Jan Sevcik

How to improve #interoperability through #FHIR? Efficient interoperability is key to the democratization of data, but smaller EHRs have been slow to join the movement. CapMinds and Vozo have helped its #ehr clients especially smaller one to join the #fhir movement.

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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 Daga. 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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Ideally, we map #openEHR to #FHIR profiles and technically address the data capture and storage issue. Marrying the two could potentially address much of the profile proliferation issue that has yet to be solved. If we don't, what's the alternative for capturing the context and meaning of the data? Create yet another standard that must be mapped to FHIR, C-CDA, and HL7v2? There would have to be compelling reasons to do this to offset the unnecessary complexity and usability issues that would result. #StopTheMaddness #CrisisOfDesign

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

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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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Join us on June 5, 2024, at 1 PM (ET) for a webinar on tackling the reproducibility and generalizability challenges in #MachineLearning ! Learn strategies for data accessibility, leakage prevention, preprocessing, algorithm selection, and more to ensure reliable ML outcomes. Farhad Maleki Society for Imaging Informatics in Medicine (SIIM) Register: ⬇ https://lnkd.in/dqrDUg2J Objectives: - Equip participants with strategies to facilitate data accessibility, prevent data leakage, and standardize data preprocessing to enhance the reproducibility of machine learning projects. - Provide insights into selecting appropriate algorithms, promoting code sharing, and managing dependencies effectively to achieve reliable machine learning outcomes. - Advocate for the adoption of comprehensive and transparent research documentation standards to improve the clarity and reproducibility of machine learning research. - Demonstrate how to utilize advanced tools and frameworks and adhere to community standards to significantly improve the reproducibility of machine learning systems. #ML #DataScience #Healthcare #Webinar #AI

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Drug shortages threaten patients’ access to medications and are associated with adverse #healthoutcomes and increased costs. The authors propose the implementation of a value-based reimbursement system to incentivize provider selection of multi-source products manufactured in more resilient supply chains. #value #drugdevelopment #pricing #patientoutcomes #outcomesresearch #heor #healtheconomics #marketaccess #reimbursement #drugpricing #generics #amcp #managedcare #pharma #patientaccess #medicare #healthpolicy #valuebasedcare #valuebasedcontracting

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This paper discusses the need for regulatory and reimbursement frameworks that support the flexible interactions and groupings of digital health technologies (DHTs) to enhance their application in clinical settings. 1️⃣ DHTs are increasingly being grouped into packages for specific clinical tasks, creating new paradigms in medical treatment. 2️⃣ Current frameworks are insufficient for the complex interactions within DHT suites, necessitating updated regulations. 3️⃣ DHT suites refer to a collection of interconnected digital health devices and applications that are grouped together to perform specific clinical tasks in defined settings. These suites leverage the combined functionalities of individual devices to deliver comprehensive healthcare solutions. Examples of DHTs include wearables, mobile health devices, telehealth platforms, and health information technology systems. 4️⃣ The pandemic accelerated the adoption and temporary regulatory changes for DHTs, highlighting the need for permanent frameworks. 5️⃣ There are significant variations in how different countries perceive, evaluate, and reimburse DHTs. 6️⃣ Ensuring interoperability and addressing cybersecurity concerns are major challenges for the adoption of DHT suites. 7️⃣ Assessing the cost-effectiveness and gathering evidence for DHT suites introduces new complexities, especially regarding initial investments. 8️⃣ Future Directions: The article calls for new regulatory and HTA frameworks to support the innovative use of DHT suites in healthcare. ✍🏻 Rebecca Mathias, Peter McCulloch, Anastasia Chalkidou, Stephen Gilbert. Digital health technologies need regulation and reimbursement that enable flexible interactions and groupings. npj Digital Medicine. June 2024. DOI: 10.1038/s41746-024-01147-z

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So now it's a doctor's job to help their patients see the value in AI? Yeah, that ain't happening. The benefits of a solution, to any user, should be self-evident or it's back to the drawing board. #healthcare #consumerism #patients #patientexperience #physicians #doctors #nurses #clinicians #physicianburnout #ai #generativeai https://lnkd.in/gcm8FcVr

