Praveen Soti, MD, MBA

Honored to have hosted an inspiring panel at our HLS Spring event, where five industry captains shared their insights on how to advance AI-led transformation in healthcare and life sciences. Our esteemed leaders, Aalpen Patel, MD, MBA, FSIR, Peter Slavin, MD, Neil Gomes, Manish Varma, and Shub Bhowmick, gave us the 10 commandments to drive impact and innovation. Their top 5 recommendations: 1. Embrace the transformative power of AI/Gen AI and push the envelope on innovation. 2. Democratize the use of AI and Gen AI platforms and ecosystems. 3. Let AI handle the mundane tasks while you focus on what matters. 4. Educate everyone on the possibilities and be an evangelist for the new Gen AI capabilities. 5. Focus on core business problems, measure value and innovate. accordingly. Their 5 life experiences: 1. Stay curious and question the status quo. 2. Take measured risks and play on your pitch. 3. Stay focused on core business problems and don't get distracted. 4. Have a multi-disciplinary approach to solving problems. 5. Don't take life too seriously and bring an outside-in perspective. Let's keep the conversation going and drive AI-chassis for Healthcare and Life Sciences. #Tredence #Beyondpossible #HealthcareAI #SettingAIChassis

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💡📈 Building a Strong Business Case for GenAI Investment Few Healthcare and Life Sciences organizations have begun adopting GenAI across their operations, and are already seeing tangible benefits (https://shorturl.at/fgrPZ). Initial reported use cases are focused on efficiently extracting and searching for relevant information within notes, reports, and other documents, and creating personal employee productivity tasks (https://shorturl.at/beqR1). Adoption of GenAI is particularly high in the areas of operations, research, and administrative tasks. 🙄 While GenAI adoption is surging, many use cases continue to mimic the user experience of ChatGPT. It's almost as if the industry is being constrained by the first mover, rather than exploring the full potential of these powerful AI models. Still, most firms continue to struggle to convince stakeholders to invest in GenAI. As leaders, you need to be able to showcase its potential impact on your organization and balance implementation costs against the cost of maintaining the status quo. Here are key points to consider: 1. 💰 Cost Comparison: Analyze the upfront and ongoing costs of implementing GenAI versus maintaining current processes, highlighting potential long-term savings and benefits. 2. 📊 Return on Investment (ROI): Calculate the overall financial return from implementing GenAI, considering factors like cost savings, productivity gains, and revenue increases. Use this data to justify the investment. 3. 🏆 Value-Driven Metrics: Focus on metrics that demonstrate GenAI's impact on business value, such as increased customer satisfaction, improved decision-making, and enhanced product/service quality. 4. 📲 Scalability and Flexibility: Evaluate GenAI's ability to scale alongside your business and adapt to changing needs, enabling future growth and cost savings through automation and efficiency gains. 5. 🛡 Risk Mitigation: Assess potential risks and challenges associated with GenAI implementation, such as integration issues or data privacy concerns. Develop strategies to mitigate these risks and ensure a successful rollout. Five-Step Framework for Building a GenAI Business Case: 🕵‍♀️ Identify Opportunities: Pinpoint areas that can benefit from GenAI, focusing on tasks or processes that are time-consuming, repetitive, or require creative input. 📊 Estimate Impact: Quantify the potential benefits of GenAI, using key metrics to support your estimates. 🚧 Assess Risks and Challenges: Identify potential risks and challenges and develop strategies to mitigate them. 🗺 Develop a Roadmap: Create a detailed implementation roadmap that outlines the steps required to integrate GenAI into your organization. 🔍 Monitor and Evaluate: Measure the success of your GenAI implementation using key metrics and success indicators, adjusting your strategy as needed. 💬 Share your experiences on building a business case for GenAI! 📢 Subscribe to my newsletter here: https://lnkd.in/g3bdneR7 #genai #ai #aws

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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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🚀 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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I am grateful to the Forbes Tech Council for featuring me in their latest expert panel on “Innovative Data Management.” AI is unlocking vast human potential in health plan operations by streamlining manual tasks, and provider data management serves as a compelling case study for this evolution. The downstream impacts of AI’s utilization in areas like roster automation, mining provider legal contracts and improving directory accuracy are profound and hold significant implications for the entire healthcare industry. As roster ingestion becomes fully automated and directory accuracy improves, we are witnessing firsthand how AI creates space for humans to engage in higher-order thinking. Generative AI is only going to accelerate this trend, and I am proud that HiLabs is part of that innovation

