Equity is a pressing concern in digital health, which aims to improve the efficiency and effectiveness of medical care. However, historical racism has hindered equity in healthcare, creating disparities that affect patients before they even receive medical care and persist throughout their treatment. For example, racial disparities are evident in higher mortality rates for Black women during pregnancy compared to White women. To address these disparities and dismantle structural racism, the R4P (Remove, Repair, Restructure, Remediate) approach, supported by the 3R (Retrofit, Reform, Reimagine) model, is proposed.
Applying the R4P/3R framework to digital health innovations, three key areas are identified: AI algorithms, wearable devices, and telehealth.
AI algorithms used in healthcare may unintentionally embed bias due to existing inequities and unrepresentative datasets. Immediate remediation involves retrofitting technologies with racial equity goals, increasing transparency, evaluating algorithms for equity criteria, and reworking flawed associations.
Consumer wearable devices may perpetuate racial disparities, as some measurements are less accurate for darker skin tones. Therefore, efforts should focus on increasing access and education in marginalized communities, evaluating equity within FDA approval processes, ensuring transparency about accuracy limitations, and conducting research to develop devices that function across racial phenotypes.
Telehealth, despite its promise to increase healthcare access, still faces racial disparities. Factors such as reduced broadband access, health literacy disparities, and patient preferences reduce telemedicine uptake among racial minorities. Remediation involves interventions such as remote interpreters, technology education, evaluating financial incentives and quality metrics, and establishing broadband access programs.
To realize an equitable future for digital medicine, the R4P/3R framework must be applied. Addressing bias in AI algorithms, promoting access to accurate wearable devices, and remediating disparities in telehealth are crucial steps. Transparency, evaluation of algorithms, and infrastructure for racial equity criteria are necessary for AI algorithms. Increasing access, evaluating equity in FDA processes, and developing devices for different racial phenotypes are needed for wearable devices. Interventions such as remote interpreters, technology education, and broadband access programs can help remediate telehealth disparities.
By centering equity in the development and implementation of digital health innovations, racial disparities can be reduced. The R4P/3R approach provides a comprehensive framework for creating sustainable change and ensuring equitable access to safe and effective healthcare. Addressing biases and disparities in AI algorithms, wearable devices, and telehealth is essential to achieve an equitable future in digital medicine.
Source:
Raza, M. M., Venkatesh, K. P., & Kvedar, J. C. (2023). Promoting racial equity in digital health: Applying a cross-disciplinary equity framework. Npj Digital Medicine, 6(1), 1-3. https://doi.org/10.1038/s41746-023-00747-5
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