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      Differences by Race in Outcomes of an In-Person Training Intervention on Use of an Inpatient Portal : A Secondary Analysis of a Randomized Clinical Trial

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          Key Points

          Question

          Does the effectiveness of patient training and portal functionality interventions implemented to increase patient portal use differ by racial groups?

          Findings

          In a secondary analysis of a randomized clinical trial of 2892 participants, Black participants had lower frequency of portal use compared with White participants, but the in-person training (compared with a training video) and the full set of portal functions (compared with a limited set of functions) interventions were not different in Black individuals and White individuals at increasing inpatient portal use.

          Meaning

          These findings suggest that despite evidence that in-person training and robust portal functionality increased use across participants of all races, Black individuals still used the portal less than White individuals.

          Abstract

          This secondary analysis of a randomized clinical trial assesses differences of the effectiveness of patient training and portal functionality interventions implemented to impact portal use by race.

          Abstract

          Importance

          Differences in patient use of health information technologies by race can adversely impact equitable access to health care services. While this digital divide is well documented, there is limited evidence of how health care systems have used interventions to narrow the gap.

          Objective

          To compare differences in the effectiveness of patient training and portal functionality interventions implemented to increase portal use among racial groups.

          Design, Setting, and Participants

          This secondary analysis used data from a randomized clinical trial conducted from December 15, 2016, to August 31, 2019. Data were from a single health care system and included 6 noncancer hospitals. Participants were patients who were at least 18 years of age, identified English as their preferred language, were not involuntarily confined or detained, and agreed to be provided a tablet to access the inpatient portal during their stay. Data were analyzed from September 1, 2022, to October 31, 2023.

          Interventions

          A 2 × 2 factorial design was used to compare the inpatient portal training intervention (touch, in-person [high] vs built-in video tutorial [low]) and the portal functionality intervention (technology, full functionality [full] vs a limited subset of functions [lite]).

          Main Outcomes and Measures

          Primary outcomes were inpatient portal use, measured by frequency and comprehensiveness of use, and use of specific portal functions. A logistic regression model was used to test the association of the estimators with the comprehensiveness use measure. Outcomes are reported as incidence rate ratios (IRRs) for the frequency outcomes or odds ratios (ORs) for the comprehensiveness outcomes with corresponding 95% CIs.

          Results

          Of 2892 participants, 550 (19.0%) were Black individuals, 2221 (76.8%) were White individuals, and 121 (4.2%) were categorized as other race (including African, American Indian or Alaska Native, Asian or Asian American, multiple races or ethnicities, and unknown race or ethnicity). Black participants had a significantly lower frequency (IRR, 0.80 [95% CI, 0.72-0.89]) of inpatient portal use compared with White participants. Interaction effects were not observed between technology, touch, and race. Among participants who received the full technology intervention, Black participants had lower odds of being comprehensive users (OR, 0.76 [95% CI, 0.62-0.91), but interaction effects were not observed between touch and race.

          Conclusions and Relevance

          In this study, providing in-person training or robust portal functionality did not narrow the divide between Black participants and White participants with respect to their inpatient portal use. Health systems looking to narrow the digital divide may need to consider intentional interventions that address underlying issues contributing to this inequity.

          Trial Registration

          ClinicalTrials.gov Identifier: NCT02943109

          Related collections

          Most cited references51

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          State of Telehealth.

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            Digital inclusion as a social determinant of health

