Dominique H.

Oracle Health announced plans to launch a next-generation EHR platform in 2025, incorporating advanced cloud and AI capabilities. This new EHR will feature Oracle's clinical AI agent, voice-activated navigation, and Health Data Intelligence, which integrates patient data from over 300 sources to enhance clinical workflows and improve patient outcomes. The platform aims to replace traditional EHRs, which often dictate rigid workflows, with a more dynamic system that allows for personalized care plans. Oracle Health emphasizes that the new EHR is a complete reinvention rather than an upgrade of the previous Cerner system. Additionally, Oracle plans to become a Qualified Health Information Network to facilitate standardized data sharing across the healthcare ecosystem. Executives at Oracle also unveiled new cloud applications to improve patient management and streamline front-office operations, reinforcing the company's commitment to enhancing healthcare accessibility and data utility while addressing interoperability challenges in the industry.

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Navigating Real-World Evidence for Health Technology Assessment: Key Insights from Industry Experts 📊🔍 *The recent webinar featuring insights from April Betts (UCB), Stephen J. Duffield (NICE), and Rachel Cummings (Decisive Consulting) provided an in-depth look into the evolving role of real-world evidence (RWE) in health technology assessments (HTAs). Here are the key takeaways: ✔ Evolution and Importance of RWE: RWE is becoming a staple in healthcare, impacting regulatory decisions and market access. It addresses gaps inherent in randomized controlled trials (RCTs) by providing data on larger populations and real-world practices. 🌐🧬 ✔ Challenges and Opportunities: The integration of RWE faces challenges such as data quality, relevance, and regulatory acceptance. However, it presents opportunities to enhance evidence for HTAs, especially in demonstrating long-term effectiveness and safety. 🚧📈 ✔ Strategic Applications: RWE is critical in areas like cost-effectiveness analyses and can influence the decision-making processes of bodies like NICE. Effective RWE strategies involve early planning and continuous adaptation to evolving healthcare landscapes. 📝💡 ✔ Collaborative Efforts: Successful RWE utilization requires collaboration across various stakeholders, including academia, industry, and regulatory bodies. This collaboration helps ensure that RWE meets diverse regulatory requirements and adds value to HTA submissions. 🤝🌍 ✔ Future Directions: The use of RWE is expected to grow, with increasing sophistication in data analytics and broader regulatory acceptance. Stakeholders must remain agile and proactive in leveraging RWE to inform healthcare decisions and policies. 🔮📊 *These discussions highlight the critical role of RWE in shaping the future of healthcare decision-making, underscoring the need for strategic alignment and robust methodologies in its application. #HealthcareInnovation #RealWorldEvidence #HTA #Pharmaceuticals #HealthTech

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An interesting article that highlights the importance of aligning strategies with value-based care #VBC goals to enhance patient outcomes and operational efficiency. One critical aspect is #datadrivencare management. Utilizing analytics platforms to aggregate and analyze data from sources like electronic health records #EHRs allows for population health analysis, risk stratification, and tracking quality metrics, all of which contribute to improving care quality. Additionally, placing proactive #preventivecare at the center of your strategy is essential. Implementing wellness visits, chronic disease management, and timely immunizations can boost overall health while reducing long-term costs. Another vital factor is enhancing #carecoordination. Developing care transition protocols, streamlining provider communication, and creating multidisciplinary care teams help ensure a seamless patient journey and better outcomes. Furthermore, increasing #patientengagement and education plays a crucial role. Addressing #socialdeterminants of health is also key. Screening for social challenges, building a community resource database, and ensuring proper referrals and follow-ups are necessary steps to support overall patient well-being. Finally, building #partnerships and aligning incentives with payers, community organizations, and technology vendors is essential. Collaborative efforts to align goals, share data, and explore innovative solutions lead to better patient care and organizational performance. By integrating these strategies into your healthcare business approach, you can effectively align with VBC goals and enhance #patientcare. #healthcarestrategy #resourceoptimization #performanceincentives #stafftraining #healthcareinnovation #hbr #leadership #strategy #lifelonglearner #digitaltransformation #innovations