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This paper evaluates frameworks for the procurement, integration, monitoring, and evaluation of AI tools in clinical settings, focusing on mapping these to the Plan-Do-Study-Act (PDSA) cycle. 1️⃣ Plan: ▫ Assessing how AI tools can be incorporated into existing clinical workflows is critical to their successful implementation. ▫ Emphasizing the need for clear documentation and transparency about the AI tool’s functioning to build trust among users. ▫ Addressing ethical considerations, including data privacy and bias, is essential before implementing AI tools. ▫ Evaluating the financial viability of AI tools, including purchase, implementation, and maintenance costs. ▫ Ensuring AI tools meet regulatory standards and are approved by relevant authorities. ▫ Defining legal responsibilities and liabilities in case of adverse events or errors caused by AI tools. 2️⃣ Do: ▫ Successfully incorporating AI tools into the existing processes and assigning roles for their use and maintenance. ▫ Providing adequate training for end-users to effectively operate AI tools. ▫ Ensuring that AI tools are user-friendly and perceived as valuable additions to the clinical workflow. ▫ Involving a multidisciplinary team with the necessary technical and clinical expertise to support AI integration. 3️⃣ Study: ▫ Using predefined metrics like sensitivity, specificity, and accuracy to validate AI tools. ▫ Collecting feedback on user satisfaction and acceptance to identify barriers to effective use. ▫ Measuring the impact of AI tools on patient outcomes, such as morbidity and mortality rates. ▫ Establishing protocols for reporting and analyzing adverse events or system failures. 4️⃣ Act: ▫ Continuously monitoring and adjusting AI tools based on feedback and performance data to enhance their effectiveness. ✍🏻 Sarim Dawar Khan, Zahra Hoodbhoy, Raja MHR, Kim JY, Hogg HDJ, Manji AAA, et al. (2024) Frameworks for procurement, integration, monitoring, and evaluation of artificial intelligence tools in clinical settings: A systematic review. PLOS Digit Health 3(5): e0000514. DOI: 10.1371/journal.pdig.0000514

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

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👉🏼 Chat-ePRO: Development and pilot study of an electronic patient-reported outcomes system based on ChatGPT 🤓 Zikang Chen 👇🏻 https://lnkd.in/ejE_73_2 🔍 Focus on data insights: - Chat-ePRO correctly generates conversation based on PRO forms with a success rate of 95.7%. - The system accurately extracts PRO data instantaneously from conversations with a Macro-F1 score of 0.95. - Subjective evaluations from doctors (>70%) indicate that Chat-ePRO comprehends questions and consistently generates responses. 💡 Main outcomes and implications: - Chat-ePRO demonstrates a higher response rate (90%) and longer interaction time (10.86 s/turn) compared to other methods in the pilot study. - Feasibility of utilizing prompt engineering algorithms to drive Large Language Models (LLMs) for ePRO data collection tasks is validated. - The Chat-ePRO system effectively enhances patient compliance. 📚 Field significance: - Development of Chat-ePRO showcases the potential of LLMs in improving user compliance in electronic Patient-Reported Outcome (ePRO) systems. - The study highlights the importance of leveraging advanced algorithms like prompt engineering for enhancing data collection processes in healthcare settings. 🗄️: [#Chat-ePRO, #patient-reported outcomes, #data insights, #healthcare technology]

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🚀 Exciting Times in Translational Medicine: Overcoming Data Challenges with a Federated Database 🌐 As Translational Medicine Leaders, we navigate a landscape where data complexities can hinder progress and innovation. Here's how a comprehensive Federated Database can revolutionize our workflows and propel us into a new era of healthcare excellence: 🔍 **Data Fragmentation:** Combining clinical and multi-omic data from disparate sources can be a Herculean task, leading to inefficiencies and potential inaccuracies in analysis. A Federated Database consolidates over 250M patient records globally, streamlining data access and ensuring comprehensive insights. 💻 **Data Integration:** Bringing together diverse data types such as WES, WGS, RNA-seq, SNP, and other omics data is crucial for holistic patient understanding. With a Federated Database, we can seamlessly integrate these datasets, enabling in-depth analyses and cross-disciplinary collaborations. ⚖️ **Data Privacy and Security:** Protecting patient information while facilitating data sharing is a delicate balance in translational research. A Federated Database implements robust privacy protocols and encryption measures to safeguard sensitive data, fostering trust among stakeholders and compliance with regulatory standards. 🚄 **Accelerated Research:** With a vast repository of genomic and clinical data at our fingertips, Translational Medicine Leaders can expedite research timelines and make data-driven decisions with confidence. The scalability and speed of a Federated Database empower us to extract actionable insights efficiently. 🔬 **Enhanced Collaboration:** Collaboration lies at the heart of translational research success, yet siloed data can impede meaningful partnerships. By leveraging a global network of data within a Federated Database, we can break down barriers and foster cross-border collaborations that drive impactful discoveries. 💡 **Innovative Discoveries:** The wealth of data available within a Federated Database opens doors to novel discoveries and personalized treatment strategies. By harnessing the power of comprehensive genomic and multi-omic data, Translational Medicine Leaders can pioneer breakthroughs that transform patient care on a global scale. Let's embrace the opportunities that a Federated Database presents in our quest to revolutionize translational medicine. Together, we can unlock the full potential of data-driven healthcare and shape a future where precision medicine becomes a reality.🚀🔬💡 #TranslationalMedicine #DataInnovation #PrecisionHealthcare

