What is TechQuity?

Background

Health inequities have been embedded in U.S. health care delivery since the country's inception. Three seminal reports, the 2001 Institute of Medicine's Crossing the Quality Chasm, the 2003 report Unequal Treatment,¹ and the 2020 National Academy of Medicine's (formerly Institute of Medicine) Artificial Intelligence in Healthcare² represented inflection points in highlighting the substantial disparities in access, clinical care, and outcomes, and recommended that equity in health care and health technology must be achieved to deliver quality care.³ Though Crossing the Quality Chasm set up the STEEEP framework, which explicitly called out equity as one of six health care quality domains (alongside safety, timeliness, effectiveness, efficiency, and patient-centered care) the issue of inequities in health care delivery was truly laid bare in Unequal Treatment, which also called upon health care institutions and providers to develop strategies to confront disparities in care.⁴ Artificial Intelligence in Healthcare introduced the "Quintiple Aim" where "Equity and Inclusion" was added to the "Quadruple Aim."⁵

Since these reports, health care institutions have slowly recognized health care delivery as one of multiple, mutually reinforcing institutions through which structural racism affects the health of people who identify as Black, Indigenous, Asian and Pacific Islander, Latino/a/x, and multiracial groups. Importantly, as technology becomes a greater facilitator and driver of health care delivery, it is urgent to recognize the ways that its misuse may exacerbate structural racism that already exists in the U.S and in medicine. As the impact of the COVID-19 pandemic and social movements of the 2020s have again exposed the crisis of health inequities in the U.S., it is critical that we take this historical moment to promote anti-racism actively in a relentless effort to eliminate inequities in care. As we confront the role of technology as part of the reinforcing structure of health care inequities, we require a framework to guide specific anti-racism activities within health technology applications to reshape the use of technology as a force for promoting equity in health, in ways that begin to address root causes of structural racism. Specifically, an anti-racism and pro-equity approach to the use of technology, or TechQuity, must (1) address structural racism and discrimination to achieve a diverse workforce to co-create and implement technologies that promote health equity, (2) collect and track data that is representative of the concerns and needs of populations that face health inequities, (3) deploy data-driven and technology strategies to hold health institutions accountable for achieving equity and monitoring progress toward this end,

Figure 1.

Advancing the Quintiple Aim.

and (4) use the power of artificial intelligence in transparent ways so that algorithms may be trained on diverse data and used to enhance the health of diverse communities. The definition and key priorities for TechQuity are outlined below.

4 Key Priorities for TechQuity

TechQuity⁶ is the strategic development and deployment of technology in health care and health to achieve health equity.⁷ We highlight four key priorities for TechQuity: (1) workforce diversity; (2) data trust; (3) equity dashboards; (4) transparent AI. (Figure 1)

I. Workforce Diversity

Marian Wright Edelman, founder of the Children's Defense Fund stated, "It's hard to be what you can't see."⁸ While she was referring to the "all-white world of children's books," the same saying can be applied to the worlds of health care and technology. While the U.S. population has over 30% self-identifying as African American or Hispanic,⁹ only 11% of U.S. physicians¹⁰ and 15% of people in technology jobs identify as members of these persistently underrepresented minorities.¹¹ This underrepresentation worsens as you go higher into faculty, leadership, and C-suite positions in both health care and technology.^12,13 We must assure that our workforces represent the populations affected by structural and health inequities that we hope to support and whose health we seek to improve.

II. Data Trustworthiness

Trustworthy and complete data...