Artificial intelligence (AI) technologies are increasingly gaining traction across various sectors of the healthcare industry. UBC’s Vice President of Product and Innovation, Ron Lacy, recently discussed the widespread adoption of AI, specifically focusing on the utilization of large language models (LLMs) to support the patient services market.
The Evolution of AI in Patient Access
In the early-to-mid 2010s, the industry explored big data and predictive analytics, using AI for the early analysis of patient behavior based on claims data. This claims-based approach used historical data to predict things like enrollment in specialty programs or what a patient’s copay might be.
The evolution is now moving beyond prediction to provide actual access details.
With the use of cognitive AI tools and LLMs, AI agents are now able to make calls and collect data from payers to provide patients with specific answers about what they will really pay.
Challenges and Risks Associated with AI Platforms
While cloud-based AI platforms offer powerful, accessible tools like speech recognition and generative AI models, their utilization presents distinct challenges:
Anomaly Detection
The biggest challenge is anomaly detection—determining if the answer delivered by the AI tool is correct. Unlike a human reimbursement specialist who can recognize incorrect information and hang up to call a different agent, AI cannot yet perform this level of on-the-spot critical assessment.
Change Management
Change management is one of the biggest challenges when it comes to the adoption of AI. Organizations must establish a thorough approach, including pilots and employee engagements, to address pushback and build awareness among various stakeholders at multiple levels.
The Future of AI in Patient Services
Key projections for the deployment of AI in patient access and support include:
- Prescriber Workflow Integration: Continuing the evolution of AI by initiating calls directly to the payer from within the prescriber’s workflow (e.g., while the doctor is dictating the prescription order) to learn about patient access and cost.
- Point-of-Prescription Engagement: Leveraging AI to ensure the right messages are delivered to patients at the right time, possibly via an AI bot interaction while the patient is still in the office.
- Retrieval-Augmented Generation (RAG): Utilizing RAG where a manufacturer has a controlled set of approved information pertaining to the product or therapy, but allows patients to interact and ask questions, receiving controlled content back.
Level Up Your Brand's Patient Access with AI
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