Delta: HealthTech Innovators

Roupen Odabashian

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Welcome to 'Delta', a podcast where we delve deep into the world of healthcare transformation. Join us as we speak with Health Tech innovators, leading researchers, forward-thinking engineers, and passionate individuals dedicated to reshaping the healthcare landscape. If you're curious about the future of healthcare and those spearheading positive change, 'Delta' is your essential listen.

4D AGO

AI-Powered Residency Screening: How RankRX Uses LLMs to Fix Unfair Application Filtering

MalkeAsaad, plastic surgery resident and founder of Rank RX, shares how he built an AI platform using large language models to revolutionize residency application screening. From med school in war-torn Aleppo to Mayo Clinic and MD Anderson, Malke discusses the unfair filtering system that inspired Rank RX—where Nobel Prize laureates get rejected for missing a cutoff by one point—and how AI can make hiring more objective and efficient. What you'll learn: - Why the current residency application system is broken (Nobel Prize winner rejected for 1-point score gap) - How Rank RX uses AI/LLMs to screen 1,000–2,000 applications (30–80 pages each) in minutes - Building a tech team as a physician entrepreneur without coding background - Customer acquisition strategies for healthcare startups (networking, ads, vendor screening) - Market validation: assessing if your solution solves a real problem people will pay for Timestamps - 0:00 – Unfair residency filtering: Nobel Prize winner rejected for 1-point gap - 1:14 – Malke Assad’s journey: From Aleppo to leading U.S. institutions - 3:45 – Rank RX: How AI/LLMs bring objectivity to application screening - 4:21 – How it works: Custom scoring and program-driven selection criteria - 8:36 – Real-world usage: Positive feedback and automated recommendation letter analysis - 10:32 – Building a tech team without a coding background - 17:35 – Key advice for physician entrepreneurs: Turning ideas into scalable companies - RankRX Website: https://www.rank-rx.com/ - Malke Assad LinkedIn: https://www.linkedin.com/in/malke-asaad-43b908177 - The Match Guy Website: https://thematchguy.thinkific.com

23 min
NOV 2

From FDA Clearance to 1 Billion Views: How This Medical Device Startup Went Viral

When Sahil and his brother started Otoset in their mid-20s, they had no idea their FDA-cleared ear cleaning device would generate over 1 billion social media views and force them to completely pivot their business model. In this episode, Sahil shares the unexpected journey from building a B2B medical device company to creating "the front door to ear care", a direct-to-consumer healthcare network serving 40 million Americans with chronic ear wax issues. 🔑 KEY TAKEAWAYS: → How they became some of the youngest founders to get FDA 510(k) clearance → The unexpected social media virality that changed everything → Why they pivoted from partner clinics to owning their own locations → Marketing strategies: organic content, influencers, and patient education → The critical role of FDA clearance as a competitive differentiator → Building credibility before scaling consumer marketing → Finding the right mentors in healthcare entrepreneurship 💡 WHO THIS IS FOR: ✓ HealthTech & MedTech founders navigating FDA pathways ✓ Startups exploring direct-to-consumer healthcare models ✓ Entrepreneurs learning to leverage social media for medical products ✓ Anyone interested in the consumerization of healthcare 📊 BY THE NUMBERS: - 40 million Americans affected by ear wax buildup - 1 billion+ views across social media - $99 cash-pay model (first treatment) - 20-30 patients/day in company-owned clinics - Expanding to 50+ major metros Timestamps: 00:00 - Introduction: The Brother's Ear Wax Problem 01:21 - What is Otoset? The First FDA-Cleared Ear Cleaning Device 03:22 - Why FDA Clearance Matters & How They Got It 06:56 - The Unexpected Social Media Explosion 08:55 - The Strategic Pivot: B2B Device to D2C Healthcare Network 11:36 - Business Model: $99 Cash-Pay & Building Owned Clinics 15:18 - Beyond Ear Care: Hearing Health & Expansion Plans 17:15 - Marketing Strategy: Organic, Influencers & Patient Education 20:33 - Building Credibility Before Scaling Consumer Marketing 22:35 - Biggest Lesson: Find Healthcare Entrepreneur Mentors Early 24:33 - Final Thoughts & Key Takeaways 📌 Key Resources & Links 🔗 Otoset Website: https://otoset.com/ 🔗 Otoset Linkedin: https://www.linkedin.com/company/visitallears/ 🏥 Find a Certified Clinic: https://otoset.com/pages/find-clinic 💼 Connect with Sahil: https://www.linkedin.com/in/sahildiwan/

25 min
OCT 6

AI in Medicine is BROKEN: Stanford PhD Exposes the 95% Accuracy Lie | LLMs in Healthcare

