Equitable healthcare for marginalized older adults begins with equitable data collection and application.

  • Despite the boom in healthcare technology over the last decade, the quality of care for older adults remains insufficient, due to the lack of standards for equitable health and social data collection and its application to include older adults, especially marginalized populations. 
  • This means that data-dependent development shaping healthcare technology, products, services, and innovation is not only inadequately meeting the needs of the older adults who already spend the most on healthcare, but is also deepening bias and historical inequalities.
  • TSF believes the data collected and used by the technologists, entrepreneurs, funders, and researchers should be more inclusive and equitable.

Areas of Need

Inadequate data on marginalized populations impedes our ability to understand inequities and evaluate interventions.

  • The amount of data required for emerging technologies is significant, and access to data is fragmented across many systems, making it difficult to paint a full picture for individuals and groups.
  • Disaggregated data is often unavailable and expensive to obtain, so data are grouped into broad and heterogenous categories, which masks characteristics, needs, and outcomes across marginalized groups and limits our ability to investigate the intersectionality of demographic factors (e.g., intersection of age, race/ethnicity, income, and disability status).
  • Globally, demographic and health surveys typically exclude women aged 50 and over and men aged 55 or 60 and over from their remit.1

Bias in the application and use of data is deepening historical inequalities.

  • Bias in model design can shape how race and other social factors are treated in an algorithmic model (i.e., use of poor proxies or provider scores).
  • A widely used algorithm used for healthcare prioritization was found to drastically underestimate the needs of Black patients compared to white patients with similar needs.2

Grant, Investment, and Partnership Focus

  • Enhance representation of aging populations in big data generation and collection. Bridge the data gap by funding solutions that address data acquisition and/or disaggregation for underrepresented population segments.  
  • Advance the quality and accuracy of data and data-dependent tools to improve health and social care outcomes for older adults. Invest in data generation and collection efforts that focus on upstream structural factors that drive health inequities for aging populations.
  • Support industry standards for health data that are inclusive and elevate older adults’ best interests. Elevate data equity as a consequential issue for cross-sector leaders and catalyze market-driven standards.
  • Democratize the application of emerging tech to benefit aging populations. Sponsor innovative approaches to assessing the value and impact of algorithms and predictive analytics on the aging population.