Vikram Singh

💰🤖 Making the Financial Case for Generative AI in Healthcare and Life Sciences Generative AI has the potential to revolutionize the healthcare and life sciences industries, particularly when it comes to streamlining complex back-office operational workflows. Business and technology leaders can make a compelling financial case for investing in these solutions by highlighting their impact on Net Present Value (NPV) and overall efficiency. Here is how to get started: 1. 🔎 Assessing Current Workflows: Begin by evaluating existing back-office processes that have become overly complex and require significant human intervention. Identify areas where generative AI can help simplify, automate, and optimize these workflows, reducing the need for manual involvement. 2. 💵 Projecting Cost Savings: Estimate the potential cost savings from implementing generative AI solutions, including reduced labor expenses, increased productivity, and lower error rates. Be sure to consider factors like implementation costs, training, and ongoing maintenance when calculating overall savings. 3. 📊 Calculating NPV: Apply the Net Present Value (NPV) method to assess the financial viability of your proposed generative AI investments. By discounting future cash flows to their present value, NPV helps determine whether the projected financial benefits outweigh the initial investment and ongoing costs. 4. 🌟 Highlighting Long-Term Benefits: In addition to cost savings, emphasize the long-term strategic advantages of adopting generative AI for your firm. These may include increased agility, enhanced innovation capabilities, and improved competitiveness in the market. 5. 📚 Building a Compelling Case: Combine the financial analysis with a narrative that illustrates the transformative potential of generative AI for your organization. A well-crafted financial case, supported by data-driven projections and strategic insights, will help secure buy-in from key stakeholders. As always, collaborating with your technology and GSI partners can be incredibly valuable when going through business validation exercises. Your partners likely have specialized expertise, tools, and resources that can complement your own capabilities and accelerate the progress. By tapping into their knowledge and support, you can accelerate the exercise, overcome obstacles, and increase your chances of securing funding. 💬 Share your experiences and strategies for building a financial case for generative AI investments in healthcare and life sciences below! 📢 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 #GenerativeAI #FinancialCase #HealthcareInnovation #LifeSciences #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #healthcare #patientcare #ai #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess

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Thoughtful planning in the establishment of a commercial data strategy prior to brand launch can help to frame the approach to patient-centricity. Data warehouses are a lot of work to rebuild, the tips in this Beghou Consulting article help to provide four keys to inform that planning so that patients stay at the center of focus. #data #pharma #biotech #patient-centered

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Great insight by my colleague David Devine 🚀 Transforming Healthcare with Data Analytics 🚀 The North American healthcare analytics market is projected to soar to $11.4 billion by 2023, up from $3.13 billion in 2018. Despite this growth, many healthcare providers are still catching up with the advancements seen in other sectors, such as medical devices and payers. Key Highlights: Real-Time Decision Making: Leveraging basic patient data to make immediate care decisions. Predictive Analytics: Using machine learning to predict and reduce hospital readmissions, saving billions annually. Combating the Opioid Crisis: Data tools are crucial in monitoring e-prescribing practices and integrating with state prescription drug monitoring programs. Future Strategies: Moving beyond traditional data sets to include less structured data, such as genomic and social data, for a comprehensive analysis. Healthcare leaders must think, plan, and act differently to harness the full potential of data analytics. By advancing data strategies and focusing on both structured and unstructured data, we can transform patient care and outcomes. Let’s embrace the power of data to create a more efficient, patient-centric healthcare system! 🌟 https://lnkd.in/ebu35PAh #Healthcare #DataAnalytics #MachineLearning #AI #BigData #HealthTech

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The webinar was excellent✔ & content that applies to every healthcare segment💡 #AI to #Transform your #revenue #collectionprocess many talk about these solutions but how many can deliver❓ https://bloomvalue.com/ Brian Mericle Aspire Health Innovations

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Assessing the quality and relevance of healthcare data can be tedious and ineffective, but it doesn’t have to be. Effortlessly unlock exclusive #RealWorldData and gain rapid, streamlined analytic capabilities. This empowers teams to efficiently generate reliable insights, from data landscaping to real-world evidence generation: https://bit.ly/3ONr58E

