Michael Wang

AI is making healthcare billing so accurate that payers are actually panicking about costs going up. Let that sink in for a second. The problem isn't that AI is billing wrong. It's that AI is billing RIGHT. Providers are now capturing charges they were literally leaving on the table before because humans couldn't keep up with the documentation. This is wild because it exposes something nobody wants to talk about: our entire system was built around inefficiency. We relied on human error and slowness as a cost control mechanism. The moment you automate that away, suddenly you've got real visibility into what care actually costs. Here's the thing though. Healthcare payers are panicking and reaching for the old playbook: more denials, payment delays, prior auth friction. That's just fighting the symptom. The real question is whether they're willing to actually look at what the data is telling them about billing patterns and provider behavior. This is happening in every industry that's automating manual processes. Accuracy feels like a cost problem until you realize you were just subsidizing inefficiency the whole time. What happens when AI makes your entire cost model obsolete?

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🧠 Everyone wants to build HealthTech products that scale. But scaling in healthcare isn’t just about velocity - it’s about rigor. You’re not just shipping features. You’re earning trust across users, buyers, regulators, and clinicians. That means: Product ≠ MVP. Product = workflow, risk profile, data flow, and compliance posture. Product-market fit isn’t enough. You need product-market-trust. GTM without alignment? That’s a misfire waiting to happen. Would love your thoughts or war stories. What have you seen break first?

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Building UIs has never been easier with Tailwind. It turned styling into a productivity superpower and AI amplified that even further. But AI is also slowly breaking the economics behind open source (or at least Tailwind). It’s eating the discovery, traffic, and revenue that made open source sustainable The tools are winning, but the business models aren’t. If we don’t figure it out, we risk losing many tools that built the modern web.

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I recently learned about a cardiology clinic built specifically for South Asians: SSATHI at Stanford University. It made me pause. Why would we South Asians need our own heart-disease program at one of the world's top medical institutions? Then you look at the data: 1) In California, South Asians have the highest rate of hospitalization for coronary artery disease, 4x higher than any other ethnic group. 2) 25% of heart attacks in South Asians happen under age 40, 50% happen under age 50 3) South Asians will comprise 25% of the world's population, yet account for over 50% of the world's cardiovascular deaths. This isn't random. It's an inherited pattern. Once you see it, you can't unsee it. South Asian biology was shaped by centuries of scarcity, low protein intake, and metabolic efficiency, all adaptations to help ancestors survive. Some research suggests we have smaller coronary arteries, which means plaque becomes dangerous earlier & at lower levels. On top of higher risks, the traditional annual workups don't look at the deeper markers—like detailed cholesterol breakdowns or inflammation signals—that actually matter for our community. So important risks often get missed. Your health isn't just your habits. It's also your ancestry, your physiology, and the evolutionary backstory that came before you. This is nothing to be "fearful" of. It's just that, once you understand it, you can take a far more personalized approach with early screening, prevention, and lifestyle. This is just one example. Every community deserves healthcare designed around its own biology. There's a large opportunity and impact for physicians who are specialized in this (e.g. physicians who specifically focus on South Asians). p.s. SSATHI stands for South Asian Translational Heart Initiative at Stanford. "Sathi" also means friend in several SA languages...very fitting for such a thoughtful program :)

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Most healthcare AI creates more work than it saves. The problem isn't the technology. It's how companies are building it. I've been building Entropy (YC S24) with my co-founder Hashim, automating workflows for referral-based clinics. Most founders I see building for healthcare think it needs automation. But they're missing something critical: context that's practice-specific. Most AI healthcare solutions are just static automations. You automate one workflow. It works for a few months. Then something changes. For example, a clinic automates mapping incoming referrals to the right providers and insurance companies. Everything works great until a new doctor starts referring patients. The automation reads the doctor's name but doesn't know what to do next. So it either breaks completely or sends every referral to a human for manual processing. Now the staff is spending more time fixing the automation than they spent doing it manually. What healthcare actually needs isn't static automation. It's self-improving workflows. Instead of breaking when a new doctor appears, the system should surface it: "We found this new provider. Is this someone you want to add? Can you add their address once?" Human adds it one time. System learns context. Next referral from that doctor gets processed automatically. In healthcare, if a system breaks, patients suffer. It's not like fintech where a delayed transaction frustrates someone. This is people's health. So the biggest barrier isn't the technology or the price. It's not even implementation time. It's trust. At Entropy (YC S24), we're building self-improving workflows for referral-based clinics. Not static automation that breaks. Systems that evolve, learn and assist.

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Healthcare SaaS That Reduces Burnout — An Engineering Perspective We talk a lot about physician burnout. Long hours. Administrative overload. Endless documentation. But here’s something we don’t talk about enough: Software design plays a direct role in burnout. As engineers, we don’t see the exam room. We see workflows, schemas, APIs, dashboards. But every extra click… Every slow screen… Every poorly designed form… Eventually lands on a clinician already under pressure. Healthcare SaaS that reduces burnout doesn’t start with features. It starts with empathy. From an engineering perspective, that means: • Designing around real clinical workflows — not admin convenience • Reducing cognitive load (less clutter, smarter defaults) • Minimizing context switching between systems • Making performance a priority, not an afterthought • Automating documentation where it truly helps Speed matters. Clarity matters. Timing of information matters. If your system forces a physician to fight the interface, you’ve already failed — no matter how compliant or feature-rich it is. Good healthcare software should feel invisible. It should support decisions, not interrupt them. Technology can either amplify stress… or quietly reduce it. That choice happens at the architecture and product design level. If you're building healthcare SaaS and want to align engineering decisions with real-world clinical impact, I help teams design systems that balance usability, compliance, and scale. Because in healthcare, good architecture isn’t just technical — it’s human. #HealthcareIT #HealthTech #DigitalHealth #SaaS #UXDesign #SoftwareArchitecture

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