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 generally has 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 incredible potential of AI for the medical sector can’t be downplayed. With healthcare professionals hard-pressed to keep pace with ever-growing demand, AI promises to coordinate between sectors like social work and healthcare by identifying the influence of SDOH realities picked up by AI-driven technology. Factors like this hold out the hope of reducing administrative demands, making life less stressful for healthcare professionals. At the same time, AI promises to revolutionize financial assistance programs for healthcare providers, now partnering with AI technology experts to improve patient outcomes.

The Power of AI in Patient Care

AI is ideal for deployment in healthcare, as it excels in analyzing vast amounts of data, facilitating 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.  

Generative AI offers a progressive approach to addressing SDOH and associated barriers in healthcare. 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. The potential for early intervention to prevent potential tragedy is huge when the SDOH implications are fully considered.

By identifying these hidden needs, generative AI empowers healthcare professionals to tailor their approach to patient assistance. 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 potential of AI in the medical sector promises improved response times, enhanced care and the timely delivery of services from social worker to primary caregiver.

The Power of AI in Patient Assistance

The patient assistance process has a lot of moving parts, activated 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. Deploying AI in this area of healthcare 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. By freeing staff resources for patient-facing functions, an automated approach directly serves enhanced care.  
• An AI rules engine continuously scans availability and matches across a large network of healthcare funding programs. This offers patients a greater range of potential funding sources.  
• 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 programs, again improving the patient’s chances of finding the right source of funding. 
• AI monitors the real-time availability of open or closed programs based on funds so that the user is drawing on the most accurate program information for enrollment. This saves time and disappointment, pointing the patient only at viable opportunities.
• AI patient assistance, 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 healthcare funding 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 and affordability and to 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.