By Priyal Patel, Asssociate Vice President, Healthcare Strategy & Solutions
Health plans are losing Star ratings because insights arrive too late to change outcomes. AI closes that gap by getting the right insight to the right person while there is still time to act.
At this year’s Databricks Data + AI Summit (DAIS), health plan leaders consistently pointed to the same challenge: improving outcomes in an increasingly complex data environment. I spoke with Matthew Giglia, Healthcare and Life Sciences Forward Deployed Engineer at Databricks, about how AI is helping healthcare organizations get more value from their data.
More Data Isn’t the Missing Piece
Health plans no longer have a data problem. Most already have access to claims and clinical data, social determinants, member engagement history, quality measures, and risk scores.
But more data has not automatically translated into better Star ratings, faster interventions, or clearer insight into what is working.
That signals an important shift: the bottleneck is no longer data.
The Gap Between Insight and Action
Data was never the real constraint. Decision latency is. An insight sitting in a dashboard doesn’t close a care gap. A risk score doesn’t improve a measure on its own. A report doesn’t change a member outcome unless it reaches the workflow where decisions are made — while there is still time to act.
In many plans, the distance between knowing and doing is still measured in weeks. By then, the opportunity to intervene has often passed.
The problem is not the model or the report. It’s how long it takes to respond once an opportunity is identified. That’s why a health plan can have sophisticated analytics and still struggle to move its Star ratings.
AI Should Not Mean More Noise
The organizations getting real value from AI aren’t using it to gather more information. They’re using it to shorten the distance between insight and intervention.
In practice, AI helps health plans:
- Prioritize what matters instead of surfacing every signal with the same urgency
- Surface risk earlier so care managers and quality teams can intervene before an outcome is locked in
- Reduce the noise that buries care managers in alerts they can’t work through
While the challenge shows up differently for health plans and providers, Giglia sees the same shift from the technology side:
Health systems used to treat yesterday’s data as good enough. Now, with AI in the mix, they need that data connected across every domain it touches: governed, tagged, and traceable back to its source, but usable the moment it’s needed.” — Matthew Giglia, Databricks
The technology to do this exists. The harder work is making it practical inside the workflows care managers already use every day.
Stars Performance Is a Decision Problem
Stars ratings are often treated like a measurement problem — they aren’t. The measures matter, of course. So do dashboards, scorecards, and reporting cycles. But performance changes when teams make better decisions sooner.
That means helping teams answer the questions that shape daily work:
- Which members should we prioritize this week?
- Which interventions are most likely to improve a specific measure?
- Where is staff effort going without measurable impact?
- What actions were taken, and what changed as a result?
Whether the measure involves medication adherence, preventive screenings, or member experience, the challenge is often the same: identifying the right intervention early enough to influence the outcome.
These decisions cannot be answered by visibility alone. They require trusted data, clear prioritization logic, workflow integration, and feedback loops that show whether an action worked.
This is where Stars intelligence needs to show up: in outreach planning, care management queues, quality interventions, and resource allocation. Not after the fact and not buried in another report. It needs to be available in the flow of work while the decision still matters.
How We Partner with Databricks to Close the Gap
Perficient and Databricks partner together to help health plans close the distance between data and action.
Databricks provides a governed foundation that brings claims, clinical, and social determinants of health data into a trusted environment for analytics and AI. Perficient helps health plans put that foundation to work. We define the governance, prioritization logic, and adoption strategy needed to move AI from concept to operational impact.
The goal is not another proof of concept. It is to help care managers, quality leaders, and operations teams make faster, better-informed decisions in the workflows that drive Star performance.
Our Databricks Brickbuilder Specialization for Healthcare & Life Sciences reflects that combination. It brings together technical depth on a governed platform with healthcare expertise that translates AI into measurable outcomes.
That’s exactly the kind of work we want partners to lead. We can provide the platform and accelerators, but healthcare expertise is critical to making the solutions real.” — Matthew Giglia, Databricks
The Plans That Win Won’t Be the Ones with the Most Data
Health plans need to prioritize getting trusted information to the people who know what to do with it — while there is still time to change the outcome. That is the practical promise of AI in Stars performance.
Data, models, and platforms all matter. But they are infrastructure. The real advantage is the ability to decide and act faster than the plan next to you.
That’s what will move a Star rating next cycle: not another layer of data, but whether the member who needed the call got it in time.
Curious what else came up in health plan conversations at DAIS this year? See Why AI Stalls for Health Plans: Turning Data Into Action to see how this same issue is playing out across the industry.
Connect with our Healthcare & Life Sciences team to see what closing the distance between data and action could look like for your plan.
