Achieving Health Equity: Where Technology Falls Short
Technology in healthcare has been one of our most powerful tools in understanding and improving health outcomes. Technology enables advances in medical treatment, synthesizes large amounts of data, and identifies patterns and risk factors. It facilitates access to care through virtual visits, enables remote monitoring through personal devices, and facilitates availability and dissemination of practical and scientific information. There is no question that technology is critical to our healthcare delivery system and the potential for what it can become. But, while its potential seems limitless, its limitations today are real. Health disparities abound. Despite our advances, life expectancy is driven more by our zip code than our genetic code. As healthcare professionals, we must harness technology to amplify our capabilities, while recognizing that today’s health tech solutions alone are not enough to level that playing field. Achieving health equity requires us to appreciate and respond to the context and drivers of that inequity, the need for cross-sectoral solutions, and to acknowledge that individuals have agency to make choices and decisions that reflect their perspectives and values. Technology can support that mission, but cannot supplant that obligation.
Public Health and Health Equity
One of the first lessons I learned as a public health professional was that a community or population must tell you what it needs (based on its characteristics, prevalent health issues, barriers to care, etc.), not the other way around. Data helps us define those needs, and technology has facilitated access to that data through the use of electronic medical record (EMR) systems, coding, surveys, data sharing within healthcare and across industries, algorithms, artificial intelligence (AI), and more. From that, we can build a profile of the population: the prevalence of morbidity and mortality, patterns of healthcare utilization, socioeconomic status of the geography, etc. Technology and machine learning have enabled algorithms to be built that help us identify which patients are at risk of poor health outcomes. But, it can’t do all of our work for us. At least not yet. It can’t provide us the full picture of why those patients are as sick as they are. It can’t tell us what is driving their issues. It can’t tell us about their motivations, what drives their health decisions, competing priorities they may have or what barriers to engaging exist. Those are pieces of the puzzle that must be understood before we can claim to understand the health disparities we see. And, unless we have equitable data collection across populations, and incorporate psychosocial, environmental and sociocultural determinants of health and healthcare utilization, the algorithms we build will only be as robust and applicable as the data and people they represent.
Public health issues and issues of health equity reflect issues of our society. They are influenced by biological, socioeconomic, political, historical and environmental factors. Effective responses require recognition and understanding of their diverse drivers, which often cross sectors and industries. It is, therefore, important that the healthcare industry use and promote a portfolio of solutions that support our response to the myriad of factors at play, the reasons why and how they carry so much influence, and reflect the diverse experiences of those affected. Technology and health tech solutions can play an important role, but we must use a critical eye in interpreting their utility and application, and recognize what they can do and what they can’t. We must take the time to understand what the solutions require to succeed, and whether those requirements are ubiquitous or available only to certain groups. We must think about whether their implementation and use will align with the norms of those they attempt to support or whether their adoption will require a paradigm shift.
“Inclusive design is a challenge. AI training doesn’t consistently involve diverse groups. That will contribute to barriers for underrepresented communities.” — Julie Chatman, CISO, highly regulated industries: Healthcare, Pharma, Finance & Government
We have seen that, even when interventions are evidence-based or facilitated through technology, people and communities may not adopt or accept the solutions we believe will alleviate and mitigate morbidity, while others don’t have access to those solutions, can’t afford them, or have other priorities that they believe must come first. Our solutions are only as successful as our ability to provide them to everyone, and people’s willingness to adopt or embrace them.
Why aren’t solutions that seem objectively beneficial adopted universally?
I would argue that, perhaps, it is because change at any level requires people to change. People who might be resistant to change. People who have the agency to make choices. People who will be impacted positively by changes to behaviors, practices, regulations and policies, and people who will be impacted negatively. People who are seeking different outcomes for different reasons and with different priorities. People with limitless options and people with few. People whose lived experience and perspective has shaped their trust in institutions, medicine, doctors and the healthcare system. We are in the business of helping people, and no matter what technology or medicine or science we have, we need their buy-in.
When clinical intervention is straight-forward and curative, the solution may look the same for everyone, such as medical treatment that follows established guidelines in acute settings. But the real world is often more complicated. External influences are powerful. Even when we know the exact treatment or intervention needed (say, antibiotics for an STI or treatment for TB), there are influences outside of the clinic and hospital walls at play that we likely don’t know about or understand for the individual receiving treatment. People are not always rational actors and changing people’s behavior is a feat we have yet to master.
Vulnerable and underserved populations are even more heavily impacted by these drivers, yet we lack industry knowledge of their lived experience and the explanations for their health outcomes. These communities often know what they need, but the healthcare system today isn’t set up to receive that message. Technology can’t replace the ability of a community health worker to build trust or read the room and adjust a message to resonate more effectively. Hopefully someday, but not yet.
“Whether EHR, AI/ML solutions, or devices, their application in healthcare require(s) thoughtful design, implementation and adoption to avoid creating or extending existing disparities.” — Bilikis Oladimeji, Physician, Health Informatics and Innovation Leader
So, how can we do better?
- We must use technology as one strategy in our toolbox, but recognize that it, alone, cannot address all drivers of a health issue or provide the same impact across heterogeneous populations.
- Commitment must be made to include diverse experiences and voices into the design of new health technology.
- Interoperability and data sharing must be optimized to facilitate and support informed healthcare delivery for all consumers, including those who have historically experienced fragmented care.
- Application of technology must incorporate recognition of people’s agency and decision-making, and designers of new technology should use that knowledge to enhance their solutions.
- Engage and collaborate with stakeholders across sectors to address upstream drivers of health inequity and promote development and use of technology that supports those efforts.
We must recognize that achieving health equity will require continuous commitment and reflection, collaboration across fields, and comprehensive understanding of the context and drivers that perpetuate existing and potential disparities. This work may not yield the most lucrative or streamlined way of doing business, but it is the right thing to do.
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