Artificial Intelligence (AI) is transforming industries at an unprecedented pace, and healthcare is no exception. Health plans, however, have been slower to adopt these revolutionary tools compared to other healthcare sectors such as pharmaceuticals and medical devices. As the industry faces disruption, the time has come for health plans to leverage AI to reimagine their relationships with members, enhance operational efficiency and secure a sustainable competitive advantage.
This blog will outline an approach to AI that health plans could consider in order to accomplish those goals.
Seize Control of Your Data Quality
Clean, consolidated member data is the foundation for successful AI initiatives. AI models must be built on accurate, unified data sets to drive reliable insights and predictions.
Health plans interested in proving out AI use cases are first getting their data in order, using a customer data platform (CDP) to build unified member profiles that are then used to create tailored segments and power personalized member experiences. Because AI is used across any number of customer engagement touchpoints and for a multitude of purposes – analysis, training, predictions, recommendations, content generation, etc. – it is important that a CDP prioritize data quality at the point of ingestion vs. either performing data quality processes downstream or relying on a third-party platform.
Performing all data hygiene and data transformation tasks at the point of entry greatly reduces process inefficiencies and eliminates the snowball effect of poor-quality data that leads to poor member experiences. Consider, for example, using generative AI (GenAI) and a large language model (LLM) to power a member portal chatbot. Effective conversational AI will depend on access to a member record that is accurate and updated in real time to include every member transaction, plan details, behavior, billing history, medical history, prescriptions, etc.
In a Dynata survey commissioned by Redpoint Global, nearly half (48) percent of respondents said they would interact with AI more frequently if it would make their experiences with a brand more seamless, consistent and convenient. On the other hand, 76 percent said they are less likely to trust a brand if they sense disjointed communication with AI across channels. In addition, 58 percent said they want companies to be clear about when AI is being used.
Along the same lines, AI models must train on accurate representations of members. In some use cases, this can be done by feeding models with member data that has been stripped of PHI, but for AI to power a personalized member experience it must train on data that reflects actual member experiences.
Test Before Scaling Using a POC Approach
With data quality taken care of, health plans need to determine a proof of concept (POC) that will prove out the value of an AI initiative. Often, initial AI implementations involve low risk projects such as helping members understand plan options or available benefits, chatbots for member support, anticipating potential detractors, fraud detection and improving services.
For example, in the healthcare ecosystem a significant portion of member interactions occur in a call center where an employee is interacting with a member, helping them with an issue or challenge that often pertains to benefits. A vast majority of those interactions are repeatable, and therefore well suited to be taken over by AI. These and similar types of engagements illustrate the need for AI to train on actual member data, but health plans must also recognize that AI – as yet – is not as adept as humans in showing empathy. There is a balance between what AI and humans can each accomplish in helping members solve an issue.
What health plans can do is make sure that any AI implementation aligns with ethical standards and member-centric goals, in part by ensuring that AI-driven decisions are transparent, fair and beneficial for members. Proving out the value of AI with a successful pilot implementation will help improve operational efficiency while also setting the stage for long-term competitive advantage.
With the launch of a successful AI initiative based on a solid foundation of high-quality data, heath plans can begin to scale AI use cases, from automated member interactions to enhanced predictive models. Successfully incorporating AI-driven decisioning across a member’s complete interaction with a health plan significantly enhances the overall member experience, versus using AI to improve the experience on a specific channel. Health payers at the forefront of implementing AI to improve the member experience have the inside track in developing and promoting meaningful member relationships as a competitive strategy.
Start Your AI Journey Before You Have To
AI is revolutionizing the healthcare industry, offering payer organizations unprecedented opportunities to enhance member engagement, streamline operations and improve overall outcomes. By automating routine interactions and utilizing predictive analytics, AI can significantly reduce costs and increase efficiency. And while successful implementation of AI requires careful consideration of data organization, ethical implications and strategic deployment, health payers should not get bogged down in the planning stages. As AI technology continues to evolve, payer organizations that proactively adopt and integrate these tools will gain a competitive edge, improving member satisfaction and driving better health outcomes in an increasingly complex and demanding healthcare environment.
Redpoint Global has partnered with Engagys, a healthcare consumer engagement consulting and advisory services firm, to assist health plans with maximizing the value of their member data, including how to successfully identify and implement AI use cases. For more on how Redpoint and Engagys can help you maximize member engagement through the effective use of AI, click here.