Is AI really ready to replace doctors? Stanford PhD researcher Suana reveals shocking truths about medical AI that Big Tech doesn't want you to know. When she tested leading AI models like GPT-4, Claude, and DeepSeek on modified medical questions, their accuracy plummeted by up to 40%!In this eye-opening conversation, we dive deep into: ❌ Why 95%+ accuracy on medical exams means nothing in real clinical practice ❌ How AI models fail when there's "no right answer" (which happens constantly in medicine) ❌ The dangerous gap between flashy headlines and clinical reality ✅ How doctors can safely use AI as a co-pilot (not replacement) ✅ The future of medical AI evaluation and what needs to changeSuana is a 3rd-year PhD student at Stanford in Biomedical Data Science, pioneering real-world evaluation methods for medical AI. Her research on MedELM and benchmarking is reshaping how we think about AI deployment in healthcare.🔬 Key Research Discussed: JAMA Open publication on AI robustness in medical diagnosis MedELM: 35-dataset benchmark suite for real clinical tasks Why MedQA and USMLE-style tests don't reflect actual patient care ⚠️ CRITICAL TAKEAWAY: AI models are trained to always give an answer, even when "none of the above" is correct—a potentially dangerous flaw in medical decision-making.📚 Resources Mentioned: MedELM Leaderboard (public repository available) Research on medical AI evaluation standards Real-world hospital deployment considerations Timestamps: 0:00 - Introduction: Why Medical AI Evaluation is Broken 1:04 - Suana's Journey: From Computer Science to Healthcare AI 2:32 - The 3 Critical Problems with Current AI Benchmarks 8:28 - The Research: Testing AI with "None of the Above" 17:24 - Shocking Results: AI Accuracy Drops 8-40% 19:02 - Why AI Can't Say "I Don't Know" 23:10 - Take-Home Message: Use AI as Co-Pilot, Not Replacement 24:58 - Real Clinical Examples: When AI Actually Helps 28:12 - MedELM: The Future of Medical AI Evaluation 34:35 - Final Advice for Doctors, Patients & Developers Whether you're a physician, healthcare worker, AI developer, or patient curious about medical AI, this conversation will change how you think about artificial intelligence in healthcare. Paper link: https://jamanetwork.com/journals/jamanetworkopen/fullarticle/2837372

36 min
SEP 22

From $60K/Month Revenue Recovery to YC Success: How Ember is Fixing Healthcare's $10B Fraud Problem

Healthcare revenue integrity is broken, and Charlene, CEO of Ember, is fixing it with AI. In this episode, she reveals how hospitals lose millions due to inefficient billing processes and how her platform helps recover 51% of initially denied claims. From her background at Google Health to building a YC-backed startup that's generating over $60,000 per month in additional revenue for clients, Charlene shares the secrets behind focusing on one specific problem and executing flawlessly. Key topics covered: Healthcare revenue cycle management, AI in medical billing, startup focus strategies, building in regulated industries, and the future of healthcare fraud detection. Perfect for healthcare executives, CFOs, startup founders, and anyone interested in healthtech innovation. Timestamps 0:00 Introduction & Ember Overview 1:05 What is Revenue Integrity in Healthcare? 2:31 Ambient Listening & Clinical Documentation 4:10 Product Implementation & Integration Process 6:02 Real Results: $60K+ Monthly Revenue Recovery 8:33 Technical Deep Dive: How Ember Works 10:28 Charlene's Background & Healthcare Industry Insights 12:53 The $10B Healthcare Fraud Problem 16:17 Y Combinator Experience & Startup Focus 18:22 Building the Right Team (3-Year Journey) 21:44 Future Vision for Ember 22:41 Advice for Healthcare Entrepreneurs

