Ophir Lotan

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

39 Comments

There’s a category we believe is getting too little attention when it comes to AI in healthcare: AI Clinical Documentation Integrity (CDI) While AI ambient scribes and autonomous medical coding solutions have gotten most of the headlines, we’d argue AI CDI is equally impactful when it comes to quality care and accurate billing. For those not familiar, CDI ensures that clinical documentation accurately reflects the patient’s medical condition and the provided care. Historically, CDI was a manual slog, with dedicated CDI specialists, nurses, and physicians painstakingly reviewing patient charts to ensure documentation supported the care delivered. Today, AI is reshaping this landscape. While it’s still early days, we’re seeing three primary approaches to applying AI when it comes to CDI: 1️⃣ CDI + ambient scribing—an AI ambient scribe and AI CDI assistant operate simultaneously, generating comprehensive notes and surfacing codes for physicians to review at the point of care. (Example: Ambience Healthcare’s AutoCDI) 2️⃣ CDI via real-time diagnosis support—diagnoses are suggested at the point of care based on clinical data in the EHR, with auto-generated documentation and audit trails. (Example: Regard) 3️⃣ CDI via an AI-driven post-coding / pre-billing review—the entire patient record is ingested and analyzed against what is coded, surfacing suggestions for revenue and quality opportunities. (Example: SmarterDx) To date, AI in healthcare has focused primarily on administrative use cases. With the increasing adoption of AI CDI solutions, we’re now taking a step closer to clinical workflows. As clinicians get used to AI-suggested diagnoses in the CDI context, it will be interesting to see if this increases their comfort with AI playing a role in clinical care. Here's our working list of AI CDI vendors. Anyone else we should add? • Ambience Healthcare • Brellium • Iodine Software • Regard • Semantic Health • SmarterDx   

96

6 Comments

The Morisky Medication Adherence Scale-8 (MMAS-8) is an ideal barometric measure for assessing medication adherence among patients due to its reliability and consistency over time. Connect with me for more information and if you want to integrate this tool within your own EHR or digital platform for RPM, telehealth or DTx. Your patients deserve to know their adherence score and an opportunity to improve it over time. 1. Validity and Reliability: The MMAS-8 has been extensively validated in various patient populations and healthcare settings, demonstrating strong psychometric properties. Its reliability makes it a dependable tool for consistently measuring medication adherence among patients over time. Healthcare providers can trust the accuracy and consistency of MMAS-8 scores when assessing adherence levels. 2. Comprehensive Assessment: The MMAS-8 assesses multiple dimensions of medication adherence, including forgetfulness, carelessness, and intentional non-adherence. By capturing these various aspects of adherence behavior, the scale provides a comprehensive understanding of patients' medication-taking patterns. This comprehensive assessment enables healthcare providers to identify specific barriers to adherence and tailor interventions accordingly. 3. Easy Administration and Interpretation: The MMAS-8 is very easy to administer and interpret, making it practical for use in clinical practice. The scale consists of eight simple questions that patients can quickly respond to, minimizing burden on both patients and healthcare professionals. Scoring and interpreting MMAS-8 results are straightforward, allowing for efficient assessment of medication adherence during routine clinical visits. 4. Longitudinal Monitoring: Due to its reliability over time, the MMAS-8 can be used for longitudinal monitoring of medication adherence among patients with chronic conditions. By administering the scale at multiple time points, healthcare providers can track changes in adherence behavior, evaluate the effectiveness of adherence interventions, and identify trends or patterns that may require intervention adjustment. This longitudinal approach facilitates proactive management of adherence-related issues and supports continuity of care. 5. Predictive Value: Research has shown that MMAS-8 scores are predictive of various clinical outcomes, including treatment response, disease progression, and healthcare utilization. Patients with high MMAS-8 scores (indicating good adherence) are more likely to achieve therapeutic goals and experience positive health outcomes compared to those with low scores. By using the MMAS-8 as a barometric measure, healthcare providers can identify patients at risk of poor outcomes early on and intervene to improve adherence and prevent complications.

