Rupen Patel

Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Products • Emergency Services, Inc. + Augmedix Go ED: The emergency medicine group adopted the new ED-specific AI ambient scribe solution.    • Color + OpenAI: The cancer care tech company partnered with the GenAI giant to build a AI clinical decision support copilot.    • AKASA Medical Coding: The AI RCM vendor known for its AI prior auth tool AKASA Authorization Advisor released a new GenAI medical coding solution.    • Humata Health: The AI prior auth for providers vendor closed a $25M funding round led by healthcare org-backed funds The Blue Venture Fund and LRVHealth.    • Wellsheet + Concord Hospital Health System: The AI-enabled Smart EHR UI solution announced a client-reported 16.3% reduction in length of stay and 40% decrease in time spent in the EHR via its partnership with Concord Health, as well as the release of its new LLM-generated handoff summary feature. 🍎 VBC • BSIM Healthcare Services + Innovaccer: The healthcare provider—embarking on its first VBC contract—adopted the pop health data platform for analytics. 🩼 Durable Medical Equipment • Parachute Health: The DME e-prescribing and ordering solution released an update that integrates prior authorization. ⚕️Clinical Staffing • Matchwell + Indiana Hospital Association: The clinical staffing service partnered with the hospital member org to create the first state-wide resource pool for temp staffing. 🧑‍💻 Virtual and At-Home Care • Deaconess Health System + KeyCare: The Indiana-based health system adopted the Epic-based virtual care provider for around-the-clock virtual urgent care.    • UC Davis Health + Current Health: The health system launched a new RPM program for high blood pressure in partnership with Best Buy Health’s care-at-home platform. 🧠 Food for Thought: We came across a couple of good resources this past week on the current state of healthcare data information exchange: • This article in Health Affairs from Deven McGraw and Tina Grande provides a comprehensive overview of some of the patient health data abuses we've seen come to light in recent months, as well as a number of policy recommendations to improve the health information exchange landscape for all participants. https://lnkd.in/e_GEQ-JJ  (h/t Alya Sulaiman)    • This episode of Health Gorilla's InteropTalk gives a helpful update on the current state of affairs, with a particular focus on the state of trust across the health data exchange community and how the definition of "Treatment" may evolve. https://lnkd.in/e96AJG-5 (h/t Brendan Keeler) --- Reminder: you can sign up for the Elion Briefing to get the latest healthcare tech news delivered to your inbox each week 📬 👉 https://lnkd.in/e2UJ3ckP

36

2 Comments

There is a misconception emerging that AI agents will lead to immediate reductions in healthcare labor costs. While AI agents can automate end-to-end workflows such as booking appointments, answering basic patient questions, and performing form completion for prior authorizations, automating a single task is not equivalent to automating an entire job. Nurses are a prime example. Certain nursing tasks, such as care coordination and patient documentation, are highly automatable by AI agents; however, there is a tremendous amount of work nurses perform on the ground that cannot be fully automated with current technologies such as direct patient care, physical examinations, medication administration, wound care, patient support, and clinical assessments. These nuances make full role elimination less likely with current technologies. When articulating the ROI of an AI agent, we need to be both precise and accurate. Automating a task is more often not automating an entire profession. For roles that encompass many functions, such as nursing, AI agents can be invaluable tools for unburdening staff, increasing efficiency, and boosting throughput—benefits that are particularly valuable given current healthcare staffing and resource shortages. The most promising job candidates for full role elimination through AI remain positions with monolithic task structures, of which there are relatively few in healthcare today—scribes, medical coders, and data entry specialists for example. It is therefore unsurprising then that the most progress has been made in these categories.

