Nathan Rasch, MBA, PMP, SAFe SM, CSM

How do you know if your developer is giving you a good deal? Most teams don’t — not because they lack expertise, but because developer incentives aren’t always aligned with the owner. A pattern I’m seeing: Operator brings their “preferred developer.” No RFP. No competitive pressure. Just: “Trust us.” 12-18 months later… Hidden fees surface. Terms shift. A termination fee appears out of nowhere. When the developer controls the process instead of the owner, surprises aren’t a fluke—they’re predictable.

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Systems Design Interview Question: Q. You've been given a gratuitous $5K ER bill because insurance won't pay -- how do you resolve? A. 1. Attempt to access patient portal online to get records 2. Find patient portal, create account 3. Discover patient portal access requires invitation 4. Look for patient portal invitation department online, can't find 5. Call hospital switchboard to find out which department issues invitation 6. Get transferred to hospital IT, wait on hold for 10 minutes 7. IT can't do it, but they give you number for who can 8. Call them; phone menu is bunk, take a guess 9. Get the right person -- find out you have to go in in-person, takes 45 minutes after 10. Ask person if ER visit data will be accessible -- find out "they just do registration" -- gotta call medical records 11. Call medical records, find out "yes" Pretty simple design that I felt sick and old people can easily follow. Incredibly this design did not net me an offer and now I'm hella resentful!

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Last week I said: “Changing an EMR isn’t a software project. It’s an operational transformation.” Here’s the uncomfortable truth: Most EMR transitions don’t fail because of the vendor. They fail because leadership underestimates the disruption and overestimates their internal alignment. An EMR exposes every weak process you’ve been tolerating. • Unclear ownership • Inconsistent documentation habits • Billing shortcuts • Workarounds no one talks about When you change the system, those cracks widen. For CEOs: If you treat an EMR switch as an IT initiative, expect a revenue dip. It’s not a maybe. It’s math. For operators: If you don’t redesign workflows before go-live, your team will redesign them under stress after go-live. And that version is always worse. The organizations that succeed don’t “install” an EMR. They redesign their operating model first. Technology doesn’t create dysfunction. It reveals it. What’s one hard lesson you learned the first 90 days after go-live? #EMR #HealthcareOperations #HelathcareLeadership #RevenueCycle #HealthIT #ChangeManagement

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Tassanee hit the nail on the head. This is exactly why “the numbers don’t lie” only works if the numbers are actually accurate. Claims and performance data weren’t designed to be perfect, and small gaps can cascade into major consequences for providers and patients. Aligning data and creating a single source of truth is essential for value-based care to work in practice.

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𝗠𝗼𝘀𝘁 𝗵𝗲𝗮𝗹𝘁𝗵 𝘀𝘆𝘀𝘁𝗲𝗺𝘀 𝗱𝗼𝗻’𝘁 𝗵𝗮𝘃𝗲 𝗮𝗻 𝗔𝗜 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. 𝗧𝗵𝗲𝘆 𝗵𝗮𝘃𝗲 𝗮 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗯𝗹𝗲𝗺. Most “AI for EHR” demos look impressive: one use case, one model, a slick hop from note → code → claim. Then reality hits. The bridge snaps where it hurts most: At the seams between workflows (scribe → coder → auditor → rev cycle). When OIG, HIPAA, CMS, or internal audit start asking, “Why did the AI do that?” In my experience, sustainable healthcare AI rests on three foundations:  1. A unified data fabric so AI sees the full longitudinal patient story, not just today’s note.  2. An orchestration layer that connects documentation, coding, quality, rev cycle, and care management into one governed workflow, not a pile of point automation.  3. A robust governance and evidence chain that can withstand regulatory, legal, and clinical scrutiny with clear accountability for model behavior over time. Emerging AI governance frameworks in healthcare all converge on this direction: cross‑functional AI councils, lifecycle oversight, internal validation of local data, and explicit ties to existing clinical and operational governance. Health systems that treat AI as “just another app” bolted onto the EHR are discovering they can’t scale pilots faster than they can mature their governance, data, and operating model. So instead of asking, “Which AI scribe should we pilot next?”, CIOs, CMIOs, and operations leaders should be asking:  “𝗪𝗵𝗮𝘁 𝗸𝗶𝗻𝗱 𝗼𝗳 𝗳𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻 𝗮𝗿𝗲 𝘄𝗲 𝗯𝘂𝗶𝗹𝗱𝗶𝗻𝗴; 𝗼𝗻𝗲 𝘁𝗵𝗮𝘁 𝗰𝗮𝗻 𝘀𝘂𝗽𝗽𝗼𝗿𝘁 𝗱𝗼𝘇𝗲𝗻𝘀 𝗼𝗳 𝗔𝗜 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 𝗮𝗰𝗿𝗼𝘀𝘀 𝘁𝗵𝗲 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲, 𝗼𝗿 𝗮 𝗰𝗼𝗹𝗹𝗲𝗰𝘁𝗶𝗼𝗻 𝗼𝗳 𝗯𝗿𝗶𝘁𝘁𝗹𝗲 𝗯𝗿𝗶𝗱𝗴𝗲𝘀 𝘄𝗲’𝗹𝗹 𝘀𝗽𝗲𝗻𝗱 𝘁𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗶𝘃𝗲 𝘆𝗲𝗮𝗿𝘀 𝘀𝗵𝗼𝗿𝗶𝗻𝗴 𝘂𝗽?” Where are you already seeing cracks in your organization, and what has helped you move closer to bedrock? #ResponsibleAI #HealthITLeadership #AITransformation #Interoperability #HealthSystemStrategy

