Healthcare Technology Consulting

Most healthcare AI doesn't stall because models underperform. It stalls because infrastructure is fragmented. We are no longer constrained by algorithmic creativity. We are constrained by data silos, privacy governance, interoperability gaps, compute access, and the operational friction of translating retrospective research into prospective clinical impact. This brief examines this structural bottleneck through the Mayo Clinic Platform. The authors focus on something foundational: building an AI-ready ecosystem designed to accelerate real-world clinical research at scale. The platform provides a secure, cloud-based research environment built on de-identified, standardized EHR data from more than 15 million patients. Key capabilities include: ⭐ OMOP-aligned data models for interoperability ⭐ Structured and unstructured data ⭐ Cohort-building and schema exploration tools ⭐ Integrated workspaces with scalable CPU/GPU infrastructure ⭐ Both no-code and advanced coding environments Unlike traditional institutional repositories, Mayo Clinic Platform enables access for external researchers, supports federated multi-institutional data contributions, and embeds analytics within a privacy-preserving architecture. The paper highlights four applied studies conducted within MCP: 1️⃣ RCT emulation for heart failure drug efficacy using observational data 2️⃣ Validation of antihypertensive medications and reduced dementia risk 3️⃣ Deep learning prediction of mild cognitive impairment progression to Alzheimer’s disease 4️⃣ Neural network prediction of major adverse cardiovascular events after liver transplantation Extracting a cohort of ~15,000 patients took approximately one week. Training and running a deep learning model required roughly 10 minutes on moderate compute resources. When infrastructure friction is minimized, research velocity changes materially. Competitive advantage in healthcare AI is increasingly defined by: 💫 Data harmonization at scale 💫 Federated, privacy-preserving architectures 💫 Reproducible research pipelines 💫 Integrated compute environments 💫 Lower barriers for clinician engagement The authors also point toward multimodal expansion (notes, imaging, genomics), large-scale cross-institutional validation, and “Clinical Trials Beyond Walls” models that broaden participation and diversify real-world evidence. For those shaping AI strategy in health systems, pharma, or digital health, this paper offers a concrete example of production-grade, AI-ready infrastructure. The future of healthcare AI will not be won by isolated models. It will be won by platforms that integrate data, governance, compute, and workflow into a coherent operating system for translational impact. John Halamka, M.D., M.S. and team, great work! #HealthcareAI #HealthSystems #RealWorldEvidence #ClinicalResearch #DigitalHealth #TranslationalMedicine #PrecisionMedicine #HealthData #AIInfrastructure #MedicalInnovation

This paper delves into the potential of digital transformation in reshaping the delivery of efficient, high-quality, and secure #Healthcare. The authors highlight the immense promise digital transformation holds for the development and deployment of new care models. By integrating information, computing, communication, and connectivity technologies, digital transformation can revolutionize clinical care processes. The paper also emphasizes the potential disruptions traditional medicine might face with the entry of digital health care companies. However, it underscores the significant opportunities that arise from innovative partnerships between traditional and digital providers. 1️⃣ Digital transformation's role in healthcare: The paper emphasizes how digital transformation can significantly enhance organizational efficiencies. By leveraging technology, healthcare institutions can transform patient care models, emphasizing patient empowerment and active participation in their health journey. 2️⃣ Potential disruption in traditional #Medicine: With the rise of digital healthcare companies, traditional medical practices are at a crossroads. These digital entities are reshaping consumer expectations and putting pressure on conventional healthcare models to innovate. 3️⃣ Emerging technologies in digital healthcare: Companies in the digital healthcare space are harnessing the power of #ArtificialIntelligence, telemedicine, and blockchain electronic health records. These technologies streamline workflows, reduce errors, and ultimately lead to improved patient outcomes. 4️⃣ The promise of collaborative models: The paper suggests that there's immense potential in collaborative models between traditional and digital healthcare providers. These collaborations can span across various clinical care value-chain activities, offering a more holistic approach to patient care. 5️⃣ Use cases - Diabetes and IBD: The authors present diabetes and Inflammatory Bowel Disease (IBD) as practical examples to demonstrate the potential of digital-traditional collaborations. For instance, in diabetes care, digital tools can provide continuous feedback, medication tracking, and provider recommendations, while traditional practices offer diagnostics and routine screenings. The paper offers a comprehensive insight into the transformative potential of digital healthcare. It not only highlights the challenges faced by traditional medical practices but also presents actionable solutions through collaborative models. For anyone keen on understanding the future trajectory of healthcare, this paper provides a roadmap for harnessing the power of digital transformation. 🌐⇢ https://lnkd.in/epr_q3YS ✍🏻 Jon O. Ebbert, MD, Rita G. Khan, MBA, Bradley C. Leibovich, MD. Mayo Clinic Proceedings: Digital Health. Published:March 25, 2023. DOI: 10.1016/j.mcpdig.2023.02.006