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💰🤖 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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🌐 The future of healthcare is being shaped by #AI, and your voice matters! #IPPOSI is forming a 'Citizens' Jury' to gather public insights on the role of AI in healthcare. What are your thoughts on integrating AI into this vital sector? #AIinHealthcare #PublicOpinion #HealthTech

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We're committed to shaping healthcare IT through AI and Analytics at Tiga Healthcare Technologies. Our products, like Tiga ShareMind - Health Analytics Platform, Predis and Autononym significantly advance healthcare IT. Tiga ShareMind - Health Analytics Platform allows researchers to work with sensitive health data, enabling research securely. Predis, our early warning system, provides critical insights for public health decision-making. Furthering our devotion to data privacy, we have developed Autononym, an innovative AI-driven tool that revolutionizes data confidentiality. Autononym anonymizes personal information across various data types, including visual, tabular or free text, ensuring robust privacy in data handling. Together, these solutions showcase our dedication to innovation and excellence in healthcare technology. Let's shape the future together, as always! #Tiga #HealthcareIT #PharmaIT

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📈 🏥 Multi-Modal Data Governance: A Crucial Pillar for Generative AI Success in Healthcare and Life Sciences As healthcare and life sciences organizations explore the potential of Generative AI, establishing robust multi-modal data governance is crucial. Effective data governance serves as a solid foundation for GenAI adoption, empowering organizations to navigate challenges and accelerate the deployment of use cases to production. Key aspects of multi-modal data governance include: 1. 🔍 Data Quality and Integrity: Ensure the accuracy, completeness, and consistency of data across various sources and modalities, enabling reliable insights and AI model performance. 2. 🔐 Privacy and Security: Safeguard sensitive information by implementing strict access controls and data protection measures, fostering trust and compliance with industry regulations. 3. 🔗 Standardization and Interoperability: Establish common data standards and formats to facilitate seamless data exchange and collaboration, promoting interoperability across systems and teams. By prioritizing multi-modal data governance, healthcare and life sciences organizations can: 1. ⬆ Build Confidence: Boost stakeholder confidence in AI initiatives by ensuring the underlying data is accurate, secure, compliant with industry standards, and free from bias. 2. 🚀 Accelerate Adoption: Streamline the development and deployment of Generative AI use cases, enabling organizations to harness their full potential by making data readily available for POCs. 3. 👥 Enhance Collaboration: Encourage cross-functional teamwork and knowledge-sharing, fostering innovation and continuous improvement by publishing compliant data catalog for consumption. By investing in strong data governance practices, organizations can lay the groundwork for successful AI adoption and transform the future of patient care. The AWS HCLS team has published a blog post that provides an introduction to multimodal data analysis using AWS services - https://lnkd.in/evKGg4yq As always, I would encourage you to reach out to your technology and GSI partners to further explore industry best practices and discuss how you can implement data governance at your own organization. 💬 Share your experiences and strategies for implementing effective data governance 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 #MultiModalDataGovernance #DataGovernance #GenerativeAI #HealthcareInnovation #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #lifesciences #healthcare #patientcare #ai #clinicalresearch #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess

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On Tuesday 2 July Ankur Kalra and I are hosting a webinar on 3 key questions to evaluate AI investment opportunities in healthcare: 1. What part of your investment's AI lifecycle is defensible?  2. How does generative AI influence your investment strategy?  3. How should you assess Leadership Capability in the AI team? We'll also be talking about what breakthroughs in AI seem imminent in 2024 - what should you look out for. Ankur who is based in the US is the founder and CEO of Hop Labs, a machine learning consultancy focusing on reproducible ML research and scalable ML systems in healthcare, life sciences, and other industries. Prior to Hop Labs, Ankur was a senior technologist at Amazon, and was involved in the launch of Amazon Prime and Amazon Instant Video. Our virtual event is for: ➡️ Investors with an interest in AI in healthcare ➡️ Innovative companies deploying AI and seeking investment in 2024 ➡️ Advisers working with investors and companies seeking funding If you'd like to join us pls register using the link in the comments.

