Kate McGinley

🎯 Bridging Product Development and Market Needs in Health Tech Transitioning TunedCare from a D2C to B2B model taught me some (ahem) hard lessons about aligning product capabilities with market demands. In building scalable workflows to handle eligibility files, claims capabilities, and population health data, we contracted for 1.4M+ lives in under a year and secured major channel partnerships with NFP, USI, and Nomi. What stood out most? Success comes when you listen closely to clients, prioritize operational execution, and push for alignment across product, sales, and marketing teams—even when it's uncomfortable. I’d love to hear from founders and operators: What’s been your biggest challenge in ensuring your product truly meets market needs? #healthtech #leadership #gotomarketstrategy #healthcareinnovation #growthleadership

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The evolving and dynamic nature of AI/ML applications requires a fully integrated total product lifecycle management system.

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This isn't a post about #AI, but about data foundation and #interoperability in #healthcare. The demonstration of AI through #LLM is not sexiest part of this presentation. There's no silver bullet, we should know that at this point. Instead, I draw your attention to the rich discussion around the applicability of #OpenEHR and #HL7 #FHIR standards, and the importance of understanding why each of these #standards was developed and what for. Data definitions, data schemas, and how we apply standards to structure and represent information, both in storage and transportation, are key. Any further comments from me would be spoilers. I hope you enjoy the presentation and share your point of view, folks. Have a great week!

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Key trends and takeaways from HLTH 2024: 1. AI's Evolving Role: AI dominated discussions, with executives acknowledging its potential while also recognizing challenges in accuracy and clinician trust. There's a shift towards focusing on the practical applications of AI, with a call for partnerships to ensure reliable testing and implementation. 2. Infrastructure Focus: Beyond the glitzy AI tools, there's a growing emphasis on the "picks and shovels" needed for healthcare data accessibility, including infrastructure, compliance, and interoperability. Executives believe that improving data flow will facilitate the effective use of AI. 3. Middlemen Under Scrutiny: Pharmacy benefits managers (PBMs) faced criticism for their role in rising drug costs, with discussions highlighting misaligned incentives and the need for transparency in healthcare pricing. 4. Women's Health Spotlight: Women's health initiatives gained attention, particularly following government funding announcements and personal stories shared by advocates. However, investment in women's health remains minimal compared to overall healthcare funding. 5. Interest in Psychedelic Medicine: The potential of psychedelics for treating mental health conditions garnered significant interest, with discussions about funding and research moving forward, especially within the Veterans Affairs system. Executive Takeaways: Leaders from various sectors emphasized the importance of consumer-focused strategies, collaboration, and the need for responsible AI usage. There was a consensus on the critical role of addressing healthcare accessibility, improving patient experiences, and fostering innovations that genuinely enhance care delivery.

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Great insight by my colleague David Devine 🚀 Transforming Healthcare with Data Analytics 🚀 The North American healthcare analytics market is projected to soar to $11.4 billion by 2023, up from $3.13 billion in 2018. Despite this growth, many healthcare providers are still catching up with the advancements seen in other sectors, such as medical devices and payers. Key Highlights: Real-Time Decision Making: Leveraging basic patient data to make immediate care decisions. Predictive Analytics: Using machine learning to predict and reduce hospital readmissions, saving billions annually. Combating the Opioid Crisis: Data tools are crucial in monitoring e-prescribing practices and integrating with state prescription drug monitoring programs. Future Strategies: Moving beyond traditional data sets to include less structured data, such as genomic and social data, for a comprehensive analysis. Healthcare leaders must think, plan, and act differently to harness the full potential of data analytics. By advancing data strategies and focusing on both structured and unstructured data, we can transform patient care and outcomes. Let’s embrace the power of data to create a more efficient, patient-centric healthcare system! 🌟 https://lnkd.in/ebu35PAh #Healthcare #DataAnalytics #MachineLearning #AI #BigData #HealthTech

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How many healthcare use cases will (or should) require 100% accuracy to be trustworthy and effective for patient care? Just as we’ve seen with data science initiatives over the years, this is a tricky balance that will never be perfect and never be 100%. We need to set those expectations with physicians, administrators, staff, and patients. And that unattainable goal of perfection shouldn’t be the barrier to using #AI in #healthcare. #healthcareai #genai

