How Data Influences Value-Based Care

Here's how we built a customized drug trend benchmark to be used in value-based contracts and showed the true value of a company’s programs. --- Benchmarking is a critical tool for any company aiming to improve its performance or maintain a competitive edge. How can you evaluate your performance without relevant comparisons? Approaches to benchmarking can vary but are typically things like your pricing compared to Medicare, utilization or unit cost compared to a national average in the same line of business, or enrollment/spending trends. While broad industry benchmarking is valuable by itself, there are many companies that could benefit from a customized benchmark, particularly in #ValueBasedCare (VBC). --- Business problem: your company has dialed in the process for managing #DrugCosts for diabetes, but you don't think you're getting full credit for the value of your services because your VBC contracts compare you to national level drug trends for all drugs. For many employers, antidiabetics represent the fastest growing drug category, so you want the contract to compare your trends only to antidiabetic trends, or potentially only to trends for specific drug classes like GLP-1s and SGLT2 inhibitors. The complexity of your problem is compounded by offsetting trends even within the antidiabetic drug category because of dropping insulin list prices and rising GLP-1 utilization and rebates. --- The project: build a customized drug trend #benchmark for diabetes drug spending to be used in VBC contracting. We used Medi-Span to identify all the drugs in the antidiabetics GPI and built a drug trend model using the 90 million commercially insured lives available in Milliman’s data assets (this could also be done with Medicare or Medicaid lives). Rebates were estimated using SSR Health data, which helped smooth out the big changes in insulin list prices (from the AMP cap removal) and GLP-1 net prices (as more competition enters the market). Most employers care about net costs, not gross costs before rebates, so the inclusion of estimated rebates was vital for the benchmark. This could be further customized to specific regions of the US if the client wanted to get extra detailed in their benchmarks for a big contract. --- Armed with a customized benchmark that highlights the true value of their business, the VBC company can accurately price their services based on that value. Plus, their customers can more easily see the drops in diabetes trends (that the VBC company can actually control) instead of a slightly lower overall drug trend that could be due to several things that happened this year.

Smart Data Infrastructure: The Missing Piece in Value-Based Care Looking through the U.S. Department of Health and Human Services (HHS) AI use case inventory, I was thrilled to see data infrastructure work on the list [1]. I see it as the foundation for everything else. When data flows seamlessly in (near) real-time, amazing things become possible - even without complex predictive algorithms. Like in cooking, quality ingredients often matter more than fancy techniques. Today, I was analyzing the National Syndromic Surveillance Program (NSSP) ED visit data for RSV from the Centers for Disease Control and Prevention (CDC). While the current one week-ish reporting lag isn't bad, I keep thinking about the possibilities with real-time data infrastructure. And I'm not just talking about speed - reliability and consistency are equally crucial. Just like in patient care, being fast only matters if you're also accurate. For Medicare ACOs and MA plans, timely disease surveillance could transform how we work: - Proactively educate care managers about high-risk areas with precision timing, reducing alert fatigue and false positives that often plague current systems - Reach out to vulnerable patients (COPD, asthma) through text, email, or phone when risk is actually elevated, not just based on static rules - Enable smarter triage decisions at urgent care and PCP levels Prevent unnecessary ED visits (here's where the ROI comes in) One prevented ED visit saves thousands of dollars (maybe more). Most importantly, doing this at the "right" time, not all the time, can save a lot of unnecessary hassles and help us avoid alert fatigue - both for patients and providers. When we combine individual patient data with broader public health context (like this RSV surveillance data), we can make smarter decisions about when to intervene. This shift from reactive to proactive care mirrors what we're trying to achieve with data infrastructure - preventing information delays that lead to missed intervention opportunities while avoiding the burnout that comes from constant, context-free alerts. Although AI/ML gets all the spotlights these days, I often find that the most impactful innovations aren't in complex algorithms, but in building robust data highways that enable timely, informed decisions. Better data infrastructure makes AI more powerful by providing fresher, more actionable training data. After all, value-based care isn't just about savings - it's about right care, right place, right time, in the right hands. By the way, the chart below shows the RSV ED percentage in Georgia broken down by counties. As can be seen, it's the peak season. Be careful out there! [1] https://lnkd.in/eS6ESVv7 #HealthcareInnovation #ValueBasedCare #PopulationHealth #Healthcare #DataAnalytics

Patients don't experience healthcare in weekly reports. Yet that's exactly how most hospitals track patient feedback. I was recently shown how the patient feedback process was done. By the time the leadership team saw patient comments, almost two weeks had passed since discharge. Think about what this means in real life: → A patient misunderstands medication instructions on Monday → By Wednesday, they're experiencing complications → On Friday, they end up back in the ER → Two weeks later, leadership learns what went wrong This is not a feedback loop. The gap between when patients need help and when we learn about it isn't just a data problem - it's where patient safety breaks down. I've seen firsthand how real-time feedback transforms care: → Daily alerts flagging patients who need immediate follow-up → Dashboards showing emerging issues before they become trends → Instant insights that clinical teams can act on immediately Traditional surveys tell you what went wrong last month. Real-time feedback shows you what's happening now, when you can still make a difference. For healthcare leaders navigating the shift to value-based care, the question isn't whether you need data - it's whether you're getting it fast enough to prevent readmissions and improve outcomes. It saves lives.