Damon Feldman

The most downloaded datasets from the California HHS site? If you follow me on LinkedIn, you've probably guessed it - the Medi-Cal enrolled provider list and Suspended and Ineligible list are the top two, followed by other datasets by a wide margin. The California Health & Human Services Agency (Cal HHS) has a nice data hosting site that resembles data.cms.gov. The site looks slick and provides interesting insights, including visitor statistics and download metrics. It's fascinating to see how people are interested in provider eligibility and exclusion data for Medi-Cal billing. Looking at how organizations use these public datasets, compliance emerges as the dominant use case. This makes sense - when accessing government data portals, users are typically verifying facts related to government programs. The user base spans health plans, hospitals, IPAs, medical groups, and even commercial payers, as Medicaid suspensions often trigger reviews across the healthcare ecosystem. What's interesting is that this pattern extends beyond healthcare. On data.ca.gov, which hosts all kinds of government data, the most downloaded dataset is the directory of certified electricians. This shows a consistent theme - whether it's healthcare providers or electricians, people come to government sites primarily to verify professional credentials and eligibility. This raises an intriguing observation about how government data is used. The way organizations use these datasets - downloading lists to verify credentials and eligibility - is functionally similar to what modern Identity Provider (IdP) systems do, but through a much more manual process. What are your thoughts on the future of government data in healthcare identity verification? #Healthcare #DigitalTransformation #Compliance #HealthTech #GovTech #Identity

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We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

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Psssst healthcare folks (and job seekers on the prowl)! AI in healthcare is a hot topic these days—so here’s a quick rundown of the major trends (credit to HLTH USA). ➡️ Imaging & Diagnostics: AI in medical imaging boosts diagnostic speed, accuracy, and efficiency, aiding clinicians in early disease detection and secure workflows. ➡️ Virtual Scribes & Assistance: AI-powered virtual scribes reduce admin tasks, letting providers focus on patients while improving workflow efficiency and documentation. ➡️ Patient Engagement & Care Management: AI enhances patient engagement through personalized care, streamlined communication, and coordinated care management. ➡️ Clinical Decision Support: AI supports data-driven decisions, improving patient care with real-time insights and evidence-based guidance. ➡️ Personalized Care & Workflow Automation: AI personalizes treatment with clinical/genetic insights, optimizing care for complex diseases and improving outcomes. ➡️ Predictive Analytics & RCM: AI-driven analytics enhance healthcare by predicting trends, improving outcomes, and streamlining billing for scalable, value-based care. ➡️ Healthcare Consulting & Administration: AI-powered consulting drives efficiency, innovation, and superior outcomes by transforming healthcare operations.

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HCPCS and healthcare policies, and Claude.ai Today, while diving deep into the newly dropped HCPCS file with its historical data, I noticed something intriguing: distinct patterns in code additions across different time periods. I saw clusters of psychiatry codes in 2012-2014, DME codes around 2002, and, recently, a surge in drug-related codes. Curious about these patterns, I turned to Claude (an AI assistant by Anthropic) for insights. The response was enlightening - these weren't random clusters but rather footprints of major healthcare policy changes! What I learned: - The 2010-2014 psychiatry code surge? It aligned perfectly with the Mental Health Parity Act and ACA implementation - The 2002 DME code expansion? Connected to Medicare Modernization Act (MMA) which included major DME reforms - Today's increase in drug codes? Reflects our boom in specialty pharmaceuticals and gene therapies It's fascinating how something as technical and administrative as coding systems can tell such a rich story about healthcare evolution. Sometimes the patterns we see in data are like whispers from the past, telling us about pivotal moments in healthcare policy. This experience reminded me that there are no random patterns in healthcare data - just stories waiting to be understood. #HealthcareAnalytics #DataDiscovery #HealthcarePolicy #AI #HCPCS #Healthcare #DataStorytelling Has AI helped you uncover any interesting patterns in your work? Would love to hear your stories! The chart below shows the number of HCPCS codes that were added over the years.

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To follow up on Dede Ainbinder recent post: If we use the current EHR data to build a AI clinical decision support tool it is unlikely to capture our clinical decision making process. EHR's are inherently designed to maximize billing and minimize risk of litigation. A clinical decision support tool based on this data will likely do the same. There is a large sunken cost fallacy here where we continue to hang on to garbage data. Just because the data gathering and storage process was capital and time intensive doesnt mean the data is valuable. To truly capture the clinical decision process we need to capture data which sits outside billing and litigation. Its hard to imagine a future where this data isnt used for billing and cannot be called upon during litigation. #EHR #healthdata #clinicaldecision

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John Snow Labs #Healthcare #NLP & #LLM 5.3 is now out! This release completely overhauls entity resolution - mapping medical entities in text to standard medical ontologies, in context - greatly improving accuracy. Full release notes: https://lnkd.in/gsiGF26i Install software: https://lnkd.in/gsTqkWKT #ai #generativeai #healthcareai #healthai #datascience #ontologies #medicalcoding

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“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

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Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

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Helpful survey From the paper: “In this paper, we have surveyed 129 papers and addressed these gaps by providing a thorough and structured review of prompt attacks on LLMs and VLMs.l #insightsonai.com