Your HealthTech startup isn’t a tech company. Treating it like one can be fatal. I’ve watched brilliant founders from SaaS, fintech, and AI stumble in healthcare. Not because they lacked skill, but because they assumed healthcare works like every other industry. It doesn’t. Here’s what makes HealthTech a world of its own: 1. You’re selling to institutions, not individuals. Hospitals, insurers, and regulators move carefully, not quickly. Procurement in large systems can take 18+ months, with decisions driven by risk and compliance over hype. Committees replace single decision-makers, and the biggest competitor is often the status quo. 2. Trust is everything. In healthcare, one misstep - clinical, ethical, or regulatory - can destroy credibility overnight. I’ve seen startups lose traction after minor compliance lapses. The rules around AI and digital health evolve constantly, and staying ahead of regulation is now a core competency, not a checkbox. 3. Adoption is the hardest challenge. Clinicians spend roughly 40% of their day on admin tasks. Patients are already overloaded. If your product doesn’t fit seamlessly into existing workflows, it won’t get used... no matter how elegant the tech. True adoption takes empathy, support, and time. 4. Solve a mission-critical problem. In healthcare, survival depends on necessity, not novelty. “Nice-to-have” tools don’t last. Clinical validation through peer-reviewed studies and real-world evidence matters more than hype. Evidence earns trust—and trust drives growth. 5. Investors now expect proof of outcomes. Funding is shifting toward startups that demonstrate measurable clinical impact and sustainable revenue models, especially in high-need areas like maternal health and chronic disease management. Impact now trumps velocity. 6. Partnerships power growth. Strategic collaborations, like those between pharmaceutical companies and AI imaging startups, are shaping healthcare innovation. They help new entrants navigate regulation, gain credibility, and scale responsibly. 7. Play the long game. Healthcare rewards patience, resilience, and humility. Quick hacks and blitz-scaling don’t work here. The founders who listen, learn, and adapt to the system’s realities are the ones who thrive. HealthTech is healthcare. With just enough technology to make it work better, not worse. What would you add?

As Chief Medical Officer at GE HealthCare, my primary responsibility is to lead the medical function grounding our innovations in clinical evidence, ensuring efficacy, and bringing the voice of the clinician into every strategic decision we make. But there’s another element to this role that’s less visible yet deeply impactful: marketing. While I don’t manage marketing directly, I collaborate with our marketing teams more than one might expect from a physician by training. Why? Because in healthcare, clinical credibility and commercial clarity must go hand in hand. Here are the marketing elements I find most critical: 1. Storytelling with substance Clinicians don’t respond to hype, they respond to evidence. But evidence needs a compelling narrative. I work with marketing to ensure our stories are rooted in data, but framed in a way that communicates real-world value to providers, health systems, and patients alike. 2. Segmentation that reflects reality Understanding our clinical stakeholders - radiologists, cardiologists, oncologists, technologists, hospital executives - is essential. Marketing helps us tailor messaging by audience, while I help ensure those audience profiles reflect real clinical behaviors and challenges. 3. Positioning built on outcomes It’s not enough to say a product is innovative; we must demonstrate how it improves outcomes. The medical team contributes the data, the trials, the insights. Marketing shapes that into positioning that resonates across markets, languages, and care settings. 4. Credibility through collaboration Thought leadership is a shared responsibility. Whether we’re preparing for a major conference or publishing peer-reviewed studies, marketing helps amplify the work of our clinical experts. Together, we balance scientific rigor with accessible communication. 5. Listening as a strategy Much of marketing is about listening to the market. Much of medicine is about listening to the patient. At this intersection, I find some of the most valuable insights. Marketing teams surface unmet needs, competitive dynamics, and shifting expectations. My role is to interpret those through a clinical lens and help turn them into better solutions. In short: I don’t “do” marketing, but I can’t do my job without it. Healthcare is evolving rapidly. The Chief Medical Officer-role must evolve with it bridging clinical insight and market relevance, ensuring that what we build is not only scientifically sound, but also meaningfully communicated to the people who need it most. Would love to hear how others in clinical or marketing roles navigate this balance. #healthcare #radiology #marketing #digitalhealth