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🥂Celebrating Visionary Leaders and Encouraging Action 🚀 McKinsey estimates that Generative AI (GenAI) could generate $60B-$110B a year in economic value for pharma/medical products by boosting productivity, across drug compound identification, development & approval processes, and optimizing product marketing strategies - https://lnkd.in/efJ9Wkrx Many leading healthcare/life sciences organizations are starting to prioritize GenAI education and enablement for their teams. Over the last few week, I have had the pleasure of interacting with a few such visionary leaders who emphasized the importance of up-skilling their teams on GenAI. These leaders were happy to carve out half a day of their and their team’s time to understand GenAI, see multi-modal genAI in action, discuss GenAI hype vs reality, review GenAI adoption journey, and discuss areas where LoB and IT can start adoption exploration for their first MVP (minimal viable product). I couldn't agree more with this forward-thinking approach, and would like to share some action items to inspire leaders, across industries, to follow suit! Ask for your technology and/or GSI partners to conduct ‘Innovation w/ GenAI’ workshops with your cross-functional teams of LoB (line of business) and IT professionals on the fundamentals of GenAI and its various applications within your industry. Make sure these workshops are NOT designed as presentations or marketing services, but an interactive dialog where your teams can ask questions, see real world applications on how GenAI is being used, discuss sensitive topics, understand what a typical GenAI journey looks like, and get to understand the change management aspect of the adoption journey. Next, allow for collaboration between IT and business professionals to exchange ideas and explore GenAI-driven solutions for optimizing tasks and workflows for their most time consuming manual processes. I like to facilitate such sessions using design thinking constructs to allow for collective ideation, grouping, and voting to identify the best candidates for an MVP (minimal viable product). Important side note – I discourage POCs now because the technology has been proven and now being adopted at scale across industries. It’s time for action and accelerated adoption, so start with a MVP and iteratively develop/scale your GenAI application. Finally, provide a safe environment for experimenting with GenAI tools and techniques, allowing teams to develop solutions and evaluate their potential benefits. By setting time/scope/budgetary guardrails you can control the blast radius for safe experimentation and foster a learning culture for accelerated adoption. 💬 How are you enabling your cross-functional teams on GenAI adoption? 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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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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🏥✨ 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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#insights you expect from #Axtria, in this case from ISPOR. Key takeaways highlighted include: - The growing acceptance of causal inference methodologies in real-world evidence (RWE) - The transformative impact of Generative AI - The use of novel, real-world data (RWD) - Digital Twins

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Thoughts on this? >> Is Your LLM Agent Enterprise-Ready? Salesforce AI Research Introduces CRMArena: A Novel AI Benchmark Designed to Evaluate AI Agents on Realistic Tasks Grounded on Professional Work Environments >> Comment below! >>> lqventures.com #mhealth #AI #IoT #industry40 #healthtech

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🚀 Upscaling Generative AI as an Enterprise Platform 🥌 In my experience, adopting Generative AI (GenAI) transcends a mere application choice; it represents a strategic enterprise-wide platform decision that demands careful consideration and alignment with organizational goals. As enterprises embark on their GenAI journey, it's vital to treat scaling efforts with the same strategic approach as previous enterprise-wide initiatives, like cloud adoption. To successfully scale GenAI, consider forming a steering committee that brings together cross-functional stakeholders. This committee will oversee strategic planning, resource allocation, and change management, ensuring a holistic approach to adoption. Have you been part of a steering committee for enterprise-wide initiatives? If so, you will relate with this approach. Looking back at cloud adoption a decade ago, we can draw parallels to today's GenAI landscape. Just as organizations needed to re-architect their IT infrastructure and embrace new workflows for cloud integration, scaling GenAI requires a similar mindset shift and strategic planning. We should start by looking at business processes. Engage with stakeholders from various LoB (lines of business)—clinical, IT, and business—to foster cross-functional collaboration and gather diverse input. Just like we had to explain the business benefits of cloud to everyone during cloud adoption, scaling GenAI warrants similar motion. Educate & Communicate. Don't make assumptions. Adopt an agile approach to development, testing, and deployment, allowing for continuous learning and improvement as you scale GenAI applications. There is so much to be done. My preferred execution model is Scaled Agile Framework (SAFe) Program Increment planning. The goal is to align everyone on a shared vision, prioritize features, identify dependencies, and create a detailed plan for the upcoming Program Increment (PI), which is a time box of 8-12 weeks. Lastly, don't forget the importance of data governance, quality and privacy. Address any challenges and comply with regulations to build trust and pave the way for successful adoption. I have witnessed numerous organizations postponing implementing a comprehensive data strategy and robust data governance practices during previous transformation initiatives, such as cloud adoption. I would strongly advise against repeating these oversights, as they can hinder long-term success and impede the realization of desired outcomes. 💬 Do share your experiences and insights on GenAI adoption in the comments below! 📢 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 #cloud #data

