The Risks and Rewards of Digital Health Technology in Racial Health Equity

On January 5, 2022, the Office of Science and Technology Policy published a document in the Federal Register “requesting input on how digital health technologies are used, or could be used in the future, to transform community health, individual wellness, and health equity.” Subsequently, the RFI was extended for feedback through March 31, 2022. Below is the written response from Health Leads, informed by 20+ years of experience working locally and nationally as conveners of relevant stakeholders and drivers of shared learning, including those related to digital technologies. Throughout the years, we have had the opportunity to glean insights about what it takes to transform community health, address the social determinants of health, and create conditions to achieve racial health equity. 

That the response below almost never existed in the world is itself telling. In the long list of priorities that are necessary for Health Leads to make real on our commitments to racial health equity, writing a response was not high in urgency. Certainly, we saw its value but with limited time and capacity to do so much that is needed, keeping requests for feedback from government bureaucracies on our radar does not always rise to the top of the list (for better or for worse). In our work across the years with community health workers and people who are most impacted by community health solutions that include digital technologies, we know that we are not alone in this realistic assessment of prioritization–and in seeing that sometimes the channels and methods leveraged to call for community voice are often not the ones that make the most sense for communities themselves. So even in submitting the response below, we continue to reflect on how structures and processes can be better designed to truly hear what communities most want – on their terms, agendas, and timelines. We share these publicly now to invite others into the conversation.

Despite being one of the wealthiest nations in the world, the United States has consistently demonstrated poor health outcomes for decades. By 2007, studies showed that men in the U.S. had shorter life expectancies than in Switzerland and women in the U.S. had shorter life expectancies than peers in Japan. Poorer health relative to peers is observed across the board including from white, insured, college-educated, or in upper-income groups in the U.S. appearing to be in worse health than similar groups in comparison countries. Additionally, differences in health outcomes have long been linked to inequitable structures that continue to harm historically marginalized populations. These inequities were further exacerbated during COVID-19. For example, in 2020, we saw racial/ethnic disparities in excess deaths which led to increases in racial/ethnic disparities in all-cause mortality from 2019-2020.

Private and public sector advocates have proposed the utilization of digital technologies to help bridge divides in access to services and resources. This is indeed promising but must also be met with caution; as authors like Virginia Eubanks and Ruha Benjamin have argued, technology can add speed and scale to existing inequities baked into health and human service systems. Below are some cautions on things to avoid when individuals and institutions leverage digital technologies for improving community health as well as some ways to build consensus and align on things to promote. These recommendations are based on our work as an innovation hub and learning accelerator with diverse stakeholders across many sectors and communities as well as equity-oriented partners like those in the Vaccine Equity Cooperative and the Community-based Workforce Alliance.  

What to avoid:

1. Temptation of Templates: Seeking a template-based approach to finding the “right” data standards and technology infrastructure, rather than committing to a regular practice of responsiveness to the specific needs and desires of individuals, families, and communities that are impacted by local implementation. For example, a data sharing infrastructure that works for a specific subpopulation in Boston may not be the one that works for the consent and privacy concerns of another group in Houston. Both can be equitable and responsive to community-led design, but what is not is dropping in a template with limited options for customization.
2. Meaningless Metrics: Assessing impact of digital technologies based on measures that only matter to people and institutions in power, without gleaning insights from direct users, such as community health workers. Avoid the utilization of said metrics to label groups as low capacity or low adoption groups which can lead to diagnosing the problem as one that can be solely solved through additional computer literacy or workflow training. While training and capacity building are indeed desirable and necessary when requested by community members and service providers on the front lines, they are often limited responses to the wrong root causes when mandated by outsiders far from the reality of equitable community health efforts.
   
3. Ignoring Capacity Constraints: Adding additional burden on already stretched thin organizations and workforces by mandating they adopt a new workflow or technology that does not provide clear, added value.
4. Siloed Strategy: Failing to look at the design and implementation of digital technologies without a holistic understanding of the landscape and ecosystem. For example, caregiver sustainability (e.g., inclusion of workforces like community health workers and friends and family members who provide care regularly), data democratization, and participatory decision-making are all vital components that work as a constellation and can deeply impact the development, improvement, and evolution of digital technologies.
   

What to work toward:

Policy solutions that include a) universal, affordable, consistent health insurance coverage across the life-course; b) policies that equitably reduce unhealthy behaviors; c) reduction in poverty, particularly child poverty are widely endorsed by public health and health care professional organizations – and can have powerful impact on improving community health. To leverage digital technologies, some things to consider include:

1. Adaptive and Responsive Systems Design: While a template or checklist-based approach to equitable digital technologies should be approached, policies and procedures that can be standardized include participatory decision-making processes for which the details are defined and customized to the unique groups of stakeholders. This means that contracts, funding streams, and other forms of institutional support should support organizations that commit to designing and evolving systems that are flexible and adaptive in their data and technology infrastructures and execute on that flexibility through customized integration of partner design and input based on specific community context. FHIR v. 4.0.1 standards include mobile phone apps and demonstrate how standards can be more inclusive of community based organizations and community health workers who work outside of clinic walls. These individuals and organizations are a vital part of data exchanges and would be able to access shared systems through FHIR API even if they do not have FHIR servers.
2. Community-driven Measurement: Developing measures that matter and committing to updating said measures as collaborative networks change over time. For example, 211 San Diego recognized that the Community Information Exchange (CIE) they were stewarding in their county needed to measure how they were responsive to the challenges expressed by members of their governance body of community stakeholders. One initial process measure was clients could not be found in the shared system because consent practices and procedures needed to be addressed. In response, a key indicator in that phase of work was to see if they were making progress on addressing this concern by  measuring changes over time to the % of time a client record could be found if looked up by a system user. Once enough progress was made to determine impact in this area, governance body workgroups identified new key indicators that mattered most to members of the CIE. This is a small set of data used to inform the evolution of operations to iteratively learn and improve over time. This model allows for an agile evaluation process based on the needs of a governing body, rather than traditional evaluation plans that are held steady over time. The decisions about the most meaningful measures are made in deep partnership with communities most impacted and certainly not driven by individuals and institutions who have financial or other interests in framing particular narratives around what success and failure look like. Follow through also looks like said communities that are most impacted being part of a governance body that not only designs measures but interprets results so that the narrative is never created outside of context and without meaningful qualitative feedback.
3. Strategic Aims: Data and technology infrastructures as utilized in sustainable ways by caregivers and stewarded through participatory decision making bodies and processes are what will drive impact when leveraging digital technologies. It is how these different facets of strategy work in concert with each other that matters, rather than taking each of these approaches as separate endeavors.

While a critical piece of the puzzle, technology in and of itself will not create equitable networks and systems of community health, and in fact can cause harm if not carefully implemented with those who are closest to community health assets, goals and challenges. With these considerations, communities will be set up to drive the changes they need and want to see in their lives.