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • athenahealth released Ambient Notes, a fully integrated AI ambient scribe for ambulatory practices, which also supports switching between AI models based on context and individual preference. • Abridge is partnering with OpenNotes to evaluate AI-generated visit summaries. 📞 AI Contact Center • healow health released Genie, a phone-, text-, and chat-based AI conversational chat solution for appointment scheduling, prescription refills, and more. 🏥 AI Facility Management • Kontakt.io released a security app that allows staff to page security personnel with location information, and a Kio Nurse app for locating items with a live map view. 📈 Data & Analytics • Alameda County selected Innovaccer to unify and analyze the data in its Social Health Information Exchange.    • Cornerstone AI announced an additional $5M investment, bringing its total funding to $10M. • emtelligent released their Clinical Workflows solution, which offers both clinical and business users across healthcare organizations an interface experience to interact with their existing LLM and NLP engine. 💼 Provider & Practice Administration • Axuall announced two new health system partnerships—VA-based Inova Health System and IN-based Community Health Network—and a partnership with PayrHealth, which helps providers negotiate contracts and reimbursements. 🔁 RCM • SmarterDx released an AI denials solution that creates AI-generated appeals letters.    • Inovalon hosted its Empower 2024 summit and announced a host of new solutions and updates, including an eligibility verification solution for provider organizations and several other tools for health plans and research organizations. • XiFin, Inc. added two new payers to its Payor Rate Transparency Monitor—Humana and Blue Shield of California—and launched Payor Intelligence Hub to help providers better understand payer policies and behaviors. ⚙️ Clinical Operations • Oracle hosted its Oracle Health Summit and announced a number of new features and product updates, including an AI-native EHR, patient admin tools, and plans to become a QHIN. • eClinicalWorks hosted its National Conference and released AI RCM tools, VBC scorecards, and patient engagement updates. • Altera Digital Health released a new EHR, Paragon Denali—natively deployed in Microsoft Azure —intended for rural, critical access, and community hospitals. 🤝 Staffing, Consults, and Referrals: • Tennr raised a $37M Series B. • Cottage Hospital partnered with ShiftMed to add more flexible per diem options for acute care staff. 🧑‍💻 Virtual & At-Home Care • Brook Health is partnering with Linus Health to provide treatment plans and ongoing remote clinical care for cognitive care patients. --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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👉🏼 ChatGPT's Accuracy in Triage of Nephrology Patient Communications 🤓 Justin H Pham 👇🏻 https://lnkd.in/eYkqgU4h 🔍 Focus on data insights: - 📈 In the first trial, ChatGPT correctly triaged 93% of messages, demonstrating strong reliability. - 🔍 The AI system maintained consistency across trials, with a similar accuracy rate in both assessments. - ⚖️ Internal agreement between ChatGPT's responses was high, indicating robust performance and minimal variability in results. 💡 Main outcomes and implications: - 🤖 High accuracy in triaging suggests that AI could be effectively integrated into outpatient settings to streamline patient communication. - ⏱️ Potential reduction in response times for patient messages, leading to improved patient care and operational efficiency. - 🏥 Encourages further exploration of AI applications in healthcare for enhanced decision-making and resource management. 📚 Field significance: - 🔬 Highlights the role of artificial intelligence in transforming patient care pathways within nephrology and beyond. - 🌐 Sets a precedent for future studies assessing AI capabilities in various medical specialties. - 📊 Supports the development of AI-driven tools aimed at optimizing triage processes and improving clinical outcomes. 🗄️: [#AI #Triage #Nephrology #PatientCare #HealthcareInnovation #ChatGPT #DataInsights #Efficiency]

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Interesting perspective on #TechnologicalDebt in health care by Dave Lamar focused on the provider market, but we seem the same issues within state depts and payers. While good data governance practices are key, there must be a better way to solve this common - if not universal - challenge in health care, if only by offloading some of the administrative responsibilities that require the individual management and administration of no-value-add administrative functions. #healthdata

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👉🏼 ChatGPT and retinal disease: a cross-sectional study on AI comprehension of clinical guidelines 🤓 Michael Balas 👇🏻 https://lnkd.in/exN8AyjC 🔍 Focus on data insights: - 📊 ChatGPT achieved an impressive average score of 4.9/5.0 in alignment with AAO PPP guidelines, indicating strong performance in understanding clinical protocols. - 🏥 The evaluation covered 130 questions across 12 domains, showcasing the breadth of topics addressed in retinal disease management. - 📉 Variability in scores was noted, particularly lower performance in surgical management, suggesting areas for improvement in AI training. 💡 Main outcomes and implications: - 🤖 The study highlights the potential of AI, specifically ChatGPT, to assist in generating guideline-concordant responses for common medical retinal diseases. - 🔍 Identified errors in diagnostic criteria and treatment options emphasize the need for continuous refinement of AI models to enhance accuracy. - 📚 The findings suggest that while AI can support clinical decision-making, clinician oversight remains essential, especially in complex surgical scenarios. 📚 Field significance: - 🌐 This research contributes to the growing body of evidence supporting the integration of AI in healthcare, particularly in ophthalmology. - 🧠 It underscores the importance of interdisciplinary collaboration between AI developers and medical professionals to ensure effective application in clinical settings. - 📈 The results may influence future AI training methodologies, focusing on improving comprehensibility and accuracy in specialized medical fields. 🗄️: [#AI #retinaldisease #clinicalguidelines #ChatGPT #ophthalmology #medicalAI #surgicalmanagement #healthcareinnovation]

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What is supply assurance? It's ensuring access to the right product at the right time. While this may seem straightforward, it can be difficult to achieve in practice. Vizient expert Bill Selles discusses the challenges of supply assurance in healthcare, such as rising costs, labor shortages, and supply chain disruptions. He emphasizes why it's crucial for supply chain leaders to prioritize this issue to support their organizations and prepare for the future.