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Predictions for the Healthcare Analytics Space in 2025 Increased Integration Across Data Ecosystems As value-based care models continue to expand, seamless data integration across clinical, claims, and social determinants of health (SDOH) sources will become critical. Organizations will prioritize interoperability through networks like QHINs, Carequality, and CommonWell to create a unified patient view. Interoperability Will Become Non-Negotiable Regulatory and operational pressures will force healthcare organizations to embrace true interoperability, with frameworks like TEFCA, QHINs, and platforms like Carequality/CommonWell gaining momentum. Seamless data exchange will shift from a competitive edge to a baseline requirement. Shift from Retrospective to Predictive Analytics Organizations will move away from merely reporting past events and toward anticipating future risks, costs, and care needs. Predictive models will guide both operational efficiencies and patient care interventions. Demand for Transparent ROI Healthcare organizations will increasingly demand clear ROI from analytics investments, driven by financial pressures and accountability in value-based contracts. Solutions must demonstrate tangible outcomes like cost savings, care improvements, and enhanced risk stratification. Rise of Decentralized and Multi-Tenant Data Repositories As data ownership and governance become central, healthcare providers will adopt multi-tenant architectures to securely store and access aggregated data while maintaining control over proprietary information. AI and Machine Learning in Care Pathways(*) The use of AI to identify care gaps, optimize workflows, and automate routine tasks will grow exponentially. Providers will seek analytics solutions incorporating explainable AI to maintain trust and compliance. (*) Steep adoption curve Focus on SDOH for Equitable Healthcare Delivery Payers and providers will deepen their focus on SDOH to address health inequities. Analytics platforms will need to integrate SDOH data into actionable strategies for population health management. Emergence of Modular and Workflow-Specific Tools Comprehensive dashboards will give way to modular, workflow-specific tools that empower users to interact directly with data. These tools will emphasize usability and decision-making at the point of need. Partnerships Over Proprietary Solutions Collaboration between analytics providers and adjacent industries (e.g., tech, pharma, and public health) will drive innovation and adoption. Open ecosystems will gain traction over closed, proprietary platforms.

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My colleagues, James R. Bush, Adarsh Gosu and I were honored to lead an Executive Roundtable at the #BeckersAnnualMeeting last month. Leaders discussed the capabilities of Generative AI in health care, emphasizing the trust, governance, data modernization, and workforce training. Some highlights included: ☑ Establishing effective governance is crucial for successful AI implementation. ☑ The importance of change management can’t be overlooked. ☑ Building consumer trust and engagement is essential in AI adoption. ☑ Workforce upskilling is necessary for AI fluency and successful implementation. ☑ Designing AI solutions with scalability in mind is a must. A special thanks to Becker's Healthcare for the opportunity! Read a recap of our conversations on Beckers Hospital Review. https://lnkd.in/gEVWxwzq Want to learn more about Deloitte guidance for the potential of #AI in healthcare? We have a report with critical insights about implementation and governance. https://lnkd.in/gXE_HVG2 Bill Fera, Tina Wheeler, Adarsh Gosu, Dan Kinsella, Anne Phelps #AIinHealthCare #GenAI #FutureofHealth #Deloitte #hospitals #HealthIT *Information posted here is a personal opinion and is not a substitute for medical advice. Shared publications contain general information only and Deloitte is not, by means of this publication, rendering accounting, business, financial, investment, legal, tax, or other professional advice or services.

