The global health equity community is buzzing over the surge in new technologies and programs being developed and rolled out to facilitate solutions aimed at enhancing health outcomes and equity. Just as artificial intelligence (AI) is upending many industries with its transformational applications, AI is poised to similarly power new tools and innovative solutions in the health equity realm.
One of the more widely discussed applications for health equity is generative AI, which many industry observers anticipate could drive more than $1 trillion in health care savings, including for historically underrepresented populations who traditionally have had less access to medical professionals, effective treatments, and overall healthcare resources.
Separately, another AI application, extractive AI, possesses potentially even greater possibilities for healthcare organizations that disproportionately serve more vulnerable populations. A wealth of research has shown over time the importance of incorporating social determinants of health (SDoH) in evaluating a patient’s medical history and condition. However, without access to data that enables a truly comprehensive understanding of the factors influencing a patient’s health, healthcare organizations cannot maximize the total care they provide. But the good news is that extractive AI can bridge that gap; namely, it can allow for data interoperability, without a massive tech spend, to enable health clinics and other medical facilities to do more with patient data, even with fewer resources.
Examples abound that highlight extractive AI’s applications that support greater health equity. One practical application allows organizations to convert even handwritten text into organized data that can easily be connected to patient records. Moreover, while the larger healthcare organizations possess a much greater range of patient data than their smaller, resource-constrained counterparts, extractive AI can help “even the playing field” by identifying, incorporating and leveraging key data points that they historically could not utilize without immense costs and heavy IT integrations.
In short, AI facilitates the enhancement of health equity at a lower cost and a more minimized level of bandwidth constraints for healthcare organizations. Additionally, as extractive AI can enable data access and use by medical providers, clinics and other healthcare facilities that historically have not only been more cash-strapped, but also serve as the primary providers for underrepresented populations, industry observers and health equity advocates anticipate AI to increasingly power solutions that enhance access and equity in the healthcare space in the months and years ahead.