24

7 Comments

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

100

18 Comments

LSI, BioscienceLA, #LAMedtechweek, and HLTH's #VIVE2024 event resulted in the latest GTM Pulse Check offering which has successfully taken off! As a result, I am continuing on last week's series of go-to-market tactics. Crafting a robust marketing plan is essential for medical device companies to thrive in today's world. By leveraging these outlined sections, organizations can optimize their marketing strategies and drive meaningful impact.This post takes a step back to outline the sections of a marketing plan. Read on 👇🏼 Looking for support in creating your marketing plan?  Let’s chat! Book here 👉🏼 https://lnkd.in/g92YA9WK _____ P.S. Enjoying my content? ⚕️Follow me for more tips on medical device commercialization and repost to share with your network. #medtech #gotomarket #medicaldevices #healthcareinnovation

27

2 Comments

Q1 2024 was big for Digital Health, with $1.1B in VC funding. A large portion came from major deals like $126M for Transcarent and $108.8M for Rightway. Despite some challenges in digital therapeutics and telehealth, investments in general care ($213.4M) and health benefits navigation ($140.7M) are promising. Read what’s driving the shift from direct-to-consumer to enterprise telehealth models here: https://ow.ly/uNYs50SpXGh

27

1 Comment

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

Value-based care models provide us a way forward in healthcare by emphasizing quality over quantity in patient care, which inherently necessitates robust data tools to engage patients effectively. As technology advances within healthcare, optimizing patient engagement within these models is becoming increasingly imperative for improving outcomes and operational efficiency alike—proper technology adoption and implementation is no longer optional! #PatientCare #ValueBasedCare #VBC #Healthcare https://lnkd.in/eHxWTcQU

16

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

420

8 Comments

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

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

17

8 Comments

Interesting take from TechCrunch on AI within healthcare. I think given the pace of acceleration in the space that many of these qualms won't be relevant in a fairly short amount of time. But, interesting to note what some people are thinking about when it comes to AI in healthcare.

6

זה באמת עושה לי טוב לראות מטוס ממריא מתוך דמעה שקופה. A few thoughts on HLTH Europe 24 1. AI is the latest buzzword. Personally I’m pessimistic regarding AI’s near future effect on healthcare. I’m expecting no drama since EMR data is limited inaccurate, (physicians counldnt care less for Emr accuracy, we want to treat the patient and not the computer), and represents only a shallow and narrow picture of a round figure - the patient with needs, culture, wants, needs, risk aversion profile and so much more. Medicine can’t be one size fits all. 2. AI will make headway in “simple rule” “limited options” fields like imaging and pathology. It will also add so much demands to the workload of pathologies and radiologists through significant incidental finding systems under development. These fields will evolve and demand for good pathologists and radiologists will probably grow. 3. IOT, wearables, home monitoring, telehealth, is evolving but not speaking to the EMR. Most offerings are speaking to propriety systems and clinicians needing to interact with yet another system. This will not help scale of home care and monitoring. 4. TMI we are making too much information and desperatly need good systems to summarise and alert clinicians only when needed. 5. Medical devices - many encouraging developments that empower primary care. From Pocus and pulmonary test to colonic polyp screening our offices will see more and more technologies that will maintain the high value patients are gaining from primary care. 6. EMR’s are the root of most evil and this needs to change. Most are closed gardens expensive and limited. Open EMR and FIRE initiatives are not moving fast enough and we are stuck with suboptimal systems that contribute to burnout and limit our capacity to predict and be proactive to our patients. 7. Israel’s health-tech scene is surprisingly developed and with our excellent healthcare system we are at a place we can be a global leader in the field. Any thoughts?

33

9 Comments

Manually entering patient data into your EHR? Duncan McGillivary explores how AI is revolutionizing healthcare, offering solutions that break free from clunky EHRs and deliver more efficient workflows. Check out our latest #AnsaOpenSpaces. #healthcare #AI #EHR #clinicianempowerment

8

1 Comment

Platforms are in, point solutions are out as the healthcare AI category leaders come into focus this year. In conversation with Shelby Livingston at Endpoints News, I shared: 1. Why AI-first companies have the greatest platform potential and heuristics for identifying these opportunities at the earliest stages, 2. How companies are differentiating through co-development and distribution partnerships with leading healthcare institutions, and 3. Strategies for navigating anemic budgets and constrained market sizes through business model innovation such as AI-enabled services and pay for performance models Read more here: https://lnkd.in/dJWqv_zC Bessemer Venture Partners Abridge #healthcare #lifesciences #AI #generativeAI

