How AI can democratize healthcare, foster equitable access

In American healthcare, access to quality medical services remains a privilege often dictated by factors beyond one’s control. These disparities, deeply ingrained in the fabric of society, manifest in differing life expectancies and unequal health outcomes. Yet, amidst these challenges, Artificial Intelligence’s (AI) transformative potential holds the promise of transcending barriers, democratizing healthcare, and fostering equitable access to vital services.

Healthcare leaders must lead the way in harnessing AI to promote equitable access to quality care. Through strategic leadership, advocacy, and collaboration, company leadership has the opportunity to leverage AI as a powerful tool for advancing health equity and improving health outcomes for all individuals.

Addressing healthcare disparities
The reality of living in the US means that skin color, social status, education level and zip code can determine a person’s life expectancy. Navigating the complexities of regular screenings, understanding diagnoses, and adhering to treatment regimens poses significant challenges regardless of one’s educational level.

However, studies have shown that people without a college degree have mortality rates of more than four times their peers who attained a college education. While education isn’t the only factor, ensuring that patients receive information about their risk factors, diagnoses, and treatment plans in language they can easily understand is critical to improving their health outcomes. AI, with its large language models, has the power to help reduce healthcare disparities due to education gaps and increase access to disadvantaged populations.

Those innovations are coming fast and furious – and the potential to enhance patient literacy is a compelling reason for the healthcare industry to quickly embrace AI. Healthcare tends to move slowly, thanks to heavy regulation and the need for patient privacy, but leaders can’t wait to take action. As J.P. Morgan noted recently, the industry is ready and eager to execute AI solutions but has never had to respond to anything as fast or transformative as AI.

Seizing the opportunity to improve patient outcomes while significantly lowering costs means that healthcare will ultimately be more accessible to more people. Leaders must begin to react quickly: the current moment represents the smallest gap for understanding and harnessing AI before the technology continues on a path of exponential growth. Infusing AI into health outreach and services for people with limited formal education will connect more patients to tailored care, more quickly and effectively than current methods.

Translating the patient-doctor relationship
Understanding health conditions and treatment options can pose significant challenges for nearly every patient, as the complexities of science and medicine are inherently difficult for the average individual to grasp. All too often, errors or misunderstandings occur between doctors, patients and their caregivers. There’s no way for healthcare workers to provide the right-level of education and support for every patient, from every background. We’re left with a huge margin for communication errors, with no human-enabled solution in sight.

This scenario is ripe for AI intervention. Generative AI is trained on large language models and could potentially enable more streamlined yet customized conversations when a patient seeks care. Imagine a doctor being able to provide information to a patient at the appropriate level of easy understanding – spanning from elementary school to doctoral understanding. AI could also be upskilled to account for societal and cultural references, including formal and colloquial language, to ensure healthcare professionals are aware and can minimize the risk of misunderstanding or offense.

Adding an additional layer of complexity is the issue of trust between patients and the medical establishment. Lower income and minority patients have reported lower levels of trust in the clinicians who treat them, often rooted in historical injustices, unethical experiments, or personal experiences of mistreatment. This lack of trust can lead to decreased adherence to treatment recommendations and ultimately, poorer health outcomes compared to patients who have confidence in their healthcare providers. Again, effective communication that resonates with patients on their terms can help to build feelings of trust and partnership between medical teams, patients and caregivers.

Improving research by leveraging RWD and RWE
Incorporating real-world data (RWD) and real-world evidence (RWE) into research methodologies offers invaluable insights into patient behaviors beyond clinical settings, enhancing the evaluation of health outcomes. Both RWD and RWE rely on self-reporting by patients that may vary widely and lead to discrepancies in information sharing. If a patient is participating in an observational study or monitoring their own chronic condition, it’s critical that they’re able to provide the most accurate data. Researchers may need to combine self-reported patient data with sources such as health records and disease registries. This large trove of data is an opportunity for AI to synthesize and analyze RWD and RWE to provide researchers with perspectives on how patients are managing their health conditions.

When applied to patients of varying educational backgrounds, GenAI could combine self-reported data at the appropriate level and evaluate patient behaviors beyond clinical settings. The tools could be customized to formal or colloquial language and cultural background then translated into the appropriate language level for research purposes. In addition, LLMs are increasingly multi-modal, meaning that numerous data types such as photos and images could be included to train the AI model. The multi-modal capabilities will allow even more information to be included and reveal a more comprehensive 360-degree view of patients.

A universal example of this concept is pain management. People are notoriously terrible at assessing their pain levels using absolute numbers and scale. Conventional pain assessment techniques relying on subjective 1 to 10 scales or emoticon-based indicators are inherently flawed due to their reliance on individual interpretation. Instead, using AI, a patient could take a photo of themselves when they’re experiencing pain and describe their physical state to help train the tool and enable more accurate reporting of pain. That type of reporting would allow a doctor to ask for and assess comparisons: “Is your pain greater than yesterday? Is it greater than an hour ago?”, enhancing the overall quality of care.

Challenges for adoption and how to solve them
Delivering AI to a complex industry like healthcare requires complex evaluations. Navigating regulatory authorities, safeguarding patient confidentiality, fostering linguistic inclusivity, and refining user experiences are just a few of the multifaceted challenges that must be considered and adequately planned for to ensure success. AI experts who also understand the unique healthcare environment can provide guidance as the technology becomes more advanced and better positioned to make a positive meaningful impact on patients.

Healthcare inequities have been prevalent in the US since the beginning of modern medicine. But as awareness, prevention, diagnosis, and treatment advance, the potential to leverage AI for widespread benefit across diverse demographics becomes increasingly apparent. Investing in technology today that will help close the language gap means improving the health outcomes of generations of Americans today and moving into the future. Forbes