Thivaher (Thiv) Paramsothy

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.”

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Jeremy Knibbs and I caught up with Paul Wilder Bri Buch and Gurpreet (G.P.) Singh for a chat yesterday as we are watching the US interoperability journey with interest post the 21st Century Cures Act. We had the pleasure of Paul joining us at our Wild Health Cloud Summit in Sydney in 2022 for an armchair chat about what happens when you legislate to share health data using a stick and not a carrot. We were curious to see how things were tracking. CommonWell Health Alliance recently partnered with ELLKAY a nationwide leader in healthcare connectivity to support data exchange to 200 million patients across a network of 34 000 provider organisations in preparation for the next stage of their interoperability journey (TEFCA) the Trusted Exchange Framework and Common Agreement. The numbers seem mind boggling to us in Australia. Whilst Australia's population is a fraction of the US, we have not yet cracked the interoperability nut despite throwing billions $$ at the problem so we thought we would take a deeper dive on the US journey and our own as one of the themes at our WildHealth Summit in Melbourne on October 22nd. The program will be live next week and it’s one of our best yet! ( Watch this space!) As we can see from the Epic versus Particle dispute there is a lot we need to be across on our own journey to interoperability and a lot we can learn to avoid the same scars. #interoperability #datasharing #connectedcare

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Here’s your recap of last week’s health IT news 🗞️ 👇 🤖 AI Clinician Assistant • St Mary's Healthcare Amsterdam and 11 other health systems have now implemented Suki through its recent MEDITECH integration. 📈 Data & Analytics • Nebraska Medicine adopted Palantir Technologies to support multiple use cases, including predicting OR and PACU bed necessity, managing supply chain, and optimizing staffing changes. • MultiCare Health System deployed Xsolis’s new AI-driven Dragonfly solution to help health systems and health plans align on medical necessity and care transition decisions earlier in the process.       📋 Patient Administration • Qualtrics is partnering with Vizient, Inc to enable a new, proactive approach to measuring and managing patient experience measurement. 🔁 RCM • CorroHealth acquired the Xtend Healthcare, LLC end-to-end revenue cycle management business line from Navient. ⚙️ Clinical Operations • Duke University Health System formalized a two-year engagement with SAS, announcing two core focus areas for their collaboration: AI-powered digital twin development and ethical AI deployment. • SOL Mental Health selected Awell to improve patient access. 🗄️ Large Language Models • Hippocratic AI announced that it raised an additional $17 million as part of a Series A extension round—including participation from NVIDIA's investment arm—bringing its total funding amount to $137 million. 🧾 Orders and Fulfillment • Labcorp became a member of the CommonWell Health Alliance Qualified Health Information Network (QHIN), enabling a more seamless exchange of clinical laboratory data for providers. 🔐 Security and Compliance • Ferrum Health, Inc. announced a $16 million Series A funding round with participation from UnitedHealthcare Accelerator, Catalyst by Wellstar, and more. 🧑‍⚕️ Staffing, Consults, and Referrals • Fabric acquired TeamHealth VirtualCare to expand the capabilities and reach of its virtual healthcare service offerings for payers, employers, and health systems. • Baptist Memorial Health Care implemented ReferralMD • ShiftMed added a new capability around allied health fulfillment to its existing suite of on-demand staffing solutions. 🍎 VBC • Innovaccer is integrating Zynx Health’s content, providing clinical staff with out-of-the-box evidence-based care plans. 🧠 Food For Thought • Bain & Company’s annual healthcare IT survey reveals that ~80% of providers have increased their IT budgets in the last twelve months, with IT infrastructure/services (includes cybersecurity) and clinical workflow optimization standing out as the two highest priority IT investment areas. https://lnkd.in/g6NwdVqQ --- Want the latest healthcare tech news delivered to your inbox each week? Sign up here 📬 👉 https://lnkd.in/e2UJ3ckP

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Very interesting, insightful study. Flare Capital Partners AI in healthcare is hype. Most AI stories for healthcare are tech advancements. Not real clinical advancements or fitting into their workflows, bearing few. MedTech Innovators face these paradox. 1.Adoption requires Evidence. But evidence comes only from adoption. Clinical data can not be synthetic data for good AI. 2.Regulatory, validation requires money. But investors require regulatory, validation. 3.MedTech need faster adoption, but healthcare is slower among every profession, a cut-throat industry. They won't use it, if it is more than 2-3 mouse clicks. https://lnkd.in/gUh8sDHe

