Health Care Equity in the Use of Advanced Analytics and Artificial Intelligence Technologies in Primary Care

The integration of advanced analytics and artificial intelligence (AI) technologies into the practice of medicine holds much promise. Yet, the opportunity to leverage these tools carries with it an equal responsibility to ensure that principles of equity are incorporated into their implementation and use. Without such efforts, tools will potentially reflect the myriad of ways in which data, algorithmic, and analytic biases can be produced, with the potential to widen inequities by race, ethnicity, gender, and other sociodemographic factors implicated in disparate health outcomes. We propose a set of strategic assertions to examine before, during, and after adoption of these technologies in order to facilitate healthcare equity across all patient population groups. The purpose is to enable generalists to promote engagement with technology companies and co-create, promote, or support innovation and insights that can potentially inform decision-making and health care equity.

How the Trump Administration’s Pandemic Health Care Response Failed Racial Health Equity: Case Studies of Structural Racism and a Call for Equity Mindfulness in Federal Health Policymaking

Context: The racial health equity implications of the Trump administration’s response to the COVID-19 pandemic. Methods: We focus on four key health care policy decisions made by the administration in response to the public health emergency: rejecting a special Marketplace enrollment period; failing to use its full powers to enhance state Medicaid emergency options; refusing to suspend the public charge rule; and failing to target provider relief funds to providers serving the uninsured. Findings: In each case, the administration’s policy choices intensified, rather than mitigated, structural racism and racial health inequality. Its choices had a disproportionate adverse impact on minority populations and patients who are more likely to depend on public programs, be poor, experience pandemic-related job loss, lack insurance, rely on health care safety net providers, and be exposed to public charge sanctions. Conclusions: Ending structural racism in health care and promoting racial health care equity demands an equity-mindful approach to the pursuit of policies that enhance—rather than undermine—health care accessibility and effectiveness and resources for the poorest communities and the providers that serve them.

Measuring Health Equity in Emergency Care Using Routinely Collected Data: A Systematic Review

Identifying health care equity indicators is an important first step in integrating the concept of equity into assessments of health care system performance, particularly in emergency care. We conducted a systematic review of administrative data-derived health care equity indicators and their association with socio-economic determinants of health (SEDH) in emergency care settings. Following PRISMA-Equity reporting guidelines, Ovid MEDLINE, EMBASE, PUBMED and Web of Science were searched for relevant studies. The outcomes of interest were indicators of health care equity and the associated SEDH they examine. Among 29 studies identified, 14 equity indicators were identified and grouped into four categories that reflect the patient emergency care pathway. Total emergency department (ED) visits and ambulatory care sensitive condition-related ED visits were the two most frequently used equity indicators. Despite some conflicting results, all identified SEDH (social deprivation, income, education level, social class, insurance coverage and health literacy) are associated with inequalities in access to and use of emergency care. In conclusion, the use of administrative data-derived indicators combined with identified SEDH could improve healthcare equity measurement in emergency care settings across health care systems worldwide.