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As the discourse shifts from models to compound AI systems / agents, we need better AI benchmarks to evaluate multi-modal and multi-step task performance, especially in healthcare and life sciences. When we wrote the first paper demonstrating ChatGPT's performance on the USMLE, we chose the US Medical Licensing Exam as a benchmark for accessibility, speed, and ease. This benchmark was never intended to represent AI model performance on real-world clinical tasks. Today, I still see so many research teams and startups using benchmarks (like the USMLE) that are ill-fitted for assessing the true clinical or scientific performance and utility of the models they are developing for real-world contexts. Benchmark development may be seen as a "less sexy" area of research, but it is of paramount importance. Years after the rise of the transformer, we still lack adequate benchmarks for so many single-step tasks in biomedicine. With compound AI systems (i.e., architectures that integrate multiple AI models to perform complex tasks) emerging, we need new benchmarks for agentic behaviors. I'd argue that developing an agent with novel capabilities without at least proposing a companion benchmark (if an industry standard does not yet exist) may hinder the adoption of said agent, especially for high-stakes workflows. Designing more benchmarks that capture/simulate real-world clinical and scientific workflows will help us mitigate the major discrepancies observed between in silico and in vivo performance and better support safe + effective deployment of AI in biomedicine. There are already brilliant people focused here, and we need more. DMs are open if you're researching or working in this area of multi-step/multi-modal benchmarking in healthcare and life sciences! #healthcare #ai #artificialintelligence #generativeai

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Regulating #AI is a sticky wicket. As is often the case, California is ahead of the curve. SB 1047 is the first legislation to impose safety mandates for large language models. I'm as eager as anyone to see some guardrails put in place, but the risks of getting it wrong are not trivial. Solid reporting here via @jacoberobbins of PitchBook #artificialintelligence #regulation #machinelearning

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The debate about who controls one's own healthcare data is fundamental to how we evolve our health systems. - It enables us to manage our #wellness on an ongoing basis rather than any illness that may occur - It enables us to contribute our unique #realworlddata as we see relevant for #clinicaltrials or #populationhealth studies - It enables us to optimise #healthsystems to increase efficiency and improve #valuebasedoutcomes - It enables us to address #diversity and #bias through synthetic data generation where gaps exist - It gives us the potential of #portability of service for our healthcare needs The article from Artur Olesch shows how badly broken current systems are around the world. Currently Governments; Health Care Providers; Insurers; Tech Companies and Health Care Professionals all manage our healthcare data. Innovation in this space to enable citizens and patients to manage their own data assets and benefit from #genai tools to analyse will provide us with the next great leap forward in terms of health outcomes and system efficiencies.

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Big healthcare AI news yesterday as Tempus AI received the first-ever CPT code for an algorithm-only analysis of previously-sequenced, laboratory developed test transcriptomic data. TLDR: AI molecular phenotyping has a new business model. The PurISTSM test uses RNA profiling to identify molecular subtypes in patients with advanced pancreatic ductal adenocarcinoma, which may inform first-line therapy selection and management. While I'm skeptical that payer balance sheets are going to solve the "who pays for healthcare AI" problem, the reimbursement pipes for clinical AI are maturing. I am surprised to see more progress for usage-based vs. performance-based models given the many challenges with FFS economics. It will be interesting to see how payers assess the "medical necessity" and therefore reimbursability of clinical AI as these models come to market. https://t.co/eLe18SmihV

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17 reasons I loved #GTM24! 17. Sonal Mane could be a standalone reason to love this event! Her future of GTM AI and automation session with John Hessinger identified things GTMers should be experimenting with and how to incorporate GenAI into existing workflows. 16. Peter Ward’s discourse on formalizing market readiness and ways to align cross-functional groups. 15. Macy Berg Mody’s example of eliminating repetitive x-team readouts and making meetings more productive with shared tech informing multiple groups on customer feedback. 14. Holly Holbrook's best practices for internal comms like creating a “front door” for cross-team conversations and ideation. 13. Emi Wayner’s practical guide to GTM launch metrics was great, but it was even better to hear how she evolves metrics while retaining focus on key impact areas. 12. I loved Candice Sparks and Lexie Knauer’s dialogue on building for parity vs. differentiation. Don’t let your product get in the way of a good story! 11. The most POV-altering moment of the conference (for me) was the fireside chat on unlocking brand growth with Maya Shah-Ceccotti, Karen Sage, and Nidhi D.. It changed my thinking re: CLG. Build it into growth motions intentionally upfront! 10. “If you build it with them, they are already there.” - Kasey Stinson. 9. 43% of buyers desire a “seller-free” sales experience, according to Gartner (and Filip Szymanski in his session on PLG vs. SLG). I appreciated his thoughts on scaling growth loops, whether collaboration growth loops or user content growth loops. 8. More people would remotely lead successful GTM teams if they followed Paige Lapen’s “make work rock” framework or James B. Stanton’s weekly meeting cadence: Commit - “CFC” - Complete. 7. Internal messaging gets diluted with multiple communication methods, as Somesh Chablani pointed out. Don’t drop it into Slack and assume everyone knows. Overcommunicate with remote teams. 6. Daniel Kuperman’s competitive intelligence matrix and launch blueprint could have been an entire session, and I still would have wanted more! 5. Nonya Collier is the collaboration queen! Co-creation in a brain trust, codifying insights, amplifying the collective intel. 4. “Beyond the Launch” by Ed Cho may have convinced me that math can be fun and definitely convinced me of the necessity of focusing on pipeline maturation and not just pipeline creation. 3. Ariel Cai walked through how AI is redefining the go-to-market playbook. 2. I was excited to share my session on accelerating sustainable growth with ideal customer profiles. Plus, in a shocking turn of events, I got to chair the second day! 1. The number one reason to attend any event from The Alliance/GTM Alliance is to connect with the most incredible people! I can’t list everyone, but I have to mention Chase Payne, Cinthia Silva, Jan, Amy 🐆, Rohit Angle, Alisha Johnson, Catherine Knoll, Samantha, Sasha, and I finally got to meet the mastermind behind it all: Jack Debono!

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