Hidayath Shaik Author
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“True Nausshad Alam”
“Gaurangi Singhal Good observation. The operations tier is where a lot of clinics start getting intentional, and tool choices there do matter more than people think. The interesting shift happens when scheduling stops being just a calendar function and starts feeding into patient behavior data. That's when Tier 4 decisions start having Tier 1 consequences.”
“Hamza Zulfiqar Bhatti This is the gap most AI vendors quietly avoid. It's easy to show activity; it's harder to show that a specific automation recovered X revenue or that a prediction model saved Y appointments this month. At Zavis, we built the dashboard layer precisely for this: every tool in the stack ties back to a number. No-show reduction, recall revenue, slot utilization. If your AI stack can't answer 'what did this cost and what did it return,' it's still a cost center dressed up as a solution.”
“Excited to be part of MARL Accelerator Cohort 13 🚀Looking forward to learning, building, and executing alongside everyone in the batch.”
“100% Lucia. The data layer is no longer the challenge, activation is.What we’re seeing is that clinics have access to data, but very few are turning it into real-time decisions across CRM, front desk, and patient journeys.”
“Well put Bruno Baiocchi We’re seeing that the real differentiator isn’t early adoption alone, but the ability to operationalize AI in a way that reduces complexity over time. That typically comes down to how deeply AI is embedded into core systems like EMRs rather than existing as parallel layers.That’s also why integration strategy needs to be defined upfront, not after pilots. Otherwise, scale becomes the bottleneck.”
“This is a strong point Dr Jawad Ahmad, and honestly an under-discussed layer in the whole sovereignty debate.Data ownership is only one side of it. If the clinical reasoning inside the models is shaped by unverified annotators, you’re effectively outsourcing judgment, not just infrastructure.Even with perfect data governance, that introduces a different kind of dependency. One that’s harder to audit and even harder to correct once models are deployed at scale.What you’re pointing to is critical. Sovereignty has to extend across the full stack data, models, and the human expertise shaping them. Otherwise, it’s incomplete by design.”
“MUHAMMAD IBTESAM ASIF Thank you! What excites us at Zavis is that this transformation isn't abstract. It's showing up in real numbers: fewer no-shows, fuller schedules, doctors getting time back in their day. The tech is ready - clinics that move now will have a meaningful head start.”
“Great point Shahid Mansuri and I agree, the real challenge isn’t access to AI, it’s how well we design around human behavior.From what we’ve seen, NABIDH does a strong job on standardization and data flow, but it’s mostly infrastructure-first. The experience layer is still in the hands of providers and that’s where inclusion can either happen or break down.Compliance doesn’t inherently flatten UX, but many implementations play it safe with uniform workflows.What’s working better (in our experience) is thinking of AI as an adaptive interface, not just a decision layerLetting interactions shift between voice, chat, and human seamlessly.Adjusting tone and complexity based on patient contextDesigning for “assisted journeys” instead of fully self-serve flowsWhen AI behaves more like a responsive assistant than a rigid system, you can stay compliant and inclusive.”
“Dr Prem Jagyasi Great question, this is exactly where AI creates real impact in healthcare.Personalization isn’t just about using a patient’s name, it’s about understanding intent, context, and timing. AI helps clinics do this at scale.For example: Patient segmentation Smart follow-ups Channel personalization Predictive engagementAutomated but context-aware reminders, post-treatment care tips, and check-ins based on the specific procedure. Some patients prefer WhatsApp, others calls or email, AI can adapt communication style and timing. Identify when a patient is likely to return (or drop off) and trigger proactive outreachConversational AI: Handle inquiries instantly while still feeling human, especially for international patients across time zonesThe key is not automation alone, it’s relevant, timely, and human-like interaction at scale.Clinics that get this right don’t just improve conversions, they build long-term trust.We’re seeing this firsthand with what we’re building at ZAVIS.ai, clinics that implement this don’t just improve conversions, they significantly improve patient trust and retention.”
“It is Apoorva Pandey. And it flips everything about how we measure expertise. For decades, the expert was the person who knew the most. Now the expert is the person who can live with uncertainty and make the call anyway.”
“Completely agree Jose , it’s far more nuanced than a simple pro vs anti-AI stance.”
“That’s an interesting way to frame it Showsan Binte Halim.Completely agree that without clean, reliable clinical data, scaling AI just amplifies existing inefficiencies.Data quality and governance will quietly define how far and how fast AI can go in healthcare.”
“Vinay Aggidi Haha, true! The 'old school' way still holds its own for the simple stuff. 😉”
“Well said Sunipa Roy. Post-treatment and rehab is where a lot of long-term outcomes are actually determined.”
“Mohammed Abdallah that 50.7% number from Emirates Health Services is exactly the proof point. The prediction layer is where clinics go from reactive to proactive. Most owners dont realize their PMS already has the data to predict no-shows they just dont have anything acting on it in real time. Thats the gap we keep seeing.”
“Syed Zeeshan Hassan Thank you! And you've touched on something the research kept surfacing: Aster didn't just expand, they expanded with a model that made each new clinic less risky than the last. The pharmacy flywheel, the brand recognition, the repeat patient base across locations; these created structural tailwinds that made growth self-reinforcing rather than just opportunistic. Most clinic chains that struggle with scale are adding locations without that kind of connective tissue underneath. The lesson isn't 'expand boldly.' It's 'build the system that makes expansion defensible first.'”
“Link to the full report: https://research.zavis.ai/reports/real-estate-protech/view”
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