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.

Primary care has a critical role to play in ensuring that mission-driven values aimed at eliminating health care disparities are prioritized in the development, selection, clinical implementation, and use of advanced analytics and AI technologies. Because the application of these technologies in primary care is in its infancy, primary care professionals have a unique opportunity to guide the growth of fair, transparent, and ethical AI and analytics applications that embody health equity principles that meet the needs of diverse populations.Today, clinical decision-making in primary care is influenced by the ongoing integration of advanced analytics and AI technologies into the practice of medicine.¹ Examples include patient risk stratification, predictive modeling for disease progression,^2,3 decision-support applications,^4,5 and population health management tools for cancer screenings,^6,7 diabetes,^8,9 cardiovascular disease,^10–12 and other chronic disease conditions.¹³ These and other similar tools may or may not explicitly address the needs of diverse patient populations in primary care. Unless explicit strategies are used to promote equity, advanced analytics may inadvertently perpetuate inequities in primary care delivery, such as the use of algorithms that erroneously treat race categories as biological rather than social attributes in clinical decision making.¹⁴The importance of articulating equity as a specific goal for integrating AI into care is described in the 2019 National Academy of Medicine (NAM) report, Artificial Intelligence in Health Care: The Hope, The Hype, The Promise, The Peril. The report describes a quintuple aim to improve population health, reduce costs, improve the patient experience, promote care team well-being and achieve health care equity.¹⁵ Specifically, the report suggests that embracing health care equity would challenge a siloed approach to health care by addressing the diversity of patient needs using varied sources of data that include social determinants of health and psychosocial risk factors (Fig. ​(Fig.1).1). Equity, integral to the quintuple aim, would also require engaging diverse stakeholders to inform the design of AI applications and to monitor the impact of these technologies. The NAM report underscores the need for explicit strategies to actively embrace health care equity; without such strategies, AI applications are likely to reflect human biases in ways that will widen inequities by race/ethnicity, gender identity, sexual orientation, disability status, age, social class, geography, and other dimensions of social identity.^15,16 Open in a separate windowFigure 1Building on the quintuple aims of equity and inclusion in health and healthcare (National Academy of Medicine).¹⁴Indifference to technology and passive acceptance of biased tools pose risks to health care equity among diverse groups. To prevent this, we must be willing to articulate the priorities for successful AI and advanced analytics implementation and adopt strategies and processes that lead to equitable outcomes. To further these aims, we propose the following series of questions that should be considered before and during the adoption of an AI technology or advanced analytic strategy into practice. First, what needs of diverse patient populations can be better served by applying advanced analytics and AI technology? How can novel and diverse data sources be leveraged to enhance equity in AI implementations? How can patients and community members engage with stakeholders involved in shaping the use of AI in the delivery of health care? And finally, how are principles of diversity and inclusion reflected among those who are involved in the development, selection, and use of technology solutions to enable equitable health care?     

Cytogenetic damage in lymphocytes of rubber industry workers

Cytogenetic investigations were conducted in workers who are occupationally exposed to rubber chemicals such as tetramethylthiuram disulfide, zinc dimethyldithiocarbamate, and tetraethylthiuram disulfide using lymphocytes of peripheral blood. A significant increase in the frequency of chromosomal breaks and gaps was recorded regardless of the duration of exposure to these chemicals.     

Naloxone-insensitive inhibition of acetylcholine release from parasympathetic nerves innervating guinea-pig trachea by the novel opioid,nociceptin

The novel peptide, nociceptin and the μ-opioid agonist [D-Ala², N-Me-Phe⁴, Gly⁵-ol]-enkephalin (DAMGO) produced a concentration-dependent inhibition of electrical field stimulation (EFS)-evoked release of acetylcholine (ACh) from cholinergic nerves innervating guinea-pig trachea. The non-selective opioid receptor antagonist, naloxone, did not antagonize the inhibitory action of nociceptin under conditions where the inhibition of ACh release evoked by DAMGO was completely reversed. It is suggested that DAMGO and nociceptin can inhibit cholinergic, parasympathetic neurotransmission to the airways via the activation of classical (naloxone-sensitive) and novel (naloxone-insensitive) opioid receptors, respectively.