Engagement That Anticipates and Addresses Member Barriers

Nearly a decade after CMS launched the Meaningful Measures Initiative, quality measurement has fundamentally shifted from retrospective, process-based reporting to outcomes that reflect real patient experience and clinical impact. Meaningful Measures 2.0 and NCQA’s acceleration toward Electronic Clinical Data Systems (ECDS) signal an irreversible move toward fully digital, patient-centered measurement built on electronic clinical data, patient-reported outcomes, and real-time performance feedback.

As CMS targets a fully digital Stars ecosystem by 2030 and NCQA sunsets hybrid reporting by 2029, plans are already seeing material performance divergence exposed by ECDS transitions. At the same time, rising Stars cut points and the outsized financial influence of triple-weighted measures have narrowed the margin for error, making accuracy, speed, and data accountability mission-critical for plans reliant on bonus payments to fund benefits and member experience investments.

This evolution has also changed the rules of member engagement. Static, campaign-based outreach models cannot keep pace with members whose clinical needs, access barriers, and experience drivers shift continuously across the year. As experience and patient-reported outcomes such as CAHPS take on greater weight, projected by some analysts to approach 40% of Stars by 2029, plans must move toward real-time, signal-driven, coordinated engagement strategies.

By combining real-time clinical signals with behavioral and situational insights, and applying AI-enabled analytics across quality, pharmacy, and care coordination, plans can identify the member’s needs sooner, determine the next best action, and intervene without increasing member abrasion. In fact, the plans best positioned for the next phase of Stars will treat member engagement as a dynamic, precision-driven capability that at its best can anticipate gaps by:

• Leveraging real-time signals allowing for quicker interventions.
• Utilizing AI and advanced analytics to create deeper insights and faster decisions.
• Individualizing outreach and coordinating care to move the member to action.

Rebuilding Member Engagement

Member engagement hasn’t evolved much from the early days of preventive screening and gaps reminders. While today’s approach now includes “digital channels” like text and email in addition to mail-based and call center outreach, it still relies heavily on predictive analytics and persona-based outreach that cannot adapt to rapidly changing health status or identify the real reasons gaps remain open, often catching risk too late. And many organizations still run multiple campaigns targeted at individual members, increasing abrasion. Successful member engagement programs require a different level of systems logic built across departments that operates on real-time clinical, situational and behavioral signals that adjust with and understand the member’s changing needs.

Complex Clinical Signals

Clinical signals based on data and member interaction monitoring are foundational to all predictive analytics models, identifying when something has or hasn’t happened, like usage of a concurrent medication or missing a mammogram. However, without an overlay of Stars measure logic, they cannot address the “blind spots” inherently built into the Stars measures themselves.

Each Stars measure requires a different strategy, and this is especially true when harmonizing claims data and pharmacy activity. Traditional “personalized engagement” strategies will look for a gap and outreach to the member on that gap but lack inherent understanding of the measure itself. For example, a member who is currently on anti-anxiety medication and has a previous history of falls and hospitalization will be at risk for Poly-ACH. Proactive outreach to that member about the risk of taking opioids with an anti-anxiety can work to prevent the member from entering the denominator in the first place if they find themselves hospitalized again. For those that do enter the denominator, immediate triage needs to take place to prevent them from entering the numerator.

AI-assisted outreach becomes a powerful tool in this new era of complex clinical conditions allowing plans to coordinate measure outreach not only based on the complexity of a single measure, but also for multiple measure gaps. By coordinating these efforts across departments, and creating a more wholistic view of the member, plans not only maximize increasingly scarce resources, but create a member experience that naturally reduces abrasion and is more impactful.

Assessing the Situation

Clinical signals enhanced with situational signals provide instructions on not only what to ask the member to do, but how to do it. Overlaying the initial clinical signal with previous claims data, member complaint logs, care coordination call insights and previous survey responses creates a better understanding of the underlying situation for each member, improving the efficiency of outreach.

Situational signals can enhance existing persona work by going beyond basic segmentation to infer context. For example, signal systems can take members with a medication adherence gap and:

• identify a subset of people that are not engaging with the “90-day mail order pharmacy is convenient” messaging
• have claims activity pointing to a disability
• infer that for those members it may be an actual mobility and transportation issue, not a convenience issue
• tailor messaging towards transportation and access.

These situational signals can also inform the right mechanism for outreach. In the previous, concurrent medication example, AI-assisted outreach can identify that the member has entered the numerator and needs an immediate phone call rather than an informative letter.

Understanding the Behavior

Behavioral signals round out the member engagement puzzle as they help uncover underlying motivations for why the member is struggling to close the gap. They also help inform the right tone and best mechanism for outreach.

Is the member overwhelmed by a recent diagnosis and struggling to cope with filling prescriptions? Are they struggling financially and unable to afford medication or appointments? Do they actually feel fine and don’t see the need to go to the doctor or fill a prescription? These nuances help cater messaging in a way that shows the member they are understood and meets them in the moment.

Particularly in the member experience Stars measures, “personalization” needs to move beyond language of preference and preferred outreach channel to a deep understanding of each member as they change in real-time with health status.

The Power of Care Coordination

As member journeys become more clinically complex, getting members to do what you need them to do isn’t just about identifying gaps and moving the members to act. It’s about providing coordinated resources that support the action.

A fully operating signal system identifies, for example, the absence of a colorectal screening, understands that the member is feeling overwhelmed by the thought of a colonoscopy and can also recommend and deliver an at-home FIT test to efficiently close the gap. It can also focus on coordinating with primary care providers to help bridge the gap between them and specialists, as members notoriously forget or omit things between visits.

It Starts and Ends with Signals

As the health care industry shifts away from retrospective, process-based reporting to fully digital, outcomes-driven measurement centered on patient experience, clinical quality, and value-based care, plans leveraging real-time claims data paired with deep member insights will outperform their peers.

Static segmentation, retrospective outreach and siloed measure campaigns can’t address the growing clinical complexity of members and their rapidly changing expectations and needs. The future of member engagement is signal-driven, coordinated and patient-centric. By embracing AI-assisted analytics and harmonizing quality, pharmacy and care coordination efforts, health plans can prioritize the most impactful gaps, reduce member abrasion and improve outcomes at scale.