PQA Webinar Recap: Connecting Medication Adherence and Patient Outcomes

Health plans are under growing pressure to prove that medication adherence efforts improve member health, not just scores. That challenge was at the center of Beyond the Scoreboard: Connecting Medication Adherence and Patient Outcomes, a recent PQA webinar featuring Nicki Ghazanfarpour, Director of Pharmacy Clinical & Quality Programs at CalOptima Health, and Jimmy WuRohe, Chief Product Officer at Clarest Health.

During the conversation, the speakers discussed the limits of legacy medication management programs and what health plans can do differently. They emphasized personalizing support for each member, engaging providers on their terms, and using technology to strengthen, not replace, clinical judgment. Ultimately, their vision is a more holistic model of pharmacy care, one that connects the plan, the provider, and the member. The following are key takeaways from the event.

Understanding the Barriers Behind Non-Adherence

Not every adherence gap comes down to motivation. In vulnerable, complex populations, social barriers can make it hard to stick to medication plans. These might include limited health literacy, transportation issues, medication costs, unstable housing, and language differences.

Closing those gaps starts with personalized support, like plain-language education, help with copays, and synchronized refills that cut down on pharmacy trips.

Meeting Providers Where They Are

Providers are key partners in this process as they can adjust a member’s regimen once they understand the barriers, whether that’s simplifying a dosing schedule or switching to a more affordable option.

However, getting providers invested is its own challenge.

“Telling a provider that their patient is non-adherent via fax typically results in no change,” Ghazanfarpour said.

WuRohe pointed to three levels of alignment that can move the needle: appealing to clinical best practices, fitting into workflows providers already use, and tying in financial incentives.

Ultimately, the best approach is interdisciplinary, bringing together members, care teams, health plan pharmacists, medical directors, case managers, and behavioral health clinicians.

Setting Realistic Expectations for Predictive Analytics

On catching adverse events early, WuRohe noted, “These are hard problems. There’s no algorithm out there that can perfectly predict what’s going to happen.” In healthcare, prediction is even more complex because it involves both human biology and human behavior.

That doesn’t mean health plans should abandon predictive analytics, just that they should set realistic expectations. Used well, it helps teams target interventions and outreach more precisely.

Four Questions to Ask Before Adopting a Tool

Before adopting any tool, WuRohe recommended asking internal analytics teams and vendors four key questions:

1. What data are you using?

2. How much better can you actually predict versus the current state?

3. How have you validated this?

4. How do you monitor and maintain it over time?

Predictive Analytics vs. Generative AI

Accurate predictions about member outcomes depend on solid clinical data. Many vendors use “predictive analytics” and “generative AI” interchangeably, but they aren’t the same. The frontier models behind generative AI tools are good at working with language, not at predicting outcomes from data. “They help us train our models faster. They help us architect. They help us simplify, but they’re not built for prediction,” WuRohe said.

To make the best choices for their members, plans should keep asking vendors questions and dig into the clinical logic behind the data to make sure it’s sound.

Where Pharmacy Teams Add Value, and Where the Data Falls Short

“Pharmacy teams bring clinical know-how and data-driven mindsets,” Ghazanfarpour said. “We review the whole medication profile, making sure it’s safe, effective, and easy to manage.”

That includes checking whether a regimen fits the member’s health, age, and daily routine, and turning a complicated history into clear, practical recommendations for the care team. But even with that expertise, blind spots remain in catching problems before they escalate.

What Medication Data Misses

No single data source tells the whole story. Fill history, medical claims, and lab results each offer clues, like late refills or multiple prescribers. But none can confirm that a member is actually taking their medication.

Transitions of care expose that gap most clearly. That’s why proactive screening matters, catching early warning signs before they become an avoidable ER visit. Technology helps flag high-risk combinations, but teams have to manage alert fatigue.

“A flag is only useful if it reaches the right person, gets reviewed quickly, and leads to meaningful action,” Ghazanfarpour said.

Easing Clinician Burnout

“Clinical burnout is real, and resourcing is just tight,” WuRohe said. Risk prediction helps teams focus where it counts. “If you have limited resources, you really want to prioritize the members that need the intervention the most,” he said.

But prioritizing is only part of the problem. Care teams are also worn down by constantly moving from one member’s case to the next while trying to keep up with best practices.

Additional technology can ease that burden: a recommendation engine helps by surfacing the right guidance for each member, so clinicians don’t have to track every best practice themselves.

Grounding those recommendations in a library of established clinical rules is what makes them dependable. That foundation helps keep any AI tools accurate and aligned with medication safety standards.

Building a Shared, Member-Centered View

For members juggling multiple conditions, a breakdown often happens at the intersection of the plan, the provider, and what’s going on at home.

Health plans see the pharmacy and medical data; providers see the patient in the office. But members face challenges (transportation, food insecurity, behavioral health, side effects) that aren’t always visible to either.

Closing that gap takes a joint effort to see the whole member. Ghazanfarpour outlined several ways to put this in place:

• Pull together data: Combine pharmacy, medical, and care management data to spot patterns and adjust when something isn’t working.

• Address urgent needs first: Handle behavioral health, housing, or food access before medication management can even start.

• Personalize outreach: Tailor communication channels (text, email, or phone) and timing based on each member’s past engagement.

• Listen to member goals: Ask members directly what success looks like to them, since it isn’t the same for everyone.

• Close the feedback loop: Ensure every team member knows what action was taken to prevent fragmented systems.

When the plan, provider, and member share information and address real-life barriers together, the care plan becomes practical and far more effective.

The Future of Pharmacy Care

Looking ahead, WuRohe framed a holistic “pharmacy-as-care-delivery” model around three shifts:

1. End vendor fatigue: Unify adherence, polypharmacy, and medication review programs into one coordinated member journey instead of siloed checklists.

2. Support discharge transitions: Follow members from facility to home with proactive discharge reconciliation rather than mailing a 30-day supply and hoping for the best.

3. Stay clinician-led: Use predictive tools to point pharmacists toward the members who need a human touch.

As the conversation made clear, closing medication gaps requires shared data, connected teams, and technology that augments clinical judgment so pharmacists can focus on the members who need them most.

That’s the thinking behind Clarest Path, Clarest Health‘s medication intelligence platform, which brings all five critical medication-related Star Ratings measures under one roof to reduce utilization and improve member outcomes.

To learn more, read the press release.