Michael Toby

During AEP, brokers juggle multiple priorities. Knowing where to focus determines whether millions of Medicare members get the support they need. We built a data product: a single, trusted view with clear ownership and standardized definitions—designed to guide 2M+ Medicare members toward better coverage during AEP 2025. The shift was clear: from reactive reporting to action enablement. Data products turn analytics into an operating capability. When Analytics teams own products, they don’t report outcomes—they drive them. What decision in your business would benefit most from a true data product? #DataProducts #HealthcareAnalytics #AnalyticsLeadership #AEP #HealthTech

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5 questions that separate real predictive analytics from expensive reporting tools. Most Medicare Advantage plans ask vendors about data integration and dashboard features. Those questions get you pretty slides. Here's what actually matters: Most plans ask: "What data sources do you integrate?" You should ask: "Can you show me the member view as your third dataset?" Claims and clinical data tell you what happened. The member view tells you why. If a vendor can't surface member experience alongside outcomes, you're buying half the story. Most plans ask: "What's your predictive accuracy?" You should ask: "Show me how your model performed on last year's Stars ratings for a plan like ours." Back testing matters more than benchmarks. If they can't demonstrate retrospective accuracy on YOUR measures, the predictions are theoretical. Most plans ask: "What's your implementation timeline?" You should ask: "What's the time to first measurable ROI, and how do you define it?" Vendors love talking about go-live dates. You need to know when bonus payments improve or member retention moves. The difference between these questions？ One set evaluates technology. The other evaluates whether technology actually improves member lives and your bottom line. That's the standard your analytics vendor should meet. #Analytics #HealthcareAnalytics #PredictiveAnalytics #MA #MedicareAdvantage

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📘Resource: Behavioral Health Revenue Cycle Management Payer rules, coding standards, and documentation requirements are always evolving — making strong revenue cycle management more essential than ever. Our newest eBook breaks down the strategies, metrics, and EHR capabilities behavioral health organizations need to improve financial performance and long-term sustainability. Inside, you’ll learn: • Key EHR features that support effective RCM • How to tackle common RCM challenges • Essential metrics to track • How to prepare for value-based care • What to ask when evaluating EHR vendors Developed by the behavioral health experts at Core Solutions, this guide helps you identify revenue-hindering gaps and implement the right technology for long-term success. 🔗 Better RCM starts here — download the guide today: 👉 https://hubs.ly/Q046X7Cq0

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👩‍💻 From Order Taker to Strategic Partner: The Evolution of Healthcare Data Translation In my early days, I thought being a great data enginee/viz developer meant answering every data request quickly and exactly as asked. Need 15 filters on that dashboard? Sure. Want 6 years of claims data joined with real-time EHR feeds? No problem. But over time, I realized: The business doesn’t need a data vending machine it needs a thinking partner. 🧠 Executives want a report. But what they need is to understand how patient access delays are impacting revenue. Clinicians want metrics. But what they need is insight into bottlenecks that hurt care quality. That shift from fulfilling to translating changed everything for me. Here’s how I started making that shift: 🔄 I stopped starting with the data. I started with the decision. What would you do differently if you had this? became my favorite question. 🎯 I translated “asks” into “impacts.” Not build me a dashboard, but help me understand. For example: What’s the financial impact with similar patient outcomes? 🧭 I created a ‘Minimum Viable Insight’ approach. What’s the smallest, fastest way to inform the decision before we build the full thing? 🤝 I sat in on operational meetings. Not to present data but to understand context. What keeps leaders up at night? 🚦 I built a ‘Decision First’ intake framework that prioritizes requests not just by urgency, but by expected business value. To be promoted into leadership roles, we can’t just build data pipelines we have to build bridges between business need and technical capability. 💬 Don’t just give them what they asked for. Give them what they need even if they don’t know it yet. Curious to hear from others: Have you made this transition in your data career? What helped you stop being an order taker? #healthcaredata #dataengineering #leadership #datastrategy #analytics #decisionintelligence

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I’ve often come across job descriptions for Biostatisticians and Healthcare Data Analysts that sound nearly identical. These roles were once quite different as one is focused on statistical research and the other on healthcare operations and analytics, but today, the gap between them is narrowing as the industry evolves. With a Master’s in Healthcare Data Analytics and a major in Biostatistics (I, II, III), along with over two years of experience on NIH-funded and other federally supported projects, I’ve worked extensively in R, using data visualization packages like ggplot2, ggraph, lattice, ComplexHeatmap, plotly, etc., and ML packages such as caret, randomForest, glmnet, e1071, and mlr. I also use SQL, SAS, Tableau, and Excel for data synthesis, analysis and visualization. My training includes CITI certification in both human and non-human subjects research, as well as expertise in HIPAA compliance, IRB protocols, and data governance. I have built a strong foundation in both research and applied analytics. Back in my Pharmacy undergrad (2013 to 2017), I remember plotting dose-response trends using Excel bar graphs and histograms. Today, students are learning tools like R, Python, and Tableau right from the start. It’s incredible how quickly technology is advancing in this space. While I’m proud of how far I’ve come, I sometimes wonder if current job roles truly recognize hybrid skill sets like mine. Are organizations keeping up with new technologies in data science? Are experienced professionals being supported through these changes, or are they expected to adapt constantly just to stay relevant? I often think of my father, who once shared how difficult it was when younger managers dismissed his experience. That stayed with me. I believe that age is not a limitation, and experience is an asset that deserves recognition. So I ask: - Are we creating a future that values both innovation and experience? - Or are we moving toward a workplace that pressures people out rather than helping them grow? I would love to hear your thoughts, especially from those in healthcare, public health, or data science who are navigating change or supporting others through it. #HealthcareAnalytics #Biostatistics #DataScience #HealthTech #CareerGrowth #Upskilling #AIinHealthcare #ExperienceMatters #RespectExperience #WomenInSTEM #PublicHealth

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Most people think Healthcare Analytics = SQL + Power BI dashboards. That’s barely 20% of the job. The real learning begins when you work with real US healthcare data. Here’s what it actually taught me 👇 🧩 1️⃣ Data is always messy Claims don’t arrive on time. Provider IDs don’t match. Diagnosis codes keep changing. If you wait for “perfect data”, analysis will never start. 🧠 2️⃣ Business context matters more than queries The same metric means very different things in: • Medicare Advantage • Fee-for-Service Without context, dashboards become dangerous. 🎯 3️⃣ Accuracy beats speed In healthcare, a small mistake can: • Change risk scores • Impact reimbursements • Affect clinical decisions ⚠️ Fast but wrong = failure. 🗣️ 4️⃣ Stakeholders don’t care about tools They care about: • Cost reduction • Quality improvement • Compliance If you can’t explain insights simply, analytics fails. 📊 5️⃣ Analytics is decision support, not reporting Great analysts answer: 👉 “What should we do next?” Not: ❌ “Here is the data.” 🔑 This mindset shift is what separates: ❌ Report builders ✅ Business analysts 📌 Follow for real-world healthcare analytics insights #HealthcareAnalytics #USHealthcare #DataAnalytics #AnalyticsMindset #CareerGrowth

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