When the COVID-19 pandemic forced a rapid shift toward telemedicine and digital health services, policymakers and healthcare providers framed it as a necessary evolution in care delivery. Governments around the world invested heavily in telehealth initiatives, with organizations like the Australian Digital Health Agency leading national implementation efforts.
However, new research published in PLOS Digital Health suggests that these advancements have also introduced new layers of exclusion, creating what researchers call the Digital Determinants of Health (DDOH)—a new category of health inequities that intersect with existing social determinants of health (SDOH).
A team led by Swathikan Chidambaram at the University of Melbourne argues that digital access, literacy, and bias in health technology design must be understood as structural barriers to health equity. They write:
“There is a paucity of literature addressing how the intrinsic design, implementation, and use of technology interact with SDOH to influence health outcomes. Such interactions are termed digital determinants of health (DDOH)… DDOH is implicit in the design of artificial intelligence systems, mobile phone applications, telemedicine, digital health literacy [DHL], and other forms of digital technology.”
While digital health has often been framed as a way to close health disparities, the reality is that it reinforces existing social hierarchies. Millions of people worldwide lack digital literacy, internet access, or the financial resources to navigate online health platforms. According to a report from the UK, 11 million people do not have the skills to participate in the digital economy, meaning they also lack access to basic telehealth services. These gaps, the researchers argue, mirror broader political and economic inequalities:
“Regardless of the exact terminology, all previous work agrees on the contextualization of DDOH with respect to the broader political, societal, and economic processes that they are embedded in. Namely, differences in societal preferences, socioeconomic contexts, and political and institutional configurations will generate variations in how digital technologies are incorporated and consumed in the healthcare ecosystem.”
By analyzing digital determinants of health, researchers hope to illuminate how new healthcare technologies—rather than democratizing access—are further marginalizing those already affected by economic and social oppression.
You've landed on a MIA journalism article that is funded by MIA supporters. To read the full article, sign up as a MIA Supporter. All active donors get full access to all MIA content, and free passes to all Mad in America events.
Current MIA supporters can log in below.(If you can't afford to support MIA in this way, email us at [email protected] and we will provide you with access to all donor-supported content.)
Key Areas of Digital Health Disparities
1. Digital Health Literacy: The New Divide
Chidambaram and his team highlight digital health literacy (DHL) as a major factor in these disparities. Health literacy—the ability to access, understand, and use health information—has long been tied to better outcomes. But with the rise of digital health services, those without digital fluency are increasingly excluded from care.
“An individual’s health literacy is defined as the ability to find, understand, appraise, and use information and services to make health-related decisions correlates with health outcomes… With the increasing use of digital technologies in healthcare, digital health literacy (DHL) has emerged as a high priority for healthcare organizations and governments to effectively engage consumers in their health and wellness.”
Low digital literacy disproportionately affects older adults, those with lower levels of formal education, and people in low-income communities—precisely the groups most likely to experience poor health outcomes.
2. Telemedicine: A Solution That Creates New Barriers
Telemedicine has been widely promoted as a solution for improving access to care, particularly for people with disabilities. But the researchers highlight how telehealth services often reinforce existing inequalities. High-speed internet is a prerequisite for telemedicine, yet between 21 and 42 million Americans lack reliable broadband access, and disability is a strong predictor of this lack of access.
“[A]pproximately between 21 and 42 million Americans lack high-speed internet access, and of them, physical or mental disability is a strong predictive factor for not having access to broadband internet.”
Beyond internet access, many telehealth platforms are not designed with accessibility in mind. Graphic user interfaces (GUIs) are built primarily for sighted users, leaving people with visual impairments at a disadvantage. Those with cognitive impairments may struggle to interpret complex digital forms and fine-print materials. Furthermore, telemedicine inherently limits physical exams, lab work, and other essential diagnostic tools, restricting the scope of care for those who rely on it.
3. Artificial Intelligence: Codifying Bias in Health Care
Artificial intelligence (AI) is increasingly used in medical diagnostics and treatment recommendations, but AI models inherit biases from the data they are trained on. Chidambaram’s team warns that AI-driven healthcare may exacerbate systemic discrimination rather than eliminate it:
“If… research questions concerning disadvantaged groups are not prioritized, the structural biases will translate to less AI-based solutions as well… Firstly, the dataset [used by AI models] itself may be underrepresented or developed based on representative data but applied to the unintended minority population. Secondly, data used may have sociohistorical bias in terms of how it was entered and collected. Thirdly, the data used may not account for social categories and determinants of the intended outcome.”
For example, a study on acute kidney injury prediction models found that they severely underperformed for women because only 6.4% of the initial dataset included female patients. Similarly, diagnostic tools for skin conditions often fail to account for variations in skin tones, leading to misdiagnoses in patients with darker skin.
“In a study aimed at predicting acute kidney injury, the model severely underperformed in female patients as only 6.4% of its initial dataset were from female patients… Similar instances of underrepresentation have been seen in diagnosing skin lesions, as most algorithms do not include skin lesions in ethnic minorities.”
These biases make AI-driven healthcare particularly risky for marginalized groups, whose symptoms may not fit the “standard” patient profile embedded in these algorithms.
Moving Forward: A Call for Digital Health Justice
The researchers propose four key areas for reform:
- Expanding Digital Literacy Programs – Governments and health organizations must invest in digital literacy training to ensure that health technologies do not exclude vulnerable populations.
- Inclusive Technology Design – Developers must conduct market research that includes marginalized communities to ensure that digital tools do not reinforce existing inequalities.
- Regulating Corporate Influence – The privatization of health data raises concerns about exploitation and exclusion. Governments must ensure that digital health tools prioritize public well-being over corporate profits.
- Equity-Centered Health Policy – Digital health policies must center those most affected by health disparities, including people with disabilities, racial minorities, and low-income communities.
“With the increasing use of digital health in healthcare, the potential for health inequities it poses must be addressed. In tandem with ongoing work to minimize the digital divide caused by existing SDOH, further work is necessary to recognize digital determinants as an important and distinct entity.”
As digital technology becomes increasingly embedded in healthcare, these findings raise urgent questions: Will digital health be a tool for equity, or will it widen the gaps in care? The answer depends on whether policymakers, healthcare providers, and tech developers prioritize access, justice, and the realities of the communities they claim to serve.
****
Chidambaram, S., Jain, B., Jain, U., Mwavu, R., Baru, R., Thomas, B., … & Darzi, A. (2024). An introduction to digital determinants of health. PLOS Digital Health, 3(1), e0000346. (Link)