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🚨New #JAMA Viewpoint 🚨 “Misaligned Pharmacy Incentives in Value-Based Care” The authors discuss how rising #prescription drug spending has been particularly challenging in the context of value-based contracts between payers and providers. This is partly due to confidential #rebates, which create misaligned incentives between payers/PBMs and hospital administrators. Read more below: 🔗 https://lnkd.in/dwzBT4WD #marketaccess #pbm #access #pricing #payer #valuebased #valuebasedcare #patientjourney #patientaccess #pharmacy #managedcare #pharma

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Trusted generative AI updates are in Snowflake today! ❄ More high-performing LLMs: In addition to high-performing LLMs from Mistral AI and Google, Cortex now also supports Snowflake Arctic, Meta Llama 3 (both 8B and 70B) and Reka AI Reka-Core LLMs. All of these foundation models are available via the single, easy-to use COMPLETE function ❄ Efficient RAG and semantic search using Arctic-embed: Embed and vector functions are now in public preview and include support for Arctic embed, the world’s best practical text-embedding model for retrieval. ❄ Enhanced AI Safety with Llama Guard: As part of a collaboration with Meta, Llama Guard, an LLM-based input-output safeguard model, comes natively integrated with Snowflake Arctic, and will soon be available for use with other models in Cortex LLM Functions. #genai #datacloud #aiml #opensource #snowflakeArctic #llm #snowflakeCortex

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Like an unfazed young quarterback who keeps firing it downfield despite a spate of interceptions, CMMI keeps tossing around VBC models hoping something will connect. Contrary to its intent, CMMI's programs increased spending by $5.4 billion from 2011-2020 with another increase in net spending of $1.3 billion by 2030. Of the more than 50 models the Center has implemented, only 6 have generated "statistically significant" savings. Only 4 (Home Health Value-Based Purchasing, Prior Auth Model for Non-Emergent Ambulance Transport, Diabetes Prevention Program, and Pioneer ACO) were certified for expansion. CMMI points to the voluntary nature of the programs as a challenge to achieving cost savings. However, CMS hasn't done itself any favors here by aggressively ratcheting down payments and making downside risk >>> upside risk. (Notably, two upcoming programs -- TEAM and Inpatient THA/TKA PROMs are mandatory). Finding the VBC sweet spot was always going to require some trial and error. Perhaps CMMI has learned from the programs that failed or weren't chosen for expansion. TEAM doesn't seem to be all that different from CJR. Making Care Primary aims to closely integrate primary and specialty care (or, more likely, incentivize PCPs to take on more of this work). Not surprisingly, one of the criticisms is lack of provider input. The programs are too complex, too expensive to administer, and aren't transparent. CMMI should take note of several studies demonstrating the success of physician-led VBC efforts. Here are some other thoughts: - Offer financial grants in 5- or 10-year terms for any organization that designs its own VBC program and presents a reasonable and viable clinical/business model. CMMI would do itself a favor by partnering with or at least listening to innovators. - Medicare Advantage complexity and risk adjustment handwaving shouldn't be the only mechanisms by which advanced providers can profitably care for beneficiaries. That program isn't saving money either, but it's the only way many of these approaches can generate meaningful revenue (and most still aren't profitable). - Have regular meetings with those building VBC models, particularly those with strong physician leadership. Those meetings should be transparent and productive of action plans and iterative changes that increase model viability. - Be more open about what your goals and targets are. No one should be a victim of their own success, and if you keep moving the goalposts, you shouldn't be surprised when people get frustrated and drop out. I'm certain that, given fair incentives, a meaningful voice, and an equal seat at the table, healthcare innovators can design sustainable VBC programs. But there needs to be more collaboration and a willingness to listen and engage. Otherwise, you're just slowly adding to the alphabet soup of also-ran programs. #medicine #healthcare #health #vbc #healthcareinnovation #valuebasedcare

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