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🌟 Generative AI and Healthcare Transformation: A New Era of Innovation 🌟 I am constantly asked about #AI applications for healthcare transformation. I continue to take people back to the pain points or problems being solved. The healthcare industry is undergoing a significant transformation, driven by the integration of generative AI. This revolutionary technology is currently reshaping the way we approach care management, early disease detection, and administrative tasks. Early Benefits of Generative AI: Enhanced Care Management: AI serves as a "co-pilot" for healthcare professionals, assisting in managing patient care more efficiently and accurately. This leads to improved patient outcomes and more personalized care plans. Early Disease Detection: AI's ability to analyze vast amounts of unstructured data enables early identification of diseases, potentially saving lives and reducing healthcare costs. It's a game-changer in preventive healthcare. Reducing Clinician Burnout: By automating repetitive administrative tasks, AI allows clinicians to focus more on patient care. This not only improves job satisfaction but also enhances the quality of care provided. Affordability and Efficiency: AI-driven processes streamline operations, making healthcare more affordable and accessible. From optimizing workflows to reducing operational costs, AI is paving the way for a more sustainable healthcare system. Challenges and Considerations: Ethical and Legal Frameworks: Ensuring AI is used responsibly requires robust ethical, legal, and policy frameworks. Trustworthy AI adoption is crucial for patient safety and health equity. Technology Infrastructure: Organizations need to update their technology infrastructure, including cloud capabilities, to fully support AI implementation. As we embrace these advancements, it's essential to stay informed and engaged in the conversation about how AI can continue to transform healthcare for the better. #HealthcareTransformation #GenerativeAI #HealthTech #Innovation #AIinHealthcare Thanks to sources McKinsey & Company (https://lnkd.in/g7HZHEST) and MedCity News (https://lnkd.in/gi95t4qJ)

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Large Healthcare Technology Consulting Firms vs. Smaller Specialized Consultancies As I have discussed in previous posts, the field of technology consulting, and healthcare technology consulting in particular, is currently experiencing a downturn. Year-over-year revenues for healthcare consulting are declining, and this situation is especially challenging for smaller firms compared to their larger counterparts. Larger consultancies have the advantage of deeper cash reserves, allowing them to endure economic downturns with greater ease than smaller boutique firms. However, it is important to remember the reasons you have chosen to work with smaller firms in the past. Their specialized expertise in areas such as Revenue Cycle Management, Acute Care, or Clinical Systems Interoperability has been directly relevant to the specific issues you have encountered with your systems. Larger firms often apply generic solutions that may not be the best fit for your unique needs. Therefore, it is crucial to maintain relationships with your boutique vendors and support them whenever possible. If these specialized firms disappear, you might find yourself without the precise expertise you need when challenges arise.

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Microsoft has introduced advanced AI capabilities for healthcare organizations through Microsoft Cloud for Healthcare. This includes AI models in Azure AI Studio, which facilitate the integration and analysis of diverse medical data types such as imaging, genomics, and clinical records. Additionally, Microsoft Fabric enhances healthcare data solutions, allowing organizations to harmonize various datasets and perform care management analytics. The healthcare agent service in Copilot Studio automates tasks like appointment scheduling, while an AI-driven nursing workflow solution streamlines documentation, ultimately improving patient care and operational efficiency.

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My colleague Rafa Arranz ❄️ shows how to talk to your clinical data safely, securely, and without hallucinations using Snowflake Cortex Analyst. I'll always have a spot in my heart for data visualizations, but directly asking for insights from vast data is where the world is today thanks to ChatGPT. #Snowflake #AIDataCloud #snowflakeCortex #healthcare #healthdata #healthtech

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Interesting piece by #JAMA on the FDA's perspective on #AI in healthcare and biomedicine. It highlights how AI presents challenges but also great opportunities to impact health outcomes. The opportunities are immense as it relates to improving clinical research, drug discovery, drug development, clinical trial support, post-marketing surveillance, etc. What is essential however is the need to have optimized tools, regulation and established frameworks to ensure that AI models are safe and effective across diverse settings. This is an exciting time for ongoing AI innovation!

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🏥✨ For many years, technology investments made by health organizations were rarely a driving force behind strategic #transactions. Explore in my latest article with Sam Prescott how #generativeAI has changed the game for Health CIOs. A must-read for leaders at the forefront of change! https://lnkd.in/gRWmi5AH

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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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Learn about ethical and practical integration of AI, focusing on creating systems that improve patient care without compromising physician autonomy or patient privacy from speaker Jesse Ehrenfeld MD MPH at AIMed24. He advocates for responsible AI use that supports clinical decision-making, enhances patient safety, and alleviates administrative burdens on healthcare providers. #AIMed24