24 min
SEP 8

How AI Turns Messy EHR Into Clear Survival Predictions

Can AI forecast ICU risk from the first 36 hours of EHR data? University of Washington researcher Sihan explains TrajSurv, a survival-prediction model that converts noisy, irregular ICU time series into interpretable latent trajectories using Neural Controlled Differential Equations (NCDEs) and time-aware contrastive learning aligned to SOFA. We cover how trajectories outperform snapshots, handle missingness without heavy imputation, and remain clinically legible via vector-field feature importance and trajectory clustering. Validated on MIMIC-III and eICU with reported C-index ≈0.80 and cross-cohort ≈0.76, TrajSurv points to safer escalation, de-escalation, and bed allocation in the ICU. In this episode: survival prediction basics; limits of Cox/RSF vs deep time-series models; NCDE explained in plain language; first-36h feature set (53 labs/vitals/demographics); metrics (C-index, Brier, dynamic AUC); interpretable clustering linked to outcomes; and what’s next—adding interventions for counterfactual simulation and extending to oncology. Link to the paper: https://arxiv.org/abs/2508.00657 Timestamps 00:00 Why trajectories beat snapshots in EHR 01:00 Guest intro: Sihan, UW Biomedical Informatics 01:40 Survival prediction 101 and clinical use 03:40 From Cox/RSF to deep learning on time-varying data 05:03 What is TrajSurv (pronounced “traj-surf”)? 06:16 NCDE explained with the “ship + weather” analogy 08:14 Handling irregular sampling and missing data 09:14 Time-aware contrastive learning aligned to SOFA 10:47 Datasets: MIMIC-III and eICU; first 36h features (labs, vitals, demo) 12:40 Results: C-index ≈0.80; cross-cohort ≈0.76; interpretability 14:30 Workflow: CDS, monitoring, escalation, de-escalation 16:15 Why humans miss multi-variable long-horizon trends 18:21 Latent trajectory clustering and survival differences 23:18 Next: interventions, counterfactuals, oncology applications 25:40 Closing Roupen Odabashian LinkedIn: https://www.linkedin.com/in/roupen-odabashian-md-frcpc-abim-183aaa142/ Sihang Zeng: https://www.linkedin.com/in/zengsh/ #HealthcareAI #ClinicalDecisionSupport #EHR #ICU #SurvivalAnalysis #DeepLearning #NCDE #MIMICIII #eICU #SOFA

26 min
AUG 25

How AI Fixes Medical Record Errors | $125B Healthcare Problem Solved

Medical documentation errors cost U.S. hospitals over $70 billion in denied claims and $55 billion in lawsuits every year. In this episode, we sit down with Dimitri, Founder & CEO of WorkDone Health, a Y Combinator-backed startup that’s building the "Grammarly for medical records." WorkDone Health automates chart review, compliance checks, and billing validation in real-time, preventing errors before they cost hospitals money—or compromise patient safety. We explore: Why CFOs are the first to feel the pain of documentation errors How AI-powered compliance and quality checks reduce denials Lessons from Y Combinator and scaling a healthcare startup Why WorkDone could be the antidote to insurance AI denials If you’re a healthcare leader, investor, or builder in healthtech, this episode shows the future of clinical documentation. Timestamps: 0:00 – Intro: The cost of documentation errors ($70B in denials, $55B lawsuits) 1:00 – Dimitri’s journey: From physics to healthcare AI 3:00 – The problem: Reactive vs. proactive documentation review 5:15 – Real-world example: Left vs. right shoulder conflict 7:00 – Sepsis bundle case study 9:00 – How WorkDone Health prevents denials in real time 12:00 – Impact on CFO metrics: denials, lawsuits, billing cycle 14:30 – The “antidote” to insurance AI claim denials 18:00 – How the tool works: real-time vs. batch checks 22:00 – Prioritization of alerts: reducing physician burden 27:00 – Vision: “Grammarly for medical documentation” 29:00 – Lessons from Y Combinator for healthtech startups 32:00 – HIPAA compliance and why it matters from Day 1 37:00 – Future of WorkDone: API integrations with EMRs Dimtry Karpov: https://www.linkedin.com/in/dmitrykarpov/ WorkDone: https://www.linkedin.com/company/workdonehealth/ WorkDone: https://www.wrkdn.com/ Roupen Odabashian: https://www.linkedin.com/in/roupen-odabashian-md-frcpc-abim-183aaa142/