235

13 Comments

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

35

2 Comments

🔍 Unlocking Enhanced Market Access: Key Insights from Reuters Events Pharma, "Engage payers with patient value to streamline pathways to access" Diego Cortina de la Fuente, MD, M.Sc. Dusica Hadzi Boskovic ✔ In this recent webinar, industry experts shared invaluable strategies on how to streamline market access by aligning payer engagement with patient value. Here are the pivotal insights: ✔ Patient-Centric Value Propositions: Demonstrating the real-world patient benefits of a treatment is crucial. Evidence that highlights improved patient outcomes can significantly influence payer decisions. ✔ Integrated Evidence Strategy: Developing a robust evidence generation strategy that encompasses both clinical and economic data is essential. This integrated approach helps in presenting a compelling value story to payers. ✔ Collaborative Partnerships: Establishing strong relationships with healthcare providers and payers is key. These partnerships facilitate the exchange of valuable insights, leading to better alignment on treatment value and patient needs. ✔ Leveraging Real-World Data: Utilization of real-world data to support value claims can expedite payer buy-in. This data provides concrete evidence of how treatments perform in everyday clinical practice, beyond clinical trial settings. ✔ Navigating Regulatory Landscapes: Understanding the specific requirements of different regulatory environments helps in tailoring the evidence that resonates most effectively with local payer groups. 🌐 Engaging payers effectively requires a deep understanding of the nuanced needs of both patients and payers. By focusing on evidence that underscores patient value, we can facilitate smoother pathways to market access, ultimately improving patient outcomes. Here's the link (Great Insight!): https://lnkd.in/gtizqJUi #MarketAccess #HealthcareInnovation #PatientCare #PayerEngagement #WebinarInsights

3

Have y’all ever watched someone pour water in a cup so fast that it overflows? That’s where I think we are in US #healthcare right now, and according to an article by Yahoo Finance, I’m spot on: US Healthcare spending rose to $4.8 Trillion in 2023, an estimated 17.6% of US GDP. Now I’ve worked in roles across #publichealth, #healthcare and #pharma, but admittedy never quite understood the economic jargon of comparing US healthcare spending to gross domestic product (GDP). So here’s my attempt at explaining this in plain language. ✨ GDP is the total value of all goods produced in a country during a time period; it’s a measure of the size and health of an economy. ✨ Healthcare spending refers to all money spent on healthcare services and products in a country— i.e- hospitals, doctors, medications, insurance… ✨ Why do we compare the two? When we compare healthcare spending to GDP it helps us understand how much of our economic resources are being used for health related expenses. ✨What does this headline mean? U.S. healthcare spending grew faster than the economy *overall* last year—and is expected to do so through 2032. Think of it like this: if we pour water in a cup too quickly, it will overflow. This is like healthcare costs rising faster than the economy can sustain. ✨What’s driving the increase in cost? 1) Insurance coverage has hit historic highs, 2) Medicare is projected to have the highest spending growth, 3) Hospital spending is soaring… Here are a few additional thoughts I had… 💡 How will the rapid uptake of #AI in healthcare, impact these projections? 💡 I wonder how projected healthcare workforce shortages will impact this? 💡*This* is what we mean when we say everyone should care about #healthequity, because inequities are costing all of us. What do y’all think about future drivers of cost or solutions? Drop me a line in the comments! #healthcareonlinkedin

37

2 Comments

Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • Oracle Health announced the release of its AI-powered clinical digital assistant, which integrates “clinical automation, conversation-based note generation, and proposed clinical follow ups” directly within the EHR. • NextGen Healthcare released a version of their Ambient Assist designed for small practices, which includes Spanish support and specialty-specific workflows.    • Community Health Network has begun a multiyear Microsoft implementation including Nuance Communications Dragon Medical One and DAX Copilot. 🧑‍💻 Virtual and At-Home Care • Hackensack Meridian Health partnered with Medically Home to implement their hospital at home program. • Cleveland Clinic is integrating Masimo’s wearables in a new partnership focused on hospital-based RPM. • eVisit acquired UPMC Enterprises' inpatient teleconsult technology and an investment from UPMC’s innovation and investments arm as part of their $45M Series B funding. • PatchRx released an API-based tool allowing providers to sync data directly into their existing care platforms. 📊 Data & Analytics • Atropos Health added two new partners and a data scoring system to allow users to select the most appropriate datasets for their queries. • Xealth launched a metrics tool that allows customers to measure and track patient engagement with their digital health programs. 📋 Patient Administration • Fabric acquired MeMD from Walmart. 🔄 RCM • Inovalon joined MEDITECH’s interoperable product marketplace. • RevSpring released an update that integrates a lockbox solution for check processing within its existing payment platform. • Solventum Revenue Integrity (fka 3M Health Care) released its new AI CDI software powered by Sift Healthcare, Solventum Revenue Integrity System, which uses machine learning to predict denials before they happen. 🍎 VBC Data & Analytics • Arcadia acquired CareJourney leveraging their cost, quality, and benchmark data to enhance Arcadia’s data, analytics, and workflow tools. • Adventist Health selected The Garage In's population health management platform to support its VBC operation. 🧠 Food For Thought •  Coalition for Health AI (CHAI) released its draft standards guide for public comment. https://lnkd.in/eMUXGfvz • We really appreciated Daniel Yang, MD’s post about a recent article on the emergence of a new leadership role: the Chief Health AI Officer (CHAIO) https://lnkd.in/ejZxnJCj --- 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