NewsClosing the Health Data Gap: Prioritizing Equity in AI for Older Adults

Closing the Health Data Gap: Prioritizing Equity in AI for Older Adults


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Artificial Intelligence (AI) algorithms hold immense promise for revolutionizing health monitoring for older adults, offering early detection of chronic diseases, expanding telemedicine in rural areas, and tailoring personalized treatment plans. Despite these advancements, there is a pressing need to address the data gaps within these algorithms to prevent widening health inequities among older adults.

The existing healthcare algorithms are predominantly built on data that excludes the experiences of older adults, exacerbating gaps in race, gender, and income representation. Demographic and health surveys often omit crucial age groups, perpetuating biases and hindering the potential benefits of AI in healthcare. To rectify this, innovators, entrepreneurs, and investors must play a pivotal role in addressing these gaps.

Key Recommendations:

Bridge the Data Gap for Marginalized Older Adults:

Prioritize the inclusion of aging populations, especially those marginalized, in big data generation and collection efforts.

Address data acquisition and disaggregation for underrepresented segments by amplifying the voices of those with lived experiences.

Invest in emerging data scaling strategies such as cache database queries, database indexes, replication, and sharding to fill the data gap effectively.

Navigate the Democratization of AI:

Define and integrate the strategic importance, effects, and management of AI across the healthcare sector.

Infuse equity into emerging health tech solutions by advancing the quality and accuracy of data and data-dependent tools.

Increase investment in data generation and collection efforts focusing on social determinants of health that contribute to health inequities in aging populations.

Prioritize Equity as a Competitive Lever:

Recognize equity as a competitive advantage in delivering high-quality healthcare solutions.

Align with the push for value-based care by prioritizing solutions that provide measurable, scalable gains in equitable health outcomes for older adults.

Regulators and policymakers should incentivize initiatives that prioritize measurable gains in equitable health outcomes.

Equitable AI is not just an aspiration; it is an absolute necessity, especially for the millions of older Americans currently underserved within existing frameworks. Innovators, entrepreneurs, and investors have a unique opportunity to lead the charge in establishing robust data foundations, ensuring that the algorithmic benefits of risk profiles and early interventions are accessible to all older and marginalized adults.

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