123

18 Comments

One key reason why healthcare technologies like AI, telemedicine, and software haven't structurally lowered costs or broadened access for the entire system after decades of experimentation (excluding pure financial engineering of payer contracts) is the limited reimagining of care delivery itself, with asynchronous telemedicine as a notable exception. In the same way that digitizing physical paper failed to exploit the capabilities of a new computing medium for word processing, hosting physician-patient conversations over video does not transform the actual delivery of care. In telemedicine, many of the inputs (e.g., clinician time) and therefore cost structures of in-person care persist, which is why we often see fee-for-service telemedicine companies struggle to achieve economies of scale (i.e., growing revenues non-linearly while growing costs linearly). Until we deconstruct the components of a medical appointment – from intake and triage, to chart review, history gathering, physical examination, diagnosis, treatment, and follow-up – we will continue to be underwhelmed by the disconnect between the transformative potential of these technologies and real-world patient/system impact. I’m optimistic that the emergence of reliable and performant medical AI agents will urge us to perform this deconstruction and redesign the fundamental building blocks of a “medical appointment.”

215

38 Comments

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

130

8 Comments

A health system asked if I could share what SeamlessMD’s secret sauce was for engaging patients… so I’ve decided to open source my response here. They wanted to know what principles and success factors have enabled our customer partners to achieve industry-leading results like: ⬇️ Cost of care by $1,000+ per patient ⬆️ Increased net profit of $1,000+ per patient ⬇️ 1 to 2 days length of stay ⬇️ 89% readmissions reduction by up to 89% ⬇️ 68% ED visit reduction by up to 68% ⬇️ 89% mortality ⬇️ 65% phone calls to providers Here are the main principles and success factors I shared: 1️⃣ It starts with a high touch partnership to ensure success. The People + Process we assign to a health system partnership matter even more than the Technology 2️⃣ Clinical expertise applied to the product matters more than Tech features. Our product’s secret sauce is our world class, pre-built care plan content. 3️⃣ Key elements of our care plan content that drive high patient engagement include: - Brand personalization - Condition-specific patient experiences - Personalization to a patient’s demographics & comorbidities - Condition-specific health status & symptom tracking - Automated self-care guidance for 80%+ of patient issues 4️⃣ Obsession with patient accessibility further optimizes patient engagement. - Gr. 6 reading level - Colour-blind tested - Screenreader friendly - WCAG 2.0 Level AA requirements met and more 5️⃣ Digital care plan implementation is Clinical Change Management. A successful implementation requires a proven playbook and hands-on expertise for: - Achieving care pathway standardization and alignment - while being efficient with clinician time commitment - Embedding digital care journeys into the clinical workflow: What workflows make the most sense for receiving alerts and monitoring dashboards given our limited staff resources? - Having the clinical acumen to going deep into the nitty gritty clinical workflows. E.g. for a pregnancy care journey - how will our solution know if there is an infant loss? How should the care plan change in that case? 6️⃣ Clinicians must be engaged to have a sense of ownership so they promote it to patients We’ve figured ou the optimal time commitment that ensures all clinicians are engaged during implementation (hint: physicians have very limited time!) 7️⃣ Let busy clinicians act as the Editors of Clinical Content. By having a clinical Patient Education team act as proactive Ghost Writers. - The key principles are to (1) earn clinician trust and (2) reduce friction to clinician engagement. 8️⃣ The ideal approach to patient enrollment varies from health system to health system. Sometimes a hybrid approach might even be needed 9️⃣ You must believe that the job’s not done until you achieve business outcomes. We obsess over the data and push our partners to measure ROI. More details in the slides below 👇 *** I’m curious: Which of these resonated most?

40

7 Comments

An overlooked benefit of AI scribes: standardizing and enriching clinical documentation for research purposes. Today, patient stories are filtered heavily through the lens of the physician and are further warped by billing codes. Depending on the medical specialty, pertinent positive or negative findings may not be documented, especially if they are insignificant for billing purposes. Omitting this information has tremendous downstream implications for research on disease phenotyping, progression, and treatment, as well as clinical trial recruiting. If architected thoughtfully, AI scribes can preserve the integrity of the patient journey, producing standardized and consistent documentation to support breakthrough research across disease classification, diagnostics, natural history, and therapeutic development.