            The use of digital tools and applications is steadily increasing and can support a range of health information needs 1–3 . As tools such as patient portals, health trackers, and remote monitoring devices see greater use, research suggests that tools such as health apps and patient portals can foster greater patient engagement, better support for patients outside of the clinic visit, and can improve health outcomes 3–9 . However, greater reliance on digital tools has the potential to increase disparities between those who have skills and access to digital tools and those who do not and thereby existing health disparities. According to a recent Brookings Institution report, 15–24% of Americans lack any sort of broadband connection to the Internet with which to use mobile health technology. These differences only increase when examining the issue by income groups: 38% of households earning less than $20,000 lack a broadband subscription 10 . The digital divide by income exists in both rural and urban areas. As practitioners working at the intersection of digital inclusion and health, we would like to highlight some less visible dimensions of the digital divide and offer suggestions to facilitate digital inclusion and ensure equitable and impactful adoption of mobile health technologies. Digital literacies and Internet connectivity have been called the “super social determinants of health” because they address all other social determinants of health (SDOH), as shown in Fig. 1 11 . For example, applications for employment, housing, and other assistance programs, each of which influences an individual’s health, are increasingly, and sometimes exclusively, accessible online. The costs of equipping a person to use the Internet are substantially lower than treating health conditions and the benefits are persistent and significant 12 , making the efforts to improve digital literacy skills and access valuable tools to reduce disparities. Fig. 1 Digital literacies and social determinants of health. Digital literacy and access, including skills, connectivity, devices and training and technical support, relate to all other domains of social determinants of health. With these challenges in mind, we offer the following recommendations. First, healthcare systems should adopt a digital inclusion-informed strategy regarding mobile health that (1) recognizes their community’s level of access to devices and Internet connectivity and (2) supports patients in their initial and sustained technology use. Digital inclusion refers to the activities necessary to ensure equitable access to and use of information and communication technologies, including (1) affordable broadband Internet service, (2) Internet-enabled devices, (3) access to digital literacy training, (4) quality technical support, and (5) applications and online content designed to enable and encourage self-sufficiency, participation, and collaboration 13 . These form the foundation for use of mobile technology in healthcare. While knowing whether an individual’s access is important, it is vital for health systems to understand the larger environment shaping patients’ digital experience. Adoption rates are nearing ubiquity among highly educated individuals with at least moderate income, but important pockets of nonadoption remain. Most mobile health technology requires a data plan and/or home broadband, yet the American Community Survey shows that 40% of low-income households lack a subscription, requiring them to use limited cell plan data or local public wifi hotspots 12 . These options may appear affordable but they contain important limitations. Using prepaid plans, patients may run out of data or need to prioritize data for specific uses. Even with their lower cost, they may still be unaffordable, particularly for families in need of multiple devices. Open wifi access points are another option but may only be available in public locations in which patients may feel uncomfortable accessing their personal health information. Prior to the rapid increase in telehealth use due to COVID-19, patient portals to their electronic health record (EHR) were the most common form of mobile health and a gateway to other mobile health applications. However, studies show that lack of Internet access is a leading factor inhibiting use of patient portals 14 . Smartphones may seem to be a logical and ubiquitous substitute for home Internet, but significant gaps still exist for rural, poor, and older adults. Research shows that nearly one-half of older adults and 30% of those earning less than $30,000 own a smartphone and many low-income households share devices, raising both access and privacy issues 15 . Understanding the nuances of access in the communities they serve can help healthcare systems implement more inclusive strategies. Digitally inclusive strategies of health system adoption also support patients in their use of technology at all levels and should include digital skill training, particularly for recent adopters of technology or those who may have devices with limited features. Patients may also need assistance with setting up email and patient portal accounts. In addition, it is critical to provide ongoing support for patients, reduce medical jargon, and provide interpretive resources, and ensure that technology and training are offered equitably to all patients, not just to those who are confident enough to request help 16 . Second, we recommend systematically assessing individual patients’ access and digital literacies. This became particularly clear since the rapid and pervasive shift to telehealth during the COVID-19 pandemic. Simply asking patients what devices they own and how they access the Internet is not typical in the clinical context, but this information can shape the type of technology a clinician can recommend. The lack of routine assessment prior to COVID-19 meant that some patients fell between the cracks as care shifted to nearly all virtual 17 . Incorporating this and other SDOH into the EHR encourages more consistent documentation and allows assessment of population-level metrics of access 18 . When digital skill and connectivity gaps are assessed systematically and universally, a health system can document overall population-level metrics, examine disparities, and track changes over time. Third, health systems should partner with community organizations with expertise in training in digital literacy skills and facilitating connectivity. Libraries not only offer the Internet but also provide a spectrum of training services from basic digital literacies to skills required for specific devices and applications. Some communities have leveraged community health workers and patient navigators to screen and refer patients for gaps in basic digital literacies and connectivity 19,20 . They can provide hands-on training in the use of mobile health technologies for patients who do have adequate digital access. Allied health professional education programs leverage a “train the trainer” model to prepare the future healthcare workforce to undertake these tasks 21–23 . The National Digital Inclusion Alliance (NDIA) offers a comprehensive list of organizations across the country that provide digital literacy training and national and local resources for free/low-cost Internet and computers 13 . Mobile health technologies hold significant promise to increase the efficiency of care and improve health outcomes. Yet, we must be cognizant of their potential to increase health disparities. National efforts have been undertaken to promote broadband, such as the Federal Communications Commission’s (FCC) Lifeline program that subsidizes the cost of smartphones and Internet service for low-income individuals 24,25 . However, the Lifeline Program’s impact is limited by low consumer awareness, and the qualification process varies by state and by the service provider. In addition, Internet service may still be unaffordable even with the monthly subsidy. Another program, the Federal Broadband Opportunities Program, supported over 4 million people to get online for the first time with a $4 billion program but those one-time dollars are long gone, leaving a gap in the need for adult digital literacy support. BTOP has only two remaining operational programs with no new funding on the horizon 25,26 . In response to the current COVID-19 pandemic, the FCC also introduced a variety of programs to increase Internet access for the use of telehealth, including paying for devices and access. However, the future of these programs after the COVID-19 pandemic is unclear 13 . As clinical care incorporates more technology in more contexts, we suggest the recommendations above to facilitate equitable adoption of mobile health technology.
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              Back to the Future: Achieving Health Equity Through Health Informatics and Digital Health

