A FRAMEWORK FOR DIGITAL HEALTH EQUITY

ABSTRACT

BACKGROUND: The COVID-19 pandemic highlighted both the continued impact of long-standing systemic oppression on disparate health outcomes as well as the growing importance of healthcare provided through digital means. For example, an explosion in the use of telehealth for remote care noted significant disparities in use by minority groups. There is a growing recognition of the crucial importance of determinants in the digital environment and their impact on health outcomes. These digital determinants of health (DDoH) function independently as barriers to and facilitators of health as well as interact with social determinants of health (SDoH) to impact outcomes. A framework for digital health equity, detailing key DDoHs, is needed to support the work of developers in industry, health systems operations and academia. METHODS: The framework for digital health equity is an adaptation of the NIMHD Research Framework, which is the culmination of decades of work in the field of health disparities. The NIMHD framework is organized into several domains, including biological, behavioral, physical/built environment, sociocultural environment, and the health care system. Because of its particular importance at this time -we incorporate a digital environment domain with key DDoHs. RESULTS: Determinants at the individual level include digital literacy, readiness, interest, and self-efficacy. Readiness describes necessary technological equipment availability. Interest is used here to describe an individual's desire and willingness to use and trust in digital tools. Determinants at the interpersonal level include bias, interdependence, and relationship disruption. We use the term bias to describe the impact perceptions about an individual's digital literacy, readiness and interest have on clinician willingness to enroll and engage individuals with digital healthcare tools. Relationship disruption describes the complex cultural transformation encouraged by digital technologies. For disparity populations this has the potential to impact well documented relational determinants including medical mistrust and poor-quality communication. Determinants at the community level include cellular wireless and broadband access, quality and affordability as well as health system infrastructure. Determinants at the societal level include the impact of policy, data and design standards, algorithmic bias as well as social norms and ideologies in technical industry. Key examples of facilitators of positive health outcomes are provided at all levels. CONCLUSIONS: By adapting the leading health disparities research framework for digital health equity, we hope developers will benefit from decades of progress in the field of health disparities as well as see their work in the larger context of SDoHs so that we might work together towards meaningful progress in using digital means to achieve health equity for all.