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7 Steps to Measure Your Team’s Coding Efficiency If you pay attention to these, you’ll realize high-performing coding teams are not an accident, they are built with intention. 1. Measure Accuracy, Not Only Speed. Fast coding means nothing if the denial rate climbs. Precision is the foundation, protect it. 2. Track Turnaround Time (TAT). Great coding doesn’t sit in a queue. If charts are waiting days to be touched, you’re not operating, you’re accumulating revenue delays. 3. Audit Consistently. Consistent micro-audits prevent large-scale surprises. Small corrections early prevent big downstream problems later. 4. Compare Productivity to Complexity. Not every facility works the same. Productivity goals shouldn’t be one-size-fits-all, they should reflect the way your workflows, tools, and chart types actually function in real life. 5. Watch the Denial Pattern, Not Only the Denial Count. If the same code edits repeat, it’s not a payer issue, it’s a pattern. Fix the root cause. 6. Measure Learning Velocity. How fast does the team adapt to new payer rules, new doctors, new templates, or new specialities? Agility is a performance metric. 7. Track Consistency Over Time. Anyone can have a strong week. Elite teams deliver stable performance month after month. Here’s the truth: Coding efficiency isn’t only about volume output, it’s about accuracy, consistency, adaptability, and discipline. The best coding teams aren’t found. They’re built, one standard, one habit, one improvement at a time. #medicalcoding #ICD10 #CPT #AAPC #AHIMA #RCM

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Here’s something I’ve been thinking about lately: what if we approached RCM like a product team approaches UX? → Patients = users → Payments = conversions → Statements = the interface That would mean: 📉 Mapping where patients drop off between estimate and payment 🧪 Testing different formats, reminder timing, channels 🎯 Optimizing for ease and trust — not just collection speed It’s already happening in some systems — and it’s changing how they build loyalty. 💬 Anyone experimenting with this approach? Share an example in the comments — I’d love to hear what’s working on your side. #RCM #PatientBilling #ProductThinking #HealthTech #DigitalHealth

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Your EMR stores information. It doesn’t store understanding. That’s the gap. It’s why teams repeat intake. It’s why handoffs feel like “good luck.” It’s why critical nuance disappears at exactly the wrong moment. Before: Handoffs = summaries + scattered notes + memory After (what good looks like): Handoffs = a shared storyline + clear next actions + documented reasoning When continuity of understanding improves, three things change fast: 1️⃣ Fewer repeat questions 2️⃣ Cleaner escalations 3️⃣ Real accountability across shifts This isn’t “more documentation.” It’s less confusion.

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Delivering AI-assisted ICD10 coding, AlphaCoding is built on AI models and guided by Centers for Medicare & Medicaid Services (CMS) standards. #meaningfulai #healthcaretech #augmentedintelligence #revenuecycle

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The May 2026 deadline for WCAG 2.1 AA isn’t a suggestion—it’s a countdown to a major regulatory shift in HealthTech. Is your platform a liability or an asset? 🛡️♿ Many digital health providers believe accessibility is just about "colors and fonts." It’s not. It’s about ensuring life-critical health data is reachable for everyone, from patients with visual impairments to providers with motor disabilities. As we move toward 2026, HealthTech leaders must address three strategic pillars: Operational Risk: Non-compliance is becoming a fast track to litigation and lost institutional contracts. Clinical Safety: Accessible interfaces reduce cognitive load and prevent medical errors in high-pressure environments. Market Expansion: Universal design isn't just ethical; it expands your user base to a massive, underserved demographic. At Hristov Development, we don't just "patch" accessibility. We integrate audits and frontend refactoring directly into your SDLC. Our nearshore teams ensure that your compliance journey doesn't slow down your product roadmap. Is your engineering team ready for the audit? Swipe through to see why accessibility is the new standard for HealthTech excellence. 👇 #HealthTech #DigitalHealth #Accessibility #WCAG2026 #SoftwareEngineering #Nearshore #HristovDevelopment #InclusiveDesign

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