At Rackspace, we published new research on healthcare cloud adoption based on a survey of 325 IT and digital leaders. Many organizations are moving to the cloud, but their environments are not yet ready to support AI or recover quickly from a cyber event. That is what we're hearing from leaders across the US, UK and Europe. Only 15% of organizations surveyed qualify as Cloud Leaders, with cloud fully integrated into their business strategy. And it shows in outcomes. Among those leaders, 58% report mature or well-managed AI strategies, compared to just 24% of others. At the same time, more than 70% of organizations are still in the early or fragmented stages of AI adoption, even as 44% are already seeing operational efficiency gains and 41% report reduced clinician workload. You see this gap in execution, especially when patient data needs to stay protected and critical systems need to stay online. The mistake I see most often is this. Cloud gets treated like a migration milestone instead of an operating model. Workloads move, but security, data, and recovery are managed separately. That slows recovery, increases risk, and makes it very difficult to scale AI. It is not a cloud problem. It is how the environment is run. Only 4% of organizations are extremely confident in their ability to protect patient data, and just 6% have fully integrated cyber resilience. At the same time, 41% cite security risk as the biggest barrier to AI, with another 38% pointing to data quality and interoperability. You see it quickly. Recovery plans do not match where data lives. AI models cannot rely on consistent, governed data. Compliance becomes a checkpoint instead of part of daily operations. The teams that get this right do one thing differently. They run security, data, and recovery as one system, not separate priorities. That is what allows them to move faster on AI while staying resilient. You can see this with Central North West London NHS Trust (CNWL). Working with their team, we helped rethink how the environment was operated, strengthening resilience across critical services while maintaining control over sensitive patient data. Start here. Pick one critical workload and map three things: how it is secured, where the data lives, and how it would be recovered in a cyber event. If those do not line up, that is where to focus first. If you want to go deeper, we pulled the full research together here: https://lnkd.in/ezzKbStZ

After reviewing multiple medical device submissions over the past 5 years, I've found that most failed applications shared common, preventable mistakes I've helped MedTech companies navigate the regulatory maze, cutting average time-to-market by 7 months and saving tons in remediation costs per client 𝗛𝗲𝗿𝗲 𝗮𝗿𝗲 𝘁𝗵𝗲 𝟳 𝗺𝗼𝘀𝘁 𝗰𝗼𝗺𝗺𝗼𝗻 𝗿𝗲𝗮𝘀𝗼𝗻𝘀 𝘆𝗼𝘂𝗿 𝗺𝗲𝗱𝗶𝗰𝗮𝗹 𝗱𝗲𝘃𝗶𝗰𝗲 𝘄𝗶𝗹𝗹 𝗳𝗮𝗶𝗹 𝗿𝗲𝗴𝘂𝗹𝗮𝘁𝗼𝗿𝘆 𝘀𝗰𝗿𝘂𝘁𝗶𝗻𝘆: 1. **Inadequate Risk Management** • Risk files don't align with ISO 14971:2019 requirements • Missing traceability between hazards and mitigations • Failure to update risk assessments after design changes (seen in a large portion of rejections) 2. **Poor Design Control Documentation** • Incomplete Design History Files with gaps in verification records • User needs not properly translated to design inputs • Design outputs that don't satisfy acceptance criteria 3. **Insufficient Clinical Evidence** • Relying on literature alone when equivalence can't be established • Underpowered clinical studies (average n=24 when n=68+ was needed) • Missing patient subpopulation analyses required by regulators 4. **Software Documentation Gaps** • IEC 62304 compliance issues, especially for Class B and C software • Inadequate cybersecurity risk assessments (flagged in 90%+ of connected devices) • Missing or incomplete software validation protocols 5. **Usability Engineering Failures** • Formative studies conducted too late in development • Use-related risk analysis disconnected from overall risk management • Summative testing that doesn't represent actual use environments 6. **Supply Chain Vulnerabilities** • Critical component suppliers without adequate quality agreements • Missing or insufficient supplier reviews/audits • Incomplete component specifications leading to inconsistent performance 7. **Post-Market Surveillance Planning** • Reactive rather than proactive monitoring strategies • PMCF/PMS plans that don't address residual risks • Inadequate complaint handling procedures (cited in 62% of MDR submissions) 𝗧𝗔𝗞𝗘𝗔𝗪𝗔𝗬: The most successful medical device companies build quality and regulatory strategy into their development process from day one, not as an afterthought. I've seen startups save millions and many months by investing in proper QMS and regulatory planning early The harsh reality? Most MedTech founders underestimate regulatory requirements until they're facing rejection. Don't be one of them! Want to avoid these pitfalls? My calendar is open for the next two weeks for 30-minute strategy calls with serious MedTech leaders, if you are looking for shortcuts and false claims of accelerated deadlines, then I am not the right client for you but if you want evidence backed timelines and proven experience, get in touch!