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Deidentification and Tokenization of Healthcare Data Under HIPAA: A Comprehensive Guide for Digital Health Founders The demand for healthcare data has surged as data-driven innovations continue to transform the health tech ecosystem. For digital health founders, creating a scalable and compliant model for deidentifying and tokenizing data under the Health Insurance Portability and Accountability Act (HIPAA) is essential to unlocking commercial opportunities while safeguarding patient privacy. This essay explores the technical, operational, and legal considerations for compliantly deidentifying, tokenizing, and commercializing health data. It also discusses common business models, the role of Business Associate Agreements (BAAs), and best practices for ensuring HIPAA compliance. The Importance of Deidentification and Tokenization HIPAA regulates the use and disclosure of Protected Health Information (PHI) to ensure patient privacy. Deidentification is a process to remove identifiers that could reasonably link the data back to an individual, transforming PHI into non-PHI. Once deidentified, the data is no longer subject to HIPAA, enabling secondary uses such as research, analytics, and commercialization. Tokenization, on the other hand, allows data to be pseudonymized, enabling longitudinal data linkages without directly revealing identities. Deidentification and tokenization form the backbone of data aggregation and commercialization in health tech, particularly in models involving the resale of data for research, AI training, or population health management. However, achieving compliance while maintaining data utility is complex and requires a robust understanding of HIPAA rules and technical safeguards. HIPAA-Compliant Deidentification Methods Under HIPAA, the Privacy Rule provides two pathways for deidentification: 1. Safe Harbor Method This method requires the removal of 18 specific identifiers, including names, geographic data smaller than the state level, dates directly related to an individual, and others such as Social Security Numbers, email addresses, and biometric identifiers. The key criteria are: • No actual knowledge exists that the remaining information can identify an individual. • Data must be stripped of all identifiers listed in the rule. 2. Expert Determination Method Under this method, a qualified expert applies statistical or scientific principles to assess the risk of reidentification. The expert must document that the likelihood of identifying an individual is “very small.” This method is more flexible than Safe Harbor but requires rigorous validation and expertise in statistical modeling. Continued (see bio)…

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A great read about the shift in Epic's strategy, moving from a protectionist model to a true platform. Epic's new approach of building close partnerships a select, highly curated group of tech companies coupled with the enablement of consumer apps is the winning strategy moving forward. This is a 180 degree shift from the platform strategy that Salesforce used to dominate the CRM market - being the platform that every other company must build on. The "Epic model", made possible by AI, instead allows others to build and then feed into the Epic UI. Epic is the solution that the user knows and becomes indispensable in a world where it could have easily been relegated to a database in the background. It further derisks Epic's need to build its own AI capabilities and might even lead the company to a M&A strategy in the future. https://lnkd.in/gtSXNuru

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Venture capitalists on a panel at MedCity News' INVEST conference in Chicago provided insights on the current #healthcarefunding environment, their views on AI, and why diversity still really matters. Key takeaways: 💹 The #digitalhealth funding market has more discipline, seeking founders who are paying more attention to unit economics and contribution margin and thinking critically about the milestones in their business and KPIs. 💹 Most investors want to help their portfolio companies sustainably accelerate profits and build the networks, such as #healthsystem connections, necessary for eventual commercialization. 💹 When considering #healthAI startups, a key question is asked: Is this a product or a company? AI should be an integral part of the core business rather than a trendy add-on. 💹 Diversity still really does matter. A VC on the panel said in all of the term sheets her firm signs, there is a clause that stipulates #diversity requirements for its portfolio companies. https://lnkd.in/gqsJEHBk