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Now that our HIT industry has spent nearly 40 years figuring out how to effectively pass data between sites, settings, and institutions of care (and while that journey isn't completely over, we're well on our way), we can finally turn and look farther down the road and consider what's next. In my view, advancing process consistency, agility, and change is that horizon. The BPM community is focused on that challenge, and having recently transitioned into HL7 as HL7 BPM, is squarely trying to take this on. With today's press release, HL7 is truly open-for-business in this space. Thanks to the OMG for nurturing this work along and for HL7 for having made our next home for this effort. So much to do! https://lnkd.in/dAF96g3n

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🏥✨ For many years, technology investments made by health organizations were rarely a driving force behind strategic #transactions. Explore in my latest article with Sam Prescott how #generativeAI has changed the game for Health CIOs. A must-read for leaders at the forefront of change! https://lnkd.in/gRWmi5AH

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Great presentation by Dan Buckland, MD for those looking to design Health AI/ML models. "...if we have been poorly treating a demographic or population, that will show up in the clinical data. We might not have race, gender or zip code as an identifier in the original clinical data." shared Dr Buckland. Good insights to support the need of Asher Informatics PBC AI/ML independent equity performance audits, AI-Assess. Through a funded federal research contract, we've designed a tool in which we can provide independent real-world data subgroup population testing on clinical and signal AI/ML models and products. Right now, the MVP is designed to support #oncologyAI FDA 510K submissions and enable the FDA access to the testing of an algorithms performance and therefore better evaluate an AI/ML submission. We've partnered with Gradient Health to provide data that includes race, gender, age and we're utilizing Rhino Health's federated platform to be able to pull testing data from multiple sources. If you're an oncology AI developer, we're currently conducting feedback interviews and would love to connect.

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AI tools, especially newer GenAI tools, can augment clinical diagnosis in so many ways - it can help patients seek care faster, it can improve communication between patients and their providers, it can also augment clinical decision-making to avoid premature closure and other such cognitive biases. On the other hand, these tools can also inadvertently exacerbate existing health disparities and result in patient harm. Getting this right is critical. Diagnostic errors are the number one cause of medical errors experienced by patients in the US, with 1 in 20 US adults experiencing a delayed or inaccurate diagnosis EVERY YEAR. Using new technologies to improve clinical diagnosis is a must, but we need to proceed with caution and vigilance. Join us on July 25th for a free, virtual webinar hosted by the The National Academies of Sciences, Engineering, and Medicine exploring the role of digital health and AI technologies to improve diagnosis. It's been a huge privilege serving as chair of an esteemed steering committee of national experts in clinical diagnosis and AI technologies - including Michael Howell, MD MPH, Julia Adler-Milstein, Judy Gichoya, Maia Hightower, MD, MPH, MBA, David Larson, Gene Harkless and Thomas Cudjoe, MD, MPH, MA. We've got an incredible line-up of speakers - including Michael Howell (Google), John Whyte (WebMD), Jonathan H. Chen (Stanford), Kadija Ferryman, PhD (Hopkins), Eric Horvitz (MSFT) and my boss Andy Bindman (Kaiser Permanente)! More info and registration can be found ⬇ Hope you can join us! UCSF Coordinating Center for Diagnostic Excellence (CoDEx)

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After a brief writing hiatus… Part III of “Who Pays for Healthcare AI” is live. The third installment defines five performance-based pricing models for healthcare AI and explores: • Four types of Return on Investment (ROI) in healthcare and life sciences • The importance of shielding at-risk pricing models from operational externalities • How better pricing model design can mitigate negative working capital risk under performance-based models • Taming the beast: attribution Check out Part III below, and subscribe to receive the final post of the series! https://lnkd.in/dPZSHMiy

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On July 9th, 2024, I was honored to deliver a keynote at Cleveland Clinic's inaugural Analytics & AI Summit, where I discussed high impact AI use cases in #healthcare and ways to operationalize Responsible AI (#RAI) principles at scale. Operationalizing RAI often starts by asking four key questions related to (1) Registering, (2) Testing, (3) Assessing (for bias), and (4) Governing AI. I also discussed my 1988 undergraduate thesis on "AI in Healthcare." Spoiler alert: Pretty much no-one cared about AI in 1988. 😉 Please check out my keynote here: https://lnkd.in/gNFwvd-i Rohit Chandra, Albert Marinez, Sarah Hatchett, John Guardado, Timothy Gray, CPHIMS, Sam Bhatia, Mark Modic

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