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Healthcare and life science data and analytics leaders are increasingly influencing the strategic direction of organizations. Dive into the 5 major impact areas of emerging technologies, and how they are shaping data, analytics and AI initiatives. Gartner for IT | #GartnerDA #GenerativeAI #GenAI

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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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🚀 Getting Started with Data Mesh Architecture: A Pragmatic Approach   Continuing our AI-ready data infrastructure theme from last week, this week we discussed the benefits of Data Mesh architecture for healthcare and life sciences organizations (https://lnkd.in/eSUPe3zv), and also dove deeper into how your investments in your Data Lake architecture can be leveraged to transition towards a Data Mesh architecture (https://lnkd.in/excAhdqq). That said, how should you plan to get started? What are the first few steps to start building momentum for you to reach escape velocity on this journey?   Transformation at scale isn't a single big move but many small, purpose-driven, well-calibrated steps, factoring in mis-steps, learnings, and enablement, creating momentum for sustainable change and growth. It’s best achieved through POCs to prove technology and MVPs when committing to the change journey.   Consider these steps when transitioning to a Data Mesh architecture: 1/ Outline the benefits, costs, and risks of Data Mesh implementation. Align with strategic objectives and data initiatives. Seek partner help to build a business case demonstrating ROI and NPV. 2/ Evaluate existing data processes and pain points. Identify areas for improvement using Data Mesh principles. Leverage partner assessments to understand your current architecture, prioritize LOBs, and mitigate risks. Avoid rushed or free assessments that may not address your unique needs. 3/ Develop a phased roadmap focusing on high-impact initiatives. Consider tooling, change management, and budget. Use an iterative MVP approach to deliver value quickly and refine based on feedback. Start small, learn, and repeat. If using AWS, consider their EBA (Experience Based Acceleration) mechanism for cloud transformation. Read more about EBAs here - https://lnkd.in/e_hc8Zwr   Next, develop a federated governance framework with global data standards and domain-specific policies. Align data quality, security, and privacy with industry regulations and best practices. Again, don’t boil the ocean. Focus on the pilot's scope and iteratively build this framework for your organization.   Finally, do not forget to communicate Data Mesh benefits to stakeholders, involving them in the transformation. Address concerns through effective communication and training. Form cross-functional teams with diverse skills for each domain, empowering them as change agents for their respective LoBs. 💬 What challenges do you foresee in your data mesh transformation journey? 📢 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 #datamesh #data #datatransformation

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I'm excited to share the next episode of "Life Sciences DNA" podcast series about importance of Data Analytics and AI in behavioral health with Eze Abosi, the Chief Growth Officer of Holmusk. Agilisium Consulting #MentalHealth #AI #DataAnalytics #Pharma #BehavioralHealth