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Be part of the conversation shaping the future of Health Data Utilities! The HDU Framework Supplement Version 1, developed by Civitas Networks for Health, is now open for public comment. This updated draft guidance outlines key strategies for leveraging Health Data Utilities to advance interoperability, equity, and responsible health data governance. If you want to shape the US's future of health care and public health then read it and submit your feedback by January 31, 2025. Access the Supplement: https://lnkd.in/g2X9rE6n content/uploads/2024/12/HDU-Supplement-Version-2.0-Designed.pdf Submit Feedback: https://lnkd.in/gYFGRREA #Interoperability #HealthData #PublicHealth

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Payers should consider joining a QHIN now. As an alternative to creating one-off DUAs and direct integrations with health systems, plans may opt to join a designated QHIN under TEFCA to support data exchange on a broader scale and gain access to accurate clinical data more quickly and affordably. When plans take steps to onboard with a QHIN, they should ensure their provider contracts specify a response to all QHIN queries for data, not just for treatment and individual access exchange purposes.

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👉 The Imperative for an AI Council in Healthcare and Life Sciences   Reflecting on the past 18 months, I've had the privilege of guiding numerous customers, particularly in healthcare and life sciences, as they embarked on their GenAI odyssey. These customers’ GenAI adoption journeys varied wildly: from POCs to high-level strategy engagements with McKinsey/BCG, from ProServe & Partner-led app development across LoBs to simply brainstorming use cases. Some grappled with 'analysis paralysis', while others flatly denied GenAI's relevance to their firm/industry.   Interestingly, a common thread emerged among those making real headway: the presence of an AI Council. And here's the kicker - the more a company faced regulatory hurdles, compliance issues, data security concerns, organizational structure complexities, and intricate domain complexities, the more crucial this AI Council became. It served as the compass, steering the organization and its cross-functional teams through the choppy waters of GenAI transformation.   The council is vital for mitigating risks associated with AI implementation, such as privacy concerns, data bias, and ethical dilemmas. They also facilitate change management, driving collaboration across departments and fostering a culture of responsible AI adoption. In essence, an AI Council in healthcare and life sciences is responsible for: 1/ Developing comprehensive AI strategies and governance frameworks. 2/ Managing risks associated with AI, including ethical considerations, data privacy, and regulatory compliance. 3/ Fostering cross-functional collaboration to overcome silos and drive organization-wide AI transformation. 4/ Educating and empowering stakeholders to navigate the complexities of AI and its implications in healthcare and life sciences. 5/ Prioritizing AI initiatives based on strategic alignment, potential impact, and resource requirements   By addressing these critical aspects, an AI Council enables healthcare and life sciences organizations to harness AI's potential, transforming patient care and scientific research while minimizing risks and challenges.   So, how can healthcare and life sciences organizations establish effective AI Councils? Stay tuned for my next post, where I'll dive into the key steps and best practices! 📢 Subscribe to my newsletter to get access to strategies and practical guidance on accelerating adoption of generative AI within your organization. Get started here: https://lnkd.in/g3bdneR7 #genai #ai #aws #AIinHealthcare #LifeSciences #AIGovernance #HealthTech #Innovation

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For decades, siloed data systems slowed adverse drug reporting to a crawl. Now national healthcare organizations can access the data they need in real time. Here’s how #FHIR made it possible:   The #interoperability standard plays an essential role in Project BEST, an initiative from the FDA and eHealth Exchange that automates adverse drug event reporting and improves patient safety. I recently had the pleasure of joining Jay Nakashima, president of eHealth Exchange, on a HIMSSCast to discuss how it came to life.   Jay and I explain how the U.S. Department of Veterans Affairs tapped the project to build a cutting-edge system for delivering notifications about adverse events tied to biologics and vaccinations. It replaced old approaches to information sharing with a speedy connection to drug data. And it opened the door to the robust surveillance needed to protect the people who serve our nation.   As FHIR continues to enable new workflows, unprecedented use cases abound. The only question: What thorny #healthcare challenge will you tackle with the standard next?   Listen to our discussion here: https://lnkd.in/ey6WkbmA   HIMSS #interoperability #healthcare #healthdata #FHIR #healthIT

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