AI can transform health care benefits administration

Organizations are increasingly incorporating AI, ML, and automation into open enrollment.

Employees are looking for competitive benefits, especially in health care. But for employers, providing great health care benefits can be costly, and comes with significant administrative responsibilities. Employers everywhere are grappling with the escalating time and budget demands of benefits administration, as evidenced by various studies over the last two decades. Administrative expenses account for roughly 15% to 25% of total national health care expenditures. That adds up to an estimated $600 billion to $1 trillion annually.

Artificial intelligence (AI), machine learning, and automation have emerged as transformative solutions for this challenge. These technologies hold promise to:

The AI revolution in health care benefits

AI tools, such as machine learning (ML) models, can streamline repetitive administrative tasks for benefits administrators. They save staff members precious time and provide a better member experience. These tools can also predict disease risk and other behaviors to target the right interventions to the right people in a member population. They can provide self-service analytics insights and model contract parameters to make better benefits decisions.

Reducing administrative burden with AI

AI technology can process enormous volumes of data in minutes. It can perform tasks quickly that would have taken benefits administrators or employers hours (or days) to complete manually. These capabilities result in time and cost savings – reducing administrative overhead and allowing employers, and the administrators and consultants that support them, to divert resources to enhance client experience.

Organizations are increasingly incorporating AI, ML, and automation into open enrollment to:

AI also facilitates personalized decision support to help employees make better benefits decisions. Advanced models can utilize employee health data and other information to present customized benefit recommendations. That helps employees find the most cost-effective coverage when there are multiple options to choose from, leading to happier employees.

AI chatbots can also support common questions and issues to reduce the administrative burden on benefits professionals. In addition, this technology ensures accuracy in claims processing through automated verification and scrubbing services.

Predictive analytics for cost optimization and disease risk management

In health care benefits administration, member-centric tools with predictive analytics insights are ideal for detecting cost trends and patterns. For example, a health care benefits administrator might see costs trending up due to emergency room visits for the flu in their analytics data. AI algorithms can analyze member data to predict which members are least likely to get their annual flu shot. The software can create a cohort of members at high risk of flu-related complications and future ER visits. Administrators can use this information to plan effective educational campaigns on flu vaccination, or make a case for supporting on-site flu shot clinics to encourage higher vaccination rates.

Predictive models also play a critical role in mitigating disease risk. AI-driven models can pinpoint individuals at risk of developing chronic conditions, including:

With this information, benefits administrators can consider actions to improve employee health. That might include steering people to preventive care, partnering with care managers to increase surveillance, or looking for point solutions that help prevent or manage these conditions. The end result is wellness programs personalized for the needs of each member. Participants can engage in various programs or services with unique goals and interventions that cater to their health needs.

Unlocking and harnessing data

Health care benefits administrators have access to immense volumes of member data they can use to enhance the quality and effectiveness of benefit programs. But it’s challenging to effectively harness and leverage this information to make decisions. Recent advances in generative AI provide more opportunities to make use of all the data available. AI tools with the capability to engage in unsupervised learning can analyze member information and answer questions benefits administrators often ask. It can also answer questions they never thought to ask or provide valuable insights they didn’t know they need.

Self-service analytics software equips individuals with tools to access and manipulate data, regardless of their prior experience in statistical analysis or data mining. The result is more timely, meaningful, and adaptable data. Administrators can then design optimal benefit plans and streamline decision-making to satisfy rapidly-evolving employee needs.

Implementing an end-to-end solution

As more AI solutions emerge in the employee benefits space, they bring the additional challenges of data interoperability and security. Juggling multiple vendors and getting third-party applications and point solutions to work together to produce necessary insights and cost savings can get very complicated.

With each additional vendor comes new security risks, compliance protocols, and software management tasks. A better solution is a comprehensive platform that offers seamless technology integration, with self-service capabilities for plan design and reporting. An end-to-end solution from a single vendor minimizes the dependency on outsourcing IT support. It also centralizes member data in one place. That makes it easier to identify savings opportunities, manage vendors to ensure ongoing program effectiveness and ROI, and report those insights to key stakeholders in an organization.

The benefits administration industry of tomorrow

Many organizations are already using various forms of automation in benefits administration. The next wave of generative AI and machine learning can revolutionize the industry. These transformative solutions will streamline workflows, optimize costs, and enhance the benefits landscape for companies ready to make the most of new technology.

Related: How is technology impacting the benefits industry?

Predictive analytics in end-to-end value-based care platforms enhance data utilization, improve decision making and foster a more responsive approach to benefit plan design. An AI-powered future of health care benefits administration holds the promise of significant cost savings, member-centric care, and enhanced user experiences, ultimately benefiting employers and employees.

Tim Huke is chief growth officer at Cedar Gate Technologies.

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