Industry Changes:

For patients, 2026 will be a year of significant change when it comes to their health insurance. Some notable changes include:

Medical-benefit management is on the rise and payers are noticeably using pharmacy benefit-management tactics on medical-benefit drugs as the use of provider-administered biologics increase. Seventy percent of plans surveyed indicated that they have a medical drug formulary in place.

Additionally, in 2026, there are more than 400 changes to CPT codes. For patients on medications that are infused, injected, biologic, or considered a specialty drug, we anticipate an increase in claim denials due to a mismatch in coding alignment with the revised CPT descriptors.

Medication Coverage Changes Will Drive Volatility in 2026

For patients on specialty drugs or biologics – or those that are infused or injected – we anticipate seeing more midyear plan changes that increase:

• Prior authorizations: included in 58% of plans, including the addition of more drugs to the PA lists

• Quantity and duration limits, 41% and 11% respectively

• Increases in denials and appeals, creating spikes in patient inquiries

Insurance Changes Impacting Drug Costs

Adjustments to subsidies and premiums will make out-of-pocket costs unpredictable. Patients will need help understanding:

• Whether their therapy is covered under medical or pharmacy benefit
• How formulary changes affect access to infused or specialty drugs

Our Use Case:

Medical- and pharmacy-benefit models share a level of payer complexity that patient support service programs are uniquely equipped to handle. These often include:

• Prior authorizations
• Benefit investigations

Where medical benefits increase in complexity are the multiple touchpoints often needed between the clinic, payer, brand manufacturer, and patient due to site-of-care issues, scheduling, and clinician-driven workflows.

These moving parts make call volume and case complexity difficult to forecast with historic data alone. Predictive analytics empowered with AI is positioned to fill that gap by improving demand planning in a medical-benefit environment.

Our future state ideal would be the ability to forecast call volume based on leading indicators and not just month-over-month call history. Leading indicators that may increase call volume could include:

• Payer policy changes
• Payer documentation updates
• Changes in prior authorization forms or criteria.

Using predictive analytics to analyze both call notes, and payer policy changes, we will be able to identify early signals that allow our Workforce Intelligence team to forecast upcoming workload spikes days or weeks before they hit the queue.

Closing Thought:

Medical-benefit patient support services cannot rely on traditional staffing models. Flexibility will be the differentiator this year, and predictive analytics will be the enablement tool that supports:

• Flex Staffing
• Skill-based Routing
• Tiered Agent Pools
• Temporary Surge Teams
• Dynamic Scheduling