123

11 Comments

In a recent report, only ~20% of healthcare executives claimed that they fully trust their data. At Fullsteam Health, we think that % is way too low. We're committed to reversing this trend with our automated data curation platform which was initially developed at Duke to address this very issue and allow for fast access to data that is needed and that has been cleaned, grouped, and continually monitored. https://lnkd.in/epWvPTDV

21

6 Comments

The power of one question from Jeremy Bikman: "What workflow do you solve that is absolutely essential and not provided by any other vendor in your healthcare market?" I had a great catch up with the always insightful Jeremy Bikman and we shared ideas around AI. Jeremy got at something that has been bothering me - how do you think about what would make for a successful AI application or, maybe even more broadly, any healthcare software innovation. The conversation was far ranging, but in some ways boiled down to the one question above. Truly, the power of one question!

11

3 Comments

Today’s healthcare policymakers are focused on their favorite boogeymen: physician compensation, biopharma, and new medical technologies. The real villains are ‘benefit managers’ and administrators’ who lurk in the shadows extracting 25 cents of every healthcare dollar. The reality is, in fact, in plain view if you look: administrators have grown 3200% while physicians have grown 200% over the last two decades; physician compensation is down, net of inflation. Admin comp is up, way up. PBM (pharmacy ‘benefit’ ‘management’) revenue has outstripped Pharma revenue growth in the last 20 years. Pharma extends life and improves quality of life, and extends working lives of taxpayers. PBMs - who knows… People grasp onto things they understand like doctors and drug companies. ‘Administrators’, ACOs, VBC, VBC, PBMs, spread pricing are more challenging to grasp and are allowed to obsfucate people about what they do from the shadows. America’s healthcare landscape is burdened by ‘administrators’ and ‘managers’ who extract rents from an otherwise highly innovative system. https://lnkd.in/gK3mCme4 https://lnkd.in/gPC9vKB9 DocVader puts it aptly: ‘thoughts and prayers’ https://lnkd.in/gUfnSXGV Technology is one answer. Physician and nurse leaders are another. If your company is about literally decimating administrative costs in the back office or front office of healthcare, I applaud you. As Americans we need this. Return care to the people and shrink administration and management bloat across the system. Medicare deserves this and so do the commercially ‘insured’.

158

18 Comments

AI has made good design more important than ever in healthcare. Why? Model performance alone doesn't guarantee clinical or financial value. A model can deliver a robust insight/output (accurate, reliable, complete, etc.), but clinician-users may struggle to use or integrate it in a real-world setting. As healthcare AI systems become compound machines, these design challenges amplify. Here are two simple examples: • AI systems that do not integrate seamlessly into EHRs, PACS, or pathology viewers may drive differential rates of utilization/adoption across sites, privileging some patients to a given algorithm without an underlying clinical rationale. • Imagine a model recommending a "next best step in care" that is not provided at a given hospital (e.g., due to lacking resources or infrastructure). Contextual and human factors can have a greater influence on whether the value of an AI intervention is realized, even when in silico model performance is compelling. There are two near-term opportunities here: 1) Healthcare AI model benchmarking should encompass "end-to-end systems," capturing nuances around system design, user experience, workflow integration, and ease of adoption. 2) It's time for AI-first design in healthcare – made possible by revisiting the unique capabilities of AI systems and their emergent behaviors to interrogate what new user experiences are possible. What are your favorite examples of AI-first design? #healthcare #ai #artificialintelligence #generativeai