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New Survey Reveals Healthcare's Embrace of Generative AI The 2024 Generative AI in Healthcare Survey highlights both the promise and challenges of GenAI adoption in the healthcare sector. The findings reveal a significant shift in budget allocation, with 34% of respondents reporting a 10-50% increase for GenAI initiatives. Some technical leaders even saw budget increases of up to 300%, underscoring the industry's commitment to this technology. Healthcare organizations are showing a preference for custom-built, task-specific language models over general-purpose LLMs, with 36% opting for this approach. The top use cases for GenAI in healthcare include answering patient questions, medical chatbots, and information extraction, demonstrating the technology's potential to enhance patient care and streamline operations. Although enthusiasm is high, the industry faces several adoption hurdles. Concerns about accuracy, legal risks, and the need for industry-specific solutions remain significant challenges. To address these issues, "human in the loop" remains the most common strategy for testing and improving LLM models, emphasizing the continued importance of human oversight in AI implementation. Notwithstanding these challenges, the healthcare industry is clearly committed to GenAI. As organizations continue to explore and implement these solutions, collaboration between technical experts and domain specialists will be key to unlocking GenAI's potential to improve patient care, streamline operations, and accelerate research. https://lnkd.in/eB_YbPJG #GenerativeAI #Healthcare #AIAdoption #Trendigm #HealthTech Trendigm | Enabling Innovation

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🌐💊Data Mesh Architecture: Fueling GenAI in Healthcare and Life Sciences 🚀   Building on our discussion about AI-ready data infrastructure from last week (newsletter - https://lnkd.in/erFjd527), let's explore why healthcare and life sciences (HCLS) organizations must revolutionize their data management for advanced GenAI applications. These sectors face unique challenges requiring robust, adaptable data systems. They handle diverse data types - clinical, genomic, research, and operational - while navigating strict regulations and fostering innovation. Real-time, scalable insights are crucial for critical decision-making. Empowering stakeholders to analyze data independently and providing quality, diverse training data for AI/ML models are essential. These factors underscore the need for infrastructure supporting next-gen AI-driven healthcare solutions.   While various data architectures have tried to address these challenges, data mesh stands out in the HCLS industry by decentralizing data ownership and treating data as a product. It empowers domain teams to manage their own data pipelines independently. This approach aligns well with the industry's diverse needs, where departments handle various types of data from clinical to operational.   Key Principles of Data Mesh architecture are - 1/ Assign data ownership to specific business domains, ensuring contextual management. 2/ Domains create and maintain discoverable, accessible data products for other teams. 3/ Federated governance ensures consistent quality, security, and privacy standards across domains, while preserving flexibility.   How will data mesh help HCLS firms adopt GenAI applications? It ensures diverse, quality data is discoverable for GenAI training and RAG solutions. Distributed ownership enables domain-specific security, reducing vulnerabilities. Data producers' quality responsibility enhances GenAI performance. Self-service access accelerates experimentation, speeding GenAI MVPs and innovation.   However, note that Data Mesh is complex and challenging to implement. It demands organizational change, executive support, and budget. Challenges include decentralized governance, higher skill needs in business units, less central control, increased complexity and costs, and potential tool sprawl. Careful planning is crucial for success.   Consult your technical partners, GSIs, and consultants for more on data mesh. Explore workshops to assess your data landscape, readiness for data mesh, and build a business case for executive support and budget allocation.   💬 How ready is your organization to adopt a data mesh architecture, and what specific challenges do you foresee in implementing this approach at your firm? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #data #datamesh

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How is generative #AI reshaping the pharmaceutical industry? Explore the strategic challenges and immense opportunities according to @Merck's Director of Data Science. Read now ➡️https://t.co/kM1zQz9Jjr #DataManagement #DataStrategy #Data #Analytics #CDO #DataPlatform #GenAI

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Can strategic M&As create value in #Healthcare? 🌐 In the dynamic landscape of healthcare, mergers and acquisitions play a pivotal role in shaping the market structure and enhancing competitive advantage. The trend towards consolidating healthcare #Technologies and services into integrated platforms is accelerating. This is driven by the need for comprehensive solutions that streamline processes and reduce costs. For Investors, these strategic M&As present a valuable opportunity to fund ventures that are set to reshape their markets. 💡Investing in M&As within the healthcare sector allows investors to support efficiency and #Innovation that can lead to high returns. The consolidation of point solutions into larger, more capable platforms simplifies technology management for healthcare providers. This not only increases potential market capture and customer loyalty but also enhances investor returns. https://lnkd.in/e78tc58S #FutureofHealthcare #InvestmentOpportunity #SurgeryInnovation Bjoern von Siemens Dennis Kogan Matt Krueger Amel Mokrani Bois Etienne Bauer Gargee Kashyap Emre kamil Özkan

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