Digital health inclusion aims to ensure that everyone, regardless of their background, location, or circumstances, has access to and can effectively use and benefit from digital health technologies.
Innovation-generated inequity in health refers to the disparities that arise when technological advancements in health care are not equitably distributed. While innovations like telemedicine, artificial intelligence (AI)-driven diagnostics, and mobile health applications have the potential to improve health care outcomes, they often require resources such as reliable internet access, digital literacy, and financial means that are not universally available, particularly in low resource settings (1).
Access to a tool or resource itself is not sufficient to ensure inclusion. The underlying data must also be able to benefit all stakeholders: patients, providers, researchers, and policymakers. When we talk about digital health inclusion being data inclusive, it means that the data used to drive digital health inclusion efforts is comprehensive, equitable, and representative of all communities.
For example, if a community is facing high maternal and newborn mortality rates, health authorities may first look to the data on interventions and health care access to prevent this. However, if the data is not representative of the community served—including marginalized groups such as rural women, indigenous peoples, or migrants and refugees—health authorities cannot implement targeted programs to ultimately reduce disparities and lower maternal and infant mortality rates.
Further, decisions made in the absence of a representative dataset may further exacerbate health inequities. Data inclusivity can play a crucial role in improving health care access and outcomes. Here are some key aspects of what it means for digital health inclusion to be data inclusive:
Comprehensive data collection
For data to be inclusive, it must be collected from a wide range of sources and be representative across demographics. This includes data disaggregated by factors such as age, gender, ethnicity, disability, income, citizenship status, and geographic location (2). By capturing diverse data, we can better understand the unique challenges faced by different groups and tailor digital inclusion strategies accordingly (3). Further, data inclusivity is critical to understand how socio-demographic factors influence health and health outcomes.
Equitable data access
Ensuring that data is accessible to all relevant stakeholders is crucial (while maintaining patient privacy and data sovereignty). This means providing open access to datasets that can be used by researchers, policymakers, governments, and community organizations to develop and implement effective digital inclusion programs (3). Equitable data access helps democratize information and empowers communities to advocate for their needs.
Addressing data bias
For data to be inclusive, we must also work actively to identify and mitigate biases in data collection and analysis. Biases can lead to skewed insights and perpetuate inequalities by reinforcing existing disparities leading to negative outcomes (4).
With inclusive data practice, we can help ensure that the data accurately reflect the experiences and needs of all communities (5). In addition, inclusive datasets are critical to taking advantage of emerging AI technologies. By ensuring that AI systems are trained on diverse and representative data, we can create technologies that are more accurate, reliable, and equitable—ultimately leading to better health outcomes for everyone.
Privacy and security
Protecting the privacy and security of individuals' data is a fundamental aspect of data inclusivity and critical to securing personal health information. This involves implementing robust data governance frameworks that ensure data is collected, stored, and used in ways that respect individuals' rights and comply with regulations (4).
Trust in data practices is essential for encouraging participation in digital inclusion initiatives. Patients should be aware of how their data is used and how they might be used in the future.
Health worker and patient involvement
Engaging health workers and patients in the data collection process is vital. This means involving these communities in designing surveys, collecting data, and interpreting results while ensuring cultural sensitivities are respected.
Health workers can help to ensure that data being collected is clinically relevant. The involvement of patients ensures that the data collected is appropriate and accurately reflect the lived experiences of those it aims to represent (4).
Transparent data practices
Transparency in how data is collected, analyzed, and used is key to building trust and accountability. Clear communication about data use and sharing practices helps stakeholders understand how data inform digital inclusion efforts, and allows for greater scrutiny and improvement of these practices (2).
Data is subject to the law and regulations of the country where it is collected (data sovereignty) and laws should respect the rights individuals and communities have over their data.
By utilizing inclusive health data, countries can make informed decisions for health programs, improving outcomes and fostering trust with communities and patients. Inclusive health data can drive meaningful change by ensuring that all voices are heard and accounted for in health planning and decision making, strengthening the health care system.
References:
- Aghion P, Griffith R. Innovation and inequalities. Oxford Open Economics. 2024;3(1):i1002–i1005. https://doi.org/10.1093/ooec/odad057
- Interaction Design Foundation. What is digital inclusion? The Interaction Design Foundation; 2024. https://www.interaction-design.org/literature/topics/digital-inclusion
- Wongkrachang S. Cybersecurity Awareness and Training Programs for Racial and Sexual Minority Populations: An Examination of Effectiveness and Best Practices. Contemporary Issues in Behavioral and Social Sciences. 2023;7(1):35-53. https://core.ac.uk/download/568393010.pdf
- MIT Technology Review Insights. Digital Inclusion and equity changes what’s possible. Massachusetts Institute of Technology; 2022. https://www.technologyreview.com/2022/03/08/1046844/digital-inclusion-and-equity-changes-whats-possible/
- Jonker A, Rogers J. What is Algorithmic bias? International Business Machines Corporation; 2024. https://www.ibm.com/think/topics/algorithmic-bias
Other resources:
- United Nations. Digital Inclusion. United Nations; 2022. https://www.un.org/techenvoy/sites/www.un.org.techenvoy/files/general/Definition_Digital-Inclusion.pdf
- U.S. General Services Administration website. Diversity, Equity, and Inclusion page. https://digital.gov/topics/diversity-equity-and-inclusion/. Accessed January 9, 2025.
- Yen L. What is data sovereignty? Datamation; 2023. https://www.datamation.com/big-data/what-is-data-sovereignty-and-why-does-it-matter/