              The rapid proliferation of health informatics and digital health innovations has revolutionized clinical and research practices. There is no doubt that these fields will continue to have accelerated growth and a substantial impact on population health. However, there are legitimate concerns about how these promising technological advances can lead to unintended consequences such as perpetuating health and health care disparities for underresourced populations. To mitigate this potential pitfall, it is imperative for the health informatics and digital health scientific communities to understand the challenges faced by disadvantaged groups, including racial and ethnic minorities, which hinder their achievement of ideal health. This paper presents illustrative exemplars as case studies of contextually tailored, sociotechnical mobile health interventions designed with community members to address health inequities using community-engaged research approaches. We strongly encourage researchers and innovators to integrate community engagement into the development of data-driven, modernized solutions for every sector of society to truly achieve health equity for all.
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                Author and article information

                Journal
                JAMA Netw Open
                JAMA Netw Open
                JAMA Network Open
                American Medical Association
                2574-3805
                4 April 2024
                April 2024
                4 April 2024
                : 7
                : 4
                : e245091
                Affiliations
                [1 ]Department of Family and Community Medicine, College of Medicine, The Ohio State University, Columbus
                [2 ]CATALYST, The Center for the Advancement of Team Science, Analytics, and Systems Thinking in Health Services and Implementation Science Research, College of Medicine, The Ohio State University, Columbus
                [3 ]Division of Health Services Management and Policy, College of Public Health, The Ohio State University, Columbus
                [4 ]Division of General Internal Medicine, College of Medicine, The Ohio State University, Columbus
                [5 ]Department of Biomedical Informatics, College of Medicine, The Ohio State University, Columbus
                Author notes
                Article Information
                Accepted for Publication: February 6, 2024.
                Published: April 4, 2024. doi:10.1001/jamanetworkopen.2024.5091
                Open Access: This is an open access article distributed under the terms of the CC-BY License. © 2024 Walker DM et al. JAMA Network Open.
                Corresponding Author: Daniel M. Walker, PhD, MPH, Department of Family and Community Medicine, College of Medicine, The Ohio State University, 700 Ackerman Rd, Suite 4100, Columbus, OH 43202 ( Daniel.Walker@ 123456osumc.edu ).
                Author Contributions: Drs Walker and Di Tosto had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.
                Concept and design: Walker, Hefner, MacEwan, Gaughan, Huerta, McAlearney.
                Acquisition, analysis, or interpretation of data: All authors.
                Drafting of the manuscript: Walker, Hefner, MacEwan, Huerta, McAlearney.
                Critical review of the manuscript for important intellectual content: All authors.
                Statistical analysis: Walker, Hefner, Di Tosto, Sova, Huerta.
                Obtained funding: McAlearney.
                Administrative, technical, or material support: Hefner, Sova, Gaughan, Huerta, McAlearney.
                Supervision: Walker, Sova, Gaughan, Huerta, McAlearney.
                Conflict of Interest Disclosures: Dr Sova reported receiving grants from the Ohio Department of Medicaid, the Patient-Centered Outcomes Research Institute, and the Agency for Healthcare Research and Quality outside the submitted work. Dr McAlearney reported receiving personal fees from Health Administration Press Royalties outside the submitted work. No other disclosures were reported.
                Funding/ Support: This study was supported by grants R01HS024091, R21HS024767, and P30HS024379 from the Agency for Healthcare Research and Quality.
                Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.
                Data Sharing Statement: See Supplement 3.
                Additional Contributions: We thank our research team members for their involvement in this study, and all of the study participants for their participation.
                Article
                zoi240210
                10.1001/jamanetworkopen.2024.5091
                11192182
                38573634
                9b19b088-bf99-43d9-bd6e-e68c976e7621
                Copyright 2024 Walker DM et al. JAMA Network Open.

                This is an open access article distributed under the terms of the CC-BY License.

                History
                : 6 April 2023
                : 6 February 2024
                Categories
                Research
                Original Investigation
                Online Only
                Health Informatics

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