#Nationwide Employer Healthcare Strategy. Self-Funded nationwide employers are facing employee health plan budget problems. Healthcare costs are running unexpectedly high. These high healthcare costs are being driven by High Cost Claimants... the 5% of health plan members with high costs that drive 50% of overall health plan spending. Here are 5 #Strategies for Employers to Lower High Claimant Healthcare Costs: 1) #Network: Switch carriers to the only 1 out of the 4 major insurance carriers that has decent contracts with major hospital systems. 2) #ClaimsData: Get your claims data including allowed amount (and preferably Billed Charges, Provider NPI number and Provider Tax ID Number). Put your carrier out for RFP if necessary and include this data requirement in your RFP. 3) Engage #HighCost Claimants: Use the claims data to identify and assist existing high cost claimants and predict and prevent the most probable future high cost claimants. Use age greater than 50 as an initial screen for these potential high cost claimants. 4) Address Fraud, Waste and Abuse (#FWA): Use your claims data to identify fraudulent claims and prevent future payments to that same provider equal to the amount of the fraud. 5) #PBM: Carve-out your PBM to a transparent, pass-through PBM that DOES NOT require you to fill your specialty pharmacy medications through the mail order specialty pharmacy that they own. #EmployeeBenefits #HealthInsurance #Healthcare

Building AI for healthcare is the straightforward part. Governing it after deployment is where the real challenge begins. The Medicines and Healthcare products Regulatory Agency published a piece by Jennifer Dixon, CEO of The Health Foundation. It gets to the heart of why AI regulation in medicine is fundamentally different from any other technology domain. The MHRA is strengthening its approach to regulating adaptive AI, enhancing both pre-market evaluation and robust post-market surveillance. Furthermore, ensuring that safety, performance and equity remain central as technologies evolve in real-world settings. The key word there is adaptive. Most regulatory frameworks were designed for static products. A device that works a certain way when approved continues to work that way. AI systems learn, shift, and adapt in production. The National Commission for the Regulation of AI in Healthcare is addressing questions such as whether the AI model is safe and accurate. They're also looking at whether it continues to be safe as it adapts in a real world setting. That second question is the one nobody has fully solved yet. Jennifer Dixon raises questions beyond technical performance, including whether an AI application is usable as intended and acceptable to both clinicians and patients. She also highlights concerns around fairness, as well as the risk of unsafe or biased workarounds emerging in practice. This matters enormously for public sector AI deployment beyond healthcare. Every government AI system deployed in a regulated environment faces the same fundamental tension. The approval was granted for a system at a point in time. The system continues to evolve, learn, and behave differently from what was originally validated. The governance frameworks being built for healthcare AI will become the template for every high stakes AI deployment across government. Getting this right in the NHS will shape how AI is governed in justice, welfare, and national security for the next decade. The challenge with AI is not building it. It is governing it in production when the world it operates in keeps changing. A great piece worth reading from Dame Jennifer Dixon linked below. How is your organisation approaching the governance of AI systems that evolve after deployment?

In today's healthcare the real problem isn’t a lack of tech. It’s a lack of connection. Patients want the same smooth experience they get everywhere else. But most hospitals still run on old, clunky systems. The result is friction at every step — from booking to follow-up. Here’s how we’re changing that in my hospital. We mapped the entire patient journey. Not just one app. Not just one tool. The whole experience. This is what we found: • Pre-arrival: Online booking and digital triage cut confusion and save time. • Check-in: Mobile check-in and digital forms end the paperwork shuffle. • During care: Patients get real-time results and can message their care team securely. • Follow-up: Digital discharge, reminders, and tele-reviews keep care going at home. The impact is clear. Digital appointment systems push satisfaction above 90%. No-shows drop. Clinic flow improves. Patients feel informed, prepared, and in control. But here’s the key: Tech should amplify the human touch, not replace it. A single app is not enough. You need a journey map to spot the “moments that matter.” That’s where you find the friction — and fix it. My advice to leaders: • Start with the journey, not the tool. • Cut friction with care. • Build digital pathways that boost empathy and connection. When you redesign the journey, you restore dignity to every patient. This is the future of healthcare. Simple. Human. Connected.