60

7 Comments

Just read Davis Liang's interview on Out-Of-Pocket and it got me fired up. We need more innovators tackling our outdated healthcare system. A few thoughts: First, a shoutout to Abridge for focusing on the nurse experience. Nurses are the backbone of our healthcare system, yet too often they get overlooked. Happy to see this changing. At Tensor, we're always asking: "How can we make healthcare workers' lives easier?" A study found that healthcare workers are experiencing stress and burnout at rates as high as 70%, which negatively impacts patient care. [1] It's really not just about tech - it's about supporting the humans at the heart of care. Davis mentioned the challenge of multilingual capabilities. Yup, I feel that. It's a huge issue, especially among diverse Medicaid populations. In fact, a systematic review of 14 studies involving over 300,000 participants found that language barriers in healthcare lead to a host of problems: · Miscommunication between medical professionals and patients · Reduced satisfaction for both healthcare providers and patients · Decreased quality of healthcare delivery · Compromised patient safety [2] Solving this could be a game-changer for health equity. Another critical aspect addressed was model drift - their approach to this challenge is spot on. For those unfamiliar, model drift is like your AI getting rusty. It's when your once-shiny ML model starts spitting out garbage because the world changed, and it didn't keep up. (This could mean the difference between life and death in healthcare.) As model drift shows, you can't just "set it and forget it" with AI in healthcare. Continuous improvement is key. Overall, I’m intrigued by their work on inpatient use cases. There's so much untapped potential there. It's inspiring to see companies like Abridge tackling these complex problems. While we might approach some things differently at Tensor, we're all working towards the same goal: better, more efficient healthcare for all. To me, the future of healthcare (with AI) will lead to: 1. Personalized care for all 2. Data-driven, increasingly rapid advances in standard of care 3. Prevention, prevention, prevention! 4. Better adherence to known screens to detect diseases before they become unmanageable It’s an exciting time! Hey Davis - if you're reading this, let's grab a coffee sometime. I'd love to geek out about healthcare AI with you!

11

3 Comments

Healthcare is highly suited for AI automation, with many opportunities to improve accessibility, affordability, and outcomes. AI can drive efficiencies across various functions, though it will inevitably reduce the need for some human roles. #higherorder #ai #automation #healthcare #healthtech

6

Really interesting conversation between Meghan McCarty Carino and Casey Ross about AI in senior care and the potential role that algorithms can play in determining care plans. I think using AI or algorithms in this way has benefits and challenges that come along with it. One positive effect it could have is increased personalization. Instead of giving everyone with a specific illness or medical issue the same treatment, it can help determine a more personalized solution that could improve patient outcomes. However, algorithms aren't always foolproof. In some instances, they can perpetuate bias and bad decision-making. If you're not working with unbiased, high-quality data, it could have major ramifications for a large number of patients. That's why I think if healthcare facilities use algorithms for patient care plans, humans still need to be active in developing the treatment plan. #SeniorCare #PatientCare #AIPersonalization

3

Fantastic article written by Andrew Borenstein, Vinod Swarna, and Anuj Saxena from ZS. The piece highlights that "as #healthcare costs continue to rise, U.S. health plans must be more vigilant in ensuring the integrity of their claims payment processes and systems." Specifically, health plans should lean into payment integrity allowing them to reduce medical spend, shrink provider abrasion, as well as comply with regulations and contractual agreements. #AI offers a promising solution, but many companies have yet to embrace its potential, even as annual losses reach a staggering $935 billion! https://lnkd.in/eg2CBkaT

12

1 Comment

The defining factor isn't the size of the company or startup, but the vision behind it. Take OpenAI, for instance. Until last year, unless you were deeply embedded in the ML world as a computer scientist/researcher, or enthusiast, you might not have heard of OpenAI. Meanwhile, a giant like Google was dominating the scene. However, a few years back, OpenAI was just another small startup. Now, it's rapidly evolving into a major player in big tech. Why and how? It comes down to having a long-term vision, a clear roadmap, patience, and solid execution (i.e., talents). Even in the context of healthcare AI, where (small) startups face many challenges (including the ones mentioned), the principles of vision and strategy apply. While merging with big tech might seem like an option for some, if a startup truly believes in its vision and has a well-defined path to achieve it, it should forge ahead independently. The landscape is evolving, and like OpenAI, today’s small startups with clear, visionary goals could be tomorrow's industry leaders. #starup #founders #openai #google #healthcare