39 min
JUL 27

Fixing the $1 Trillion Healthcare Bottleneck with AI

In this episode, we sit down with Chuck Feerick, founder and CEO of Latitude Health, a MedTech startup tackling one of the most overlooked — yet critically expensive — problems in healthcare: prior authorization and utilization management. Chuck shares how a personal experience in his early 20s inspired him to transform the way health plans make care decisions, using AI to reduce administrative burdens and accelerate patient access to treatment. With a background spanning health plan operations, venture capital, and startups, Chuck brings a 360° perspective on what it really takes to build a successful health tech company. We dive into: The $1 trillion administrative crisis in U.S. healthcareWhy prior authorization delays hurt patients and providersHow Latitude Health uses AI to empower—not replace—cliniciansThe real challenges of selling to health plansWhat every health tech founder must understand about procurement, ROI, and building painkiller productsThe future of AI in care decision-makingWhether you're a health tech entrepreneur, investor, or healthcare executive, this conversation is full of practical insights on solving big, unsexy problems with massive impact. 00:00 – Intro: Can AI Fix Healthcare? 01:03 – Meet Chuck Feerick, Founder of Latitude Health 02:15 – A Personal Story That Sparked a HealthTech Mission 04:10 – The Broken Prior Authorization Process Explained 06:32 – Automating Utilization Management with AI 08:44 – What Latitude Health Actually Does 10:05 – How Patients, Providers & Payers Benefit 12:20 – Chuck’s Journey: Operator, Investor, Founder 14:15 – Lessons from VC for Startup Fundraising in MedTech 16:01 – How to Sell to Payers: Complex Sales in Healthcare 18:30 – The Role of AI vs. Human in Clinical Decision Making 21:04 – How Latitude Uses LLMs to Structure Medical Data 23:19 – Training AI with Clinicians: Nurses, Doctors, CMO Input 25:12 – Building a HealthTech Startup the Right Way 27:00 – Tackling Long Sales Cycles in Healthcare 28:42 – AI is Moving Fast — Building for Flexibility 30:18 – The Unsexy Problem That Needed Solving 33:07 – Why Utilization Management Is the Key to Controlling Costs 35:45 – Administrative Waste: The $1 Trillion Opportunity 37:02 – Why Latitude Focuses on High-Impact Painkiller Tools 38:49 – Consumers, Behavior, and the ROI of Innovation 41:00 – Closing Thoughts: What Founders Must Understand About Healthcare Roupen Odabashian: LinkedIn: https://www.linkedin.com/in/roupen-odabashian-183aaa142/ X: https://twitter.com/RoupenMD Email: roupen@deltahealth.tech Tigran (Tiko) Bdoyan: LinkedIn: https://www.linkedin.com/in/chuckfeerick Watch Our Podcast at: https://youtu.be/Xj89GFyPpxw #MedicalStartup #Telehealth #SimulatedPatients #Fundraising #MedicalSchoolTools #CME #AIinMedicalEducation #CasperExam

22 min
JUL 2

Revolutionizing MedTech: How SimAI Is Changing Medical Education Forever

In this episode, we dive deep into the future of MedTech and HealthTech innovation with Tikran Bdoyan, co-founder of SimAI, an AI-driven platform transforming medical education through realistic virtual patients. Learn how SimAI is: Reducing training bottlenecks in healthcareAccelerating student evaluation and feedbackSupporting medical schools, residency programs, and CME globallyHelping international students and telehealth teams scale their trainingWe also explore: SimAI’s journey through Y CombinatorFundraising in hard-to-crack spaces like healthcare and edtechThe growing role of AI in clinical education and patient simulationTimestamps: 00:00 – Intro: Why MedTech Needs Disruption 01:07 – Meet Tikran Bdoyan, Co-Founder of SimAI 02:22 – The Problem in Medical Education Today 04:11 – What is SimAI? AI Patients Explained 06:03 – From Reddit Post to Startup Breakthrough 07:36 – The Global Demand for AI in Clinical Training 09:15 – Why Medical Exams Are Outdated 11:27 – Real-Life Benefits of SimAI for Students & Professionals 13:35 – Getting into Y Combinator: SimAI’s Journey 15:40 – The Power of Focus and Realistic Expectations 18:01 – Why Healthcare Sales Cycles Are So Slow 19:38 – Customizing AI Patients for Schools 21:25 – Instructor Tools & Performance Insights 23:16 – Use Cases: Residency, CME & Telehealth 25:12 – Fundraising in MedTech & EdTech: The Challenges 27:06 – Finding Product-Market Fit in Counseling 28:55 – SimAI’s Global TAM: US, India, Canada, IMGs 31:00 – New Trends: AI-Augmented Practitioners 32:54 – The Future of AI in Medical Education (10-Year Outlook) 34:51 – Standardizing Bedside Manner Evaluation 36:27 – Cost & Limitations of Simulated Patients 38:19 – What Tikran Wishes He Knew Before Starting 40:00 – Final Advice: Do More, Compete Less Roupen Odabashian: LinkedIn: https://www.linkedin.com/in/roupen-odabashian-183aaa142/ X: https://twitter.com/RoupenMD Email: roupen@deltahealth.tech Tigran (Tiko) Bdoyan: LinkedIn: https://www.linkedin.com/in/tigran-bdoyan/ Watch Our Podcast at https://youtu.be/7rmuSgTqkYw #MedicalStartup #Telehealth #SimulatedPatients #Fundraising #MedicalSchoolTools #CME #AIinMedicalEducation #CasperExam

26 min

Welcome to 'Delta', a podcast where we delve deep into the world of healthcare transformation. Join us as we speak with Health Tech innovators, leading researchers, forward-thinking engineers, and passionate individuals dedicated to reshaping the healthcare landscape. If you're curious about the future of healthcare and those spearheading positive change, 'Delta' is your essential listen.

Creator

Roupen Odabashian
Years Active

2023 - 2025
Episodes

52
Rating

Clean
Copyright

© Delta: HealthTech Innovators
Show Website

Delta: HealthTech Innovators