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Thank you for this comprehensive overview, Sam Pinson. The evolving landscape of AI governance in healthcare is critical for ensuring the ethical and effective deployment of AI solutions. Here are a few key takeaways and considerations: Regulatory Level Set 1. Data Stewardship: Compliance with HIPAA and FTC's Health Breach Notification Rule is essential for protecting patient health information (PHI). Ensuring robust data privacy and security measures is paramount for maintaining patient trust and regulatory compliance. 2. Consumer Protection: The recent FTC warning highlights the need for transparency in consumer privacy policies. AI developers must be vigilant in maintaining ethical data-sharing practices to avoid regulatory pitfalls. 3. Non-Discrimination: The new rule under Section 1557 of the ACA emphasizes the importance of mitigating risks related to AI and decision-support tools. Ensuring non-discriminatory practices in AI development and deployment is not only a regulatory requirement but also an ethical imperative. 4. Disclosures: The HTI-1 Final Rule from ONC underscores the necessity of comprehensive disclosure and risk management for predictive decision-support interventions (DSIs). Adherence to these regulations can provide a competitive edge for vendors integrating with certified EHR technology. 5. Medical Device Regulation: The FDA's regulation of software as a medical device (SaMD) remains a critical gatekeeping function. Navigating these pathways is essential for developers to ensure the safe and effective use of AI in clinical settings. 6. State Regulations: State-specific regulations, such as those in Colorado, Utah, and California, add another layer of complexity. Staying abreast of state-level requirements is crucial for comprehensive compliance. Best Practices and Compliance Developers and deployers should leverage industry roadmaps, federal agency guidelines, and emerging compliance structures to future-proof their AI governance strategies. The Executive Order 14110 and NIST Gen. AI Profile are valuable resources for developing robust governance frameworks. As we approach Q2, it will be important to continue monitoring regulatory developments and adapting governance strategies accordingly. Collaboration between industry stakeholders and regulatory bodies will be key to navigating this complex landscape and ensuring the responsible use of AI in healthcare. #AIgovernance #HealthTech #DataPrivacy #NonDiscrimination #Compliance #PatientSafety #Regulation

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There is no doubt that while virtual care has its place, it is not the solution to our healthcare dilemma. The solution involves a more hybrid model, with both in person evaluation, and care for complex issues, with the option to move into virtual care, and even automated care models based on the findings of the initial assessment. Healthcare is not a one size fits all option. You need a variety of solutions to offer a true patient experience, and a skilled provider to determine the best plan of care for your situation.

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Founders Intelligence spoke with over 30 founders, investors, experts, and corporate leaders to compile an actionable guide to help businesses achieve scale in digital health. They explore six overarching problems, bringing these to life with real-world case studies and delivering targeted recommendations for how corporates, start-ups and #healthcare providers can position themselves for success: https://accntu.re/3V35g7p

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Holidays are almost here, and so is Faces Of Digital Health Podcast special holiday episode with Lucien Engelen, Daniel Kraft, MD and Zayna Khayat, Ph.D.. What are some of the numbers we saw in 2024? ➡️ Investments in the first 3 quarters of 2024 did better than in 2024, we are yet to see the final outcome for the full year. ➡️ AI is becoming an expected feature of healthcare solutions, making it essential for companies to find new points of differentiation, ➡️ According to the report by HIMSS and Medscape clinicians see an improvement in efficiency with AI, but costs and staff hours are not decreasing, ➡️ In 2025 we will see first experiences and insights from AI quality assurance labs in the US, ➡️ Cybersecurity is... becoming a good business? Breaches are increasing, and attackers are going from keeping data hostage to data theft, ➡️ Women's health is gaining visibility and voice, with an important study by Bridget Gorham and Olivia Langham getting numbers on ROI of investments in women's health. And what does all this mean? Join our discussion on ⭐ Tuesday, 24 December, 1:30 pm CET ⭐ Click attend ➡️ https://lnkd.in/dkFnmJ8D on the Linkedin event, and be notified when the episode goes live! #digitalhealth #AI #healthcare #trends #predictions #healthtech

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AI is changing the game in healthcare. A quarter of VC investments in healthcare tech are projected to be AI-related solutions; plus, the US AI healthcare market is projected to receive $11.1 billion in 2024, up from $7.2 billion in 2023. AI isn't just a trend; it's transforming efficiency, boosting patient outcomes, and aiding drug discovery. The stats back it up. 84% of global healthcare leaders believe AI-driven automation can save clinicians' time by reducing administrative burdens, while 88% think it can help alleviate workforce shortages. The convergence of AI and healthcare is not just a promising development; it is essential for the future of value-based care. The potential impact on patient journeys, cost reduction, and overall healthcare delivery could be profound. #HealthcareInnovation #AI #DigitalHealth

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Thank you for these insights, Christian Hein. Your points about the cultural and communication gaps between health tech and pharma are spot on. Bridging these gaps is indeed crucial for effective collaboration. One strategy I've found effective is creating multidisciplinary teams that include both digital and pharma experts right from the start of any project. This ensures continuous knowledge exchange and alignment of objectives. Another approach is to establish joint training programs where digital teams can learn about pharma's regulatory and operational environment, and vice versa. Additionally, leveraging success stories and pilot projects to demonstrate the tangible benefits of digital and AI initiatives can help in gaining buy-in from key stakeholders. Clear communication of the ROI and value propositions tailored to the specific needs and pain points of the pharma business units can make a significant impact. What other strategies have you or others found effective in this space? Let's continue this conversation and share best practices to drive more successful health tech and pharma partnerships. #HealthTech #DigitalTransformation #PharmaInnovation #eHealth #DigitalHealth

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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?