3

🚨 Senators Question Telehealth Moves by Pharma Giants Eli Lilly & Pfizer 🚨 In October, Senators Durbin, Sanders, Welch, and Warren sent letters to Pfizer and Eli Lilly, raising concerns about their new telehealth DTC platforms. These platforms, which connect patients directly to doctors after reading about medications, could blur the lines between healthcare advice and profit-driven influence. 💊 Key Concerns: - Anti-Kickback Risks: If these platforms pressure doctors to prescribe their medications, Medicare and Medicaid laws may be at risk, specifically the Anti-Kickback Statute. - Potential for Fraud: Referring to a recent HHS alert, these models could create opportunities for fraud if companies incentivize doctors to prescribe certain medications, regardless of medical need. In a broader context, cases like Done Global, Inc., involving Adderall prescriptions, highlight the challenges and risks tied to telehealth and DTC practices, especially as federal agencies continue investigating fraud concerns. #HealthcareEthics #Telehealth #Pharma

3

1 Comment

Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • Suki is partnering with Zoom to generate clinical notes as well as other integrated AI features.    • Net Health announced it would integrate AI ambient scribe Tali AI. • Aidoc is partnering with NVIDIA to create a guideline to accelerate AI adoption, the Blueprint for Resilient Integration and Deployment of Guided Excellence (BRIDGE). 📞 AI Contact Center • Authenticx released a new AI assistant that helps users uncover business insights from their data. • Infinitus Systems, Inc. closed a $51.5M Series C with participation from Memorial Hermann Health System and others.   🏥 AI Facility Management • Cognosos, Inc. acquired Cox Prosight. 🌡️ AI Remote Patient Monitoring and Triage • Biofourmis has merged with CopilotIQ. 📈 Data & Analytics • Avandra Imaging launched out of stealth; it plans to sell to biopharma, medical research, and AI companies. • Gradient Health acquired DataAppraisal. • HealthEx launched and announced $14M in Seed and Series A funding. 📋 Patient Administration • Notable released Flow Builder, which allows users to develop their own AI agents for operational workflows within EHRs and other systems. • MIla Health Inc released its flagship AI solution of the same name, and announced implementations at provider organizations including Aayu Clinic, MIMIT Health, and Best Practices Inpatient Care. 🔁 RCM • Oracle’s patient accounting solution has been integrated with FinThrive, which enables improved functionality, performance, and a unified login experience. • Blue Shield of California and Salesforce are partnering on a solution that promises to give patients prior auth responses within the course of their appointment. ⚙️ Clinical Operations • Dyania Health closed a $10M Series A, including participation from Cleveland Clinic Ventures. 📱 Patient Support & Communications • Psych Hub released a new solution designed to support patients seeking mental health support, directing them to both non-clinical and clinical resources, thereby assisting behavioral health providers to reach new patients. • Mayo Clinic has partnered with Lin Health to support patients after treatment for fibromyalgia.   🍎 VBC • WellSky acquired Bonafide Health, allowing it to provide a more integrated platform for providers to run their businesses. 🧠 Food For Thought • 𝗦𝘁𝗿𝘂𝗴𝗴𝗹𝗶𝗻𝗴 𝘁𝗼 𝗸𝗲𝗲𝗽 𝘂𝗽 𝘄𝗶𝘁𝗵 𝗮𝗹𝗹 𝘁𝗵𝗲 𝗔𝗜 𝗴𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀? Andrew Hines, CTO at Canvas Medical, shared a breakdown (plus some hot takes) on all of the many entities currently vying for market share in the AI governance space https://lnkd.in/eMNpXw4t --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

65

4 Comments

Latest #PitchBook data for #HealthIT investment shows a preference for later stage investment across healthIT that pre-dates the VC downturn and current interest rate environment. Even though healthIT has been more resilient to current headwinds compared to health care services and PPM plays, perhaps it speaks to a need to scale solutions and points of saturation for new entrants. "...In the lower middle market, we are beginning to see more examples of a phenomenon we have anticipated for a while: PE firms and PE-backed companies acquiring VC-backed startups. Lower-middle-market PE firms perennially complain that VC- backed companies expect too-high multiples, and these firms would generally prefer to invest on a multiple of EBITDA rather than a multiple of revenue..." Speaks to a convergence that would allow PE-VC M&A on both ends of the startup spectrum. #HealthcareVC #healthcarePE

1

1 Comment