cases with the different types of prevention identified by the Centers for Disease Control and Prevention. They then detail the potential benefits of these use cases for different health system stakeholders and explore currently available and potential future sources of social and behavioral domains data. Also noted are several potential roadblocks for these analytic platforms, including limited data interoperability, expense of data acquisition, and a lack of standardized technical terminology for socio-behavioral factors. [ABSTRACT FROM AUTHOR]

The premise of this project was that social and behavioral determinants of health (SBDH) affect the use of healthcare services and outcomes for patients in an integrated healthcare system such as the Veterans Health Administration (VHA), and thus individual patient level socio-behavioral factors in addition to the neighborhood characteristics and geographically linked factors could add information beyond medical factors mostly considered in clinical decision making, patient care, and population health. To help VHA better address SBDH risk factors for the veterans it cares for within its primary care clinics, we proposed a conceptual and analytic framework, a set of evidence-based measures, and their data source. The framework and recommended SBDH metrics can provide a road map for other primary care-centric healthcare organizations wishing to use health analytic tools to better understand how SBDH affect health outcomes. [ABSTRACT FROM AUTHOR]