Generative AI in Healthcare and Patient Assistance

The healthcare landscape is undergoing a significant transformation, driven by the emergence of artificial intelligence (AI). While AI has generally found its niche in data analysis and treatment planning, a new wave of generative AI is poised to revolutionize care by optimizing patient assistance programs to improve access, affordability and
social determinants of health (SDOH).  

The Power of AI in Patient Care

AI excels at analyzing vast amounts of data, assisting in earlier disease detection, predicting potential risks and recommending evidence-based treatment plans. AI can also streamline processes and reduce costs by automating administrative tasks like appointment scheduling, billing and claims processing - freeing up staff to focus on patient interaction.  

Generative AI offers a progressive approach to addressing SDOH. This technology can analyze electronic health records, uncovering subtle clues that point to a patient's social struggles. For example, a model might recognize missed appointments due to unreliable transportation or identify mentions of food insecurity through specific keywords used by doctors.  

By identifying these hidden needs, generative AI empowers healthcare professionals to tailor their approach. Imagine a doctor being prompted by AI during a visit: "This patient may be experiencing housing insecurity. Would you like to connect them with local resources?" This real-time insight allows for proactive intervention, potentially improving treatment adherence and overall health outcomes. Generative AI goes beyond identification. It can analyze data on available social services and a patient's specific needs, recommending suitable assistance programs.

The Power of AI in Patient Assistance

The patient assistance process has a lot of moving parts by multiple stakeholders. The complexity and manual work associated with patient assistance programs includes identifying who needs help, which programs offer assistance, eligibility requirements and award coordination makes an ideal candidate for AI. Deploying AI in the area has many benefits to the end user and, ultimately, the patient.  

• An AI matching engine can proactively and automatically surface patient assistance programs which meet insurance, demographic, financial and clinical eligibility criteria for patient assistance programs, eliminating the manual search process.  
• An AI rules engine continuously scans availability and matches across a large network of funding programs.  
• AI can rank-order the matches based upon "predictive forecasting” which will consider how many match requirements have been satisfied in addition to the probability of award success, allowing the user to efficiently enroll patients in to programs.  
• AI monitors the real-time availability of open or closed programs based on funds so that the user is using the most accurate program information for enrollment.
• AI assistant, using large language models, can assist the user by surfacing specific program information when asked and offering next steps.

The integration of generative AI into SDOH management and patient assistance heralds a transformative era where technology and compassion converge. AI has the capability to analyze data, predict needs and offer personalized recommendations. This enables providers to improve access, affordability and address SDOH barriers, effectively improving care and outcomes. As AI technologies continue to evolve, it’s important to note that skilled healthcare professionals will always be crucial for personalized care and addressing complex issues; however, AI can be a valuable addition to the care team.