Jeff M.

Smarter notes start with smarter tools! DAX Copilot doesn’t just transcribe—it listens and learns. With AI-driven suggestions, it helps you fill in the gaps in your patient notes, like adding family history or key health metrics. More complete records, less second-guessing!

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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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One of the most impactful design decisions I made on a healthcare project was implementing Medallion Architecture (Bronze → Silver → Gold) in Databricks. At CVS Health, we were ingesting large volumes of pharmacy claims, eligibility, and provider data coming from PBMs like OptumRx in multiple formats (JSON, XML, Parquet). What I actually did: 🔹 Bronze Layer I landed raw claims and eligibility feeds exactly as received from S3, without transformation. This helped us with auditability, reprocessing, and HIPAA traceability when downstream issues occurred. 🔹 Silver Layer Here I applied real business logic — Deduplication of claims Eligibility validation Standardization of member, provider, and plan identifiers PHI masking rules This layer became the trusted operational dataset used across teams. 🔹 Gold Layer I built analytics-ready tables for reporting, SLA monitoring, and downstream dashboards. These were optimized for Redshift/Snowflake consumption and Power BI reporting, with performance tuning and partitioning. Why this worked in production: Reduced reprocessing effort when source data changed Clear separation of concerns between raw, clean, and business data Made debugging claims issues much faster Helped us stay compliant with HIPAA and GDPR This architecture wasn’t theoretical — it directly supported near real-time eligibility checks and helped reduce claim adjudication errors by ~30%. Sometimes good architecture isn’t about fancy tools — it’s about designing data layers that teams can actually trust. #DataEngineering #Databricks #DeltaLake #MedallionArchitecture #HealthcareData #AWS #Snowflake

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You trained the model. You trusted the output. But can you trace the data that led to the decision? 👀 AI in analytics isn’t dangerous because it’s wrong. It’s dangerous because it’s convincing, even when it’s wrong. 📰 This article breaks down the real risks of AI-powered insights and how to design for trust with traceability, governance, and human oversight. Read it here: https://lnkd.in/eKuQnv7J

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Too many data projects stall before delivering value. Why? Teams try to design the perfect solution up front. A better pattern I’ve seen work: • Create a few metrics. • Release a small dataset. • Publish a simple dashboard or visualization. • Watch how the business actually uses it. Then improve it. Small, fast releases create momentum, reveal what really matters, and get insights into people’s hands sooner. You don’t need the full blueprint on day one. 𝐒𝐭𝐚𝐫𝐭 𝐬𝐦𝐚𝐥𝐥. 𝐃𝐞𝐥𝐢𝐯𝐞𝐫 𝐟𝐚𝐬𝐭. 𝐋𝐞𝐚𝐫𝐧. 𝐈𝐭𝐞𝐫𝐚𝐭𝐞. How do your teams approach this—incremental releases, big launches, something in between?

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1 Comment

Shift from data reports to true data mastery. I remember feeling a disconnect, often reviewing reports without truly understanding the 'why' behind every metric. That feeling stayed with me when I started building the CMGP Calculator. I watched physicians rely on mental math to estimate their billing time. They would guess how long a referral letter took. They would approximate their charting minutes. And they would consistently underestimate both. The gap between work done and work billed was not a knowledge problem. It was a visibility problem. So I built the calculator around a simple principle: do with, not do for. The tool does not bill for you. It does not automate your decisions. It does not replace your judgment with algorithms. It shows you your own patterns. It teaches you to see the time you spend. It helps you understand what you are entitled to bill. This matters because AI scribes are flooding the market right now with a different promise. They listen to your visits. They write your notes. They suggest billing codes. But they only capture what happens inside your EMR. They miss the time you spend in NetCare. The treatment research you do between patients. The referral letters you write after hours. All that cross-platform work that makes up actual patient care. And they create dependency. You hand over the thinking. You lose the competency. You become reliant on a system that does not teach you anything. The calculator takes the opposite approach. When you track your time, you start noticing patterns. You realize you spend 20 minutes on complex referral letters. You see that pre-appointment chart review adds up. You discover you are giving away hours of billable work every single day. And then something unexpected happens. You start batching similar tasks together. You create dedicated blocks for prescriptions, for referrals, for lab reviews. Your workflow becomes more efficient because you finally see how you actually spend your time. One physician told me the calculator helped her reorganize her entire day. That was not the goal. But it was the result of making invisible work visible. Competency beats convenience every time. When you understand your own data, you make better decisions. When you see your own patterns, you optimize your own workflow. When you build your own strategic literacy, you do not need to outsource your thinking to a black box. The calculator is not the smartest tool in your stack. It is not the most automated. It is not the most seamless. But it teaches you something that lasts longer than any subscription. It teaches you to see your own value.

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