What is TechQuity?

ABSTRACT

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. Equity Dashboards The application of analytics to demonstrate health care quality in the domains of safety, timeliness, effectiveness, efficiency, and patient-centeredness has been common in diverse dashboards for hospital ratings and other key health care certifications (e.g., National Committee for Quality Assurance, Joint Commission);however, equity has often been overlooked.17 Peter Drucker, a famous business thinker and writer for the modern company, stated that "if you can't measure it, you can't improve it. [...]we must move AI from being a "black box" to a "clear box" with AI factsheets like nutrition labels where buyers and end-users of AI algorithms can transparently see who trained the AI, what datasets were used, and what specific AI algorithms and models were used.28 We must assure transparent, ethical, fair, and equitable AI. Institute of Medicine, Board on Health Sciences Policy, Committee on Understanding and Eliminating Racial and Ethnic Disparities in Health Care.