Promoting racial equity in digital health: applying a cross-disciplinary equity framework

Abstract

Even as innovation occurs within digital medicine, challenges around equity and racial health disparities remain. Golden et al. evaluate structural racism in their recent paper focused on reproductive health. They recommend a framework to Remove, Repair, Restructure, and Remediate. We propose applying the framework to three areas within digital medicine: artificial intelligence (AI) applications, wearable devices, and telehealth. With this approach, we can continue to work towards an equitable future for digital medicine.

Evaluating Racial Disparities in Implementation and Monitoring of a Remote Blood Pressure Program in a Pregnant Population—A Retrospective Cohort Study

“…All patients who enroll in CMOM are offered technical assistance, but technical assistance does not address digital health literacy which is often a barrier to engagement. Research has documented decreased engagement with telehealth and wearable devices among Black patients compared to White patients, 37 , 38 but engagement can be improved with increased training on digital devices. 39 When patients decline participation or do not complete the onboarding phase, follow-up questions are needed to determine if training on the devices or addressing other digital barriers can improve enrollment.…”

Ethical considerations in using sensors to remotely assess pediatric health behaviors.

“…To overcome these and many other subjective decision points, open-source algorithms applied to raw acceleration data are increasingly becoming available to help standardize the accelerometer-derived sleep and physical activity estimates (Belcher et al, 2021; John et al, 2019; Van Hees et al, 2015). However, subjective decision making is still needed to select among available algorithms, especially considering that many algorithms have been trained by samples that do not sufficiently represent racial and ethnic minoritized groups (Raza et al, 2023).…”

“…The above validity gaps also raise questions about whether sensor-based methods, such as data collection and scoring procedures, can be effectively and equitably applied to generate accurate estimates across samples. It is likely that there are inherent biases and assumptions about users that impact data-related decision making and interpretation (Raza et al, 2023). For instance, to our knowledge, only one study of infants has evaluated impact of shared versus nonshared sleep surfaces (e.g., bed- or couch-sharing) on the validity of accelerometer-based sleep estimates (Camerota et al, 2018), yet this sleep arrangement is common among families with young children, including toddlers and preschoolers, in many parts of the world (e.g., African and Asian countries/regions (Mileva-Seitz et al, 2017; Mindell et al, 2010), including among Black and Latine families with young children in the United States; Barajas et al, 2011).…”

ChatGPT

“…Denn schwarze Patienten wurden systematisch als gesünder eingeschätzt als gleich kranke weiße Patienten [14]. Im Bereich der Medizin nimmt man diese Probleme der KI sehr ernst [15,16]. Schließlich geht es um Leben und Tod.…”