Generative artificial intelligence (AI) is rapidly transforming healthcare across the globe. It is already reshaping how healthcare operates. Generative AI is useful in streamlining clinical trial services, enhancing diagnostics, and transforming drug discovery. While gen AI is not a healthcare panacea, it does offer organizations a new tool for tackling complex challenges that traditional methods have struggled with. One such challenge is patient engagement. Many healthcare companies are unable to keep pace with changing patient expectations for how they engage with their health system.
Generative AI holds immense potential to revolutionize patient engagement by delivering personalized, scalable, and culturally sensitive support throughout the healthcare journey. Gen AI offers the possibility to advance direct patient engagement, with high potential to alleviate health system burden, reduce healthcare provider burnout, and improve patient experiences and outcomes.
Use case 1: Health education assistance
Before ChatGPT, nearly all healthcare consumers used the internet to educate themselves about their health, self-diagnose, understand treatment protocols, manage side effects, and address their mental health. However, many resources available on the internet lack quality and are hard to understand, especially for those with limited health literacy or English proficiency.
Gen AI can help provide patients and caregivers with reliable and accessible health information. Large-language models (LLMs) can be trained on high-quality health data, eliminating the shortcomings that plague traditional search. They can also be trained to be polylingual, delivering correct answers that are sufficiently understandable.
Use case 2: Co-pilots for patient triage
The COVID-19 pandemic worsened the global shortage of healthcare workers, leading to increased doctor burnout, staff departures, and declining patient experiences. The public suffered the result of this shortage in the form of health disparities and rising healthcare costs.
To help manage the shortage, many organizations have started using AI-powered co-pilots, such as chatbots, as “virtual care assistants,” helping healthcare providers focus resources on patients needing urgent attention. While these AI platforms show promise, they have failed to scale due to limited language coverage and difficulties adapting to diverse cultural contexts.
With domain-specific and culturally sensitive training, generative AI can help address many of these gaps and improve healthcare delivery worldwide.
Use case 3: Disease management interventions
After diagnosis and the start of treatment, a patient’s health journey is just beginning. Following the treatment is crucial for managing illness, especially chronic conditions, yet many patients either miss doses, take the wrong dosage, or abandon treatment altogether, leading to significant healthcare costs. Although traditional predictive algorithms can help predict when a patient is likely to miss a dose or drop treatment, they struggle to suggest effective interventions to keep patients on course.
Generative AI can enhance predictive algorithms by taking the output from a classical algorithm and using it to tailor interventions for individual patients. It can also integrate various data sources, such as genetics and biometrics, to personalize care plans, benefiting both healthcare providers and biopharma organizations.
Generative AI can create powerful patient engagement solutions, helping organizations achieve better health outcomes through tailored, data-driven support throughout the patient journey. Future advancements may include AI-driven virtual health coaches that adapt in real-time to patient needs and hyper-personalized clinical trial matching to accelerate research. By embracing these innovations, healthcare organizations can improve outcomes and reduce disparities, ensuring equitable access to high-quality care.
