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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]

The article presents a study which explored innovative interventions in which Maternal Mortality Review Committees (MMRCs) actively engaged physician trainees, enhancing their understanding of maternal population health. Topics discussed include evaluation method and findings, challenges to the training model, and analysis of integrating trainees into local and state MMRCs.

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. From those, we derived the following domains for the Framework: (1) Patient and Community Characteristics, (2) Health System Characteristics, and (3) Health Information Technology Characteristics. We structured the Framework around the following 5 phases of the digital healthcare lifecycle: planning, development, acquisition, implementation/maintenance, and monitoring/improvement/equity assessment., Discussion: The proposed Framework is designed to specify the aspects that need to be considered in a systematic and intentional approach to ensure digital healthcare solutions improve, and not exacerbate, healthcare inequities., Conclusion: The proposed Framework serves as a tool to help users and other stakeholders assess whether their healthcare solutions that involve digital technologies are equitable at every phase of the digital healthcare lifecycle., Competing Interests: None of the authors have any affiliation or financial involvement that conflicts with the material presented in this framework., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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. We performed logistic regression models with and without information from the unstructured data (Models I and II) and compared their performance with generalized estimating equation (GEE) models with and without the unstructured data (Models III and IV)., Results: The logistic model (Model I) performed well (Area Under the Curve [AUC] 0.703, 95% confidence interval [CI] 0.701:0.705) and the addition of EHR unstructured data (Model II) resulted in a slight change in the AUC (0.701, 95% CI 0.699:0.703). In the logistic models, the addition of EHR unstructured data resulted in an increase in the area under the precision-recall curve (PRC 0.255, 95% CI 0.254:0.256 in Model I versus 0.378, 95% CI 0.375:0.38 in Model II). The GEE models performed similarly to the logistic models and the addition of EHR unstructured data resulted in a slight change in the AUC (0.702, 95% CI 0.699:0.705 in Model III versus 0.699, 95% CI 0.698:0.702 in Model IV)., Discussion: Our work presents the enhancement of a novel social risk score that integrates community-level data with patient-level data to systematically identify patients at increased risk of having future social needs for in-depth assessment of their social needs and potential referral to community-based organizations to address these needs., Conclusion: The addition of information on social needs extracted from unstructured EHR resulted in an improved prediction of positive cases presented by the improvement in the PRC., Competing Interests: The authors have no competing interests to declare., (© The Author(s) 2024. Published by Oxford University Press on behalf of the American Medical Informatics Association.)

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)., Objective: This study aims to describe the protocol for a mixed methods study including a randomized controlled trial and a qualitative phase assessing the feasibility, acceptability, and effectiveness of an EHR-integrated digital platform to identify patients with social needs and provide navigation services and closed-loop referrals to CBOs to address their social needs., Methods: The randomized controlled trial will enroll and randomize adult patients living in socioeconomically challenged neighborhoods in Baltimore City receiving care at a single academic health care institution in the 3-month intervention (using the digital platform) or the 3-month control (standard-of-care assessment and addressing of social needs) arms (n=295 per arm). To evaluate the feasibility and acceptability of the digital platform and its impact on the clinical and digital workflow and patient care, we will conduct focus groups with the care teams in the health care system (eg, clinical providers, social workers, and care managers) and collaborating CBOs. The outcomes will be the acceptability, feasibility, and effectiveness of the CDS tool and closed-loop referral system., Results: This clinical trial opened to enrollment in June 2023 and will be completed in March 2025. Initial results are expected to be published in spring 2025. We will report feasibility outcome measures as weekly use rates of the digital platform. The acceptability outcome measure will be the provider's and patient's responses to the truthfulness of a statement indicating a willingness to use the platform in the future. Effectiveness will be measured by tracking a 3-month change in identified social needs and provided navigation services as well as clinical outcomes such as hospitalization and emergency department visits., Conclusions: The results of this investigation are expected to contribute to our understanding of the use of digital interventions and the implementation of such interventions in digital and clinical workflows to enhance the health care system and CBO ability related to social needs assessment and intervention. These results may inform the construction of a future multi-institutional trial designed to test the effectiveness of this intervention across different health care systems and care settings., Trial Registration: ClinicalTrials.gov NCT05574699; https://clinicaltrials.gov/study/NCT05574699., International Registered Report Identifier (irrid): DERR1-10.2196/57316., (©Elham Hatef, Thomas Richards, Kristin Topel, Sofia Hail, Christopher Kitchen, Katherine Shaw, Talan Zhang, Elyse C Lasser, Jonathan P Weiner. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 23.07.2024.)