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What does the health tech landscape look like at present? We were delighted to answer that question and share our views with HTN - Health Tech News on the critical issues currently shaping the industry. Along with the other contributors, we discussed various challenges and offered practical solutions for overcoming them. This is an insightful exchange that highlights the importance of collaboration and innovation in addressing these pressing concerns. 📈 Key Themes Were: - AI & Workforce Management: How AI can alleviate burnout and improve scheduling. - The role of AI: How AI plays a part in optimizing healthcare operations. - Market Consolidation: Understanding opportunities and challenges in a competitive market. - Data Security: The importance of securing patient data and improving interoperability. - Patient Engagement: Leveraging digital tools to enhance patient care. And a recurring theme is the need for collaboration and innovation, which is certainly close to our hearts. When we work together and share knowledge, we drive progress in the industry. This also includes working with healthcare providers, patients, and other stakeholders to develop solutions that meet their specific needs, not through guessing but through checking, collaborating, and using real-time data. Have a read to understand how these trends impact healthcare efficiency and patient outcomes... 👉 Read the full article here: https://bit.ly/3xBpF7m #HealthTech #HealthcareInnovation #PatientCare #AIinHealthcare #DigitalHealth

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A recent discussion with a potential B2B partner highlighted to me a different viewpoint when looking at the problem digital health product interoperability. Specifically how to make digital health products 'functionally' interoperable across different countries and local B2B partners. Lots of attention is going into data interoperability to figure out how different data streams can be combined in an efficient manner. Its no surprise why, the push to personalise patient treatments (including the use of AI / ML supported tools) relies on the ability to cross analyse discrete patient data sets from different sources. Peach Health Asia's role in commercialising partner digital health products across different channels gives us a unique opportunity to explore setting up commercial partnerships with varied B2B partners with different needs, systems and capabilities. Rather than look at this as a 'customisation' exercise doing this cost-efficiently is more an interoperability question tied to how versatile a product is in being deployed under different scenarios. One of the key parameters to address this challenge is the modularity of the product and whether its features be turned off and on without disrupting the product functionality. It isn't something that is top of mind for digital health developers when creating their products, products are usually designed for a single product-market fit. However in the international market no countries market and 'market opportunities' are exactly the same. The article below covers some of the solutions needed to deal with this challenge, such as setting up functions in microservices to enable modulatory and using more widely used API's. It is incredibly difficult and costly to customise a digital health product for every new market or opportunity, especially when dealing with products where a class I or II SaMD registration limits the ability to modify a product once it is approved. Don't forget going international also comes served with the whole data interoperability challenge, which represents another issue that needs to be sorted out when exploring bringing a digital health product into SE Asia.

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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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In our last blog, we explored the potential impact of a Trump 2.0 administration on healthcare and its implications for HealthScoreAI™. Today, we expand on that analysis, dividing this discussion into three parts: (1) recent developments in the Trump 2.0 administration, (2) the current state of healthcare, and (3) the role of AI in this evolving landscape.

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What Could the Election Mean for Data Interoperability? – MedCity News - MedCity News As the election approaches, the implications for healthcare data interoperability are profound yet often overlooked. Arcadia CEO Michael Meucci emphasizes that the administration's stance on data sharing can significantly influence care continuity, outcomes, and costs. While both presidential candidates agree on the need to combat data blocking, their approaches differ. Meucci warns that rolling back recent data sharing regulations could hinder progress and innovation. Regardless of the outcome, he remains optimistic about continued advancements in healthcare technology. Understanding these dynamics is crucial for healthcare IT professionals. #HealthcareIT #DataInteroperability #HealthTech #Innovation #Elections2024 #DataSharing #PatientCare ai.mediformatica.com #data #healthcare #datasharing #administration #this #interoperability #better #datainteroperability #election #healthcaredata #abortion #arcadia #digitalhealth #healthit #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/48r7zHK)

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