How AI is Revolutionizing Health Plan Operations
With the rapid growth of artificial intelligence (AI) use cases in healthcare, the opportunity for operational efficiency is key for health plans to deliver high-quality care while managing costs effectively. After spending a week with health plan executives and payer-focused partners, three use cases stood out and were mentioned in every conversation across the board.
1. Data Extraction & Data Actionability at Scale
The amount of data available to the payer sector of our industry is immense – not just in volume, but in diversity of format. The ability to ingest, normalize, and extract all relevant data elements from both structured and unstructured data sources and match it back to the individual member in a meaningful way at scale is challenging – both in operations and in infrastructure investment. Additionally, the need for all teams within a plan to access this valuable member data and take action based on their own individual use cases is key to enabling a truly data-driven approach to improving the health of their membership. That is where AI comes in. AI tools that can ingest any data format health plans process today are vital to extracting all clinical data – from PDFs, CCDAs, Images, FHIR, Provider Notes, Inferred Content, Narrative Text, and more, and providing access across the enterprise allows for health plans to improve the usability and actionability of member data at scale, not just for one purpose or one-use case.
2. Multi-Use Case Solution to Support Many Opportunities
Health plans are looking to streamline as much of the data and analytic processing with partners who support an enterprise approach versus a point solution was very popular in our discussions. For example, one health plan manages 35 different medical records partners just acquire data to support risk adjustment and quality improvement – that is just two use cases, when medical records are used for dozens of operational activities at a health plan. This also doesn’t include the ingestion or analyzing these records. It was also mentioned that many partners in this space were unable to process structured and unstructured data in a single process, so health plans are leveraging multiple vendors just to perform NLP review of records. Solutions that solve multiple use cases (risk adjustment, HEDIS®, prior authorization, care management, fraud, waste, and abuse, etc.) in a single process allows health plans to save time and money, while delivering the best care to their members.
3. Predictive Analytics Powered by AI
AI-powered predictive analytics enables health plans to process mass amounts of data very quickly to identify various opportunities to improve their members’ health. These analytics can tell you what members are at risk for certain health conditions or adverse events, infer what social factors the provider has included in text that may predict worse health outcomes, identify potential fraud activity by analyzing claims, clinical data patterns, and historical information, look at behavioral engagement trends for the best communication approach to intervene and inform members, and the list goes on and on. The traditional models have been focused on more statistically driven analytics driven by flat files or a database – limiting the scalability and speed needed to be effective and meaningful in an industry focused on a member’s health – one of the most important parts of a person or loved one’s life.
It was clear in all the discussions, the growing popularity and excitement around AI technologies offer great opportunities for health plans to leverage the data they process and make it actionable, while accelerating speed in decision making and decreasing cost by eliminating the number of manual interventions required to operate. Few in the market are looking at this opportunity to deliver enterprise value beyond narrow focused use cases. Players in this space with a vision to support a comprehensive approach to AI-strategies will allow for greater adoption of these solutions, accelerating adoption and solving some of the most vital challenges for health plans today and in the future.