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💡 📖 Demystifying AI Decisions in Healthcare and Life Sciences: The Power of Explainable AI As healthcare and life sciences organizations increasingly adopt AI, ensuring trust and transparency in AI-driven decisions becomes crucial. Explainable AI (XAI) - a powerful approach that enables stakeholders to understand how AI models make predictions and reach conclusions. A recent research paper by Stephanie Baker and Wei Xiang (2023) explores how explainability creates trustworthy and socially responsible artificial intelligence. To learn more, access the article here: https://lnkd.in/e6TE4ADR Key benefits of XAI for healthcare and life sciences organizations include: 1. 🤝 Trust Building: XAI helps build trust in AI systems by providing visibility into the decision-making process, leading to increased confidence among patients, clinicians, and regulators. 2. 😇 Ethical AI Adoption: By ensuring AI decisions are explainable, XAI facilitates ethical AI usage, minimizing unintended consequences and potential biases. 3. ⚖ Regulatory Compliance: XAI enables organizations to demonstrate compliance with evolving regulations and guidelines, such as GDPR and FDA recommendations, which emphasize transparency and interpretability. Implementing XAI requires collaboration between data scientists, clinicians, and regulatory experts to develop interpretable models and user-friendly explanations. As generative AI becomes more mainstream within healthcare and life sciences organizations, business and technology leaders would benefit by analyzing which LLMs (prompts, design patterns, and platforms) assist with XAI, and if the outcomes of their applications aligns with their corporate Responsible AI policy. As always, learn by doing - talk to your technology and strategy partners to work with you on a POC (proof of concept) that aims to prove XAI that aligns with your responsible AI policy. 💬 Share your strategies for integrating XAI and promoting transparency in AI-driven healthcare and life sciences solutions below! #ExplainableAI #XAI #Healthcare #LifeSciences #Trust #Transparency #EthicalAI #GenerativeAI #HealthcareInnovation #pharmaceutical #biopharma #biotech #aws #AmazonBedrock #ChangeManagement #innovation #genai #lifesciences #patientcare #ai #clinicalresearch #clinicaltrial #partnership #aiadoption #coe #ai #customersuccess 📢 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

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My article on "Why Building In-house Software Engineering Teams Can Transform Not-For-Profit Hospitals" is now live on Digital Health News. I hope these insights help navigate this important area within #HealthCare spectrum. #Thanks Digital Health News, #DigitalTransformation #CIO #CDO #HealthTech #Not4ProfitHospitals #PatientCare https://lnkd.in/gi8V9yZ2

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Generative AI is set to revolutionize healthcare and life sciences 🌐. The market is expected to grow by over 35% annually 📈, reaching USD 21.74 billion by 2032 💰. Read my latest blog on exploring various use cases and challenges in adoption of Gen AI Key Benefits: 🤖 Automation: AI automates routine tasks, allowing clinicians to focus on complex patient care. ⚡ Efficiency: It boosts efficiency and improves patient outcomes. 💊 Drug Discovery: AI speeds up drug discovery and clinical trials. 🧬 Personalized Medicine: AI supports precision medicine with tailored treatment plans. 📸 Medical Imaging: AI enhances medical imaging, improving diagnostic accuracy. Challenges to Address: Key challenges include data privacy 🔒, ethical use of AI ⚖️, and regulatory compliance 📝. Address these through robust security, transparent decision-making, and collaboration with clinicians. A New Era in Healthcare & Life Sciences Generative AI has the potential to transform healthcare & Life sciences 🚀. By embracing this technology and addressing its challenges, we can improve efficiency, drive innovation, and ultimately deliver better patient care ❤️. #GenAI #Healthcare #ArtificialIntelligence #GenerativeAI #MedicalImaging #DrugDiscovery #PrecisionMedicine #Innovation #LifeSciences #HealthTech #MedicalResearch

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AI chatbots within the healthcare industry commonly employ natural language processing (NLP) algorithms to grasp and respond to patient inquiries in a conversational manner. Through the utilization of NLP, these chatbots can analyze and interpret text inputs from patients, offering personalized health advice and guidance based on their individual needs and concerns. This strategy not only reduces the workload of healthcare professionals but also enhances patient engagement by providing relevant and timely assistance. #NLP #HealthTech #AIChatbots #PatientCare #HealthcareInnovation #PatientEngagement.

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I caught up with Nikhil Krishnan of Out-Of-Pocket fame to get his take on the current state of the healthcare technology landscape. Up first: his current thoughts on what's exciting and where there are opportunities for AI-driven solutions. First half of our Q&A below 👇 — 🟩 Q: When it comes to AI, what area are you most excited about? Patient-facing solutions. The AI admin and back-office space has quickly become saturated, and incumbents will always have a distribution advantage here. Which is why I’m actually surprised there aren’t more players entering the patient-facing side. I think the traditional healthcare companies are too nervous to actually go into that patient-facing area. And it's also a regulatory gray area, so if you actually get traction, you can probably help shape how this stuff ultimately gets regulated. So right now, I think the greatest opportunities are for patient-facing AI tools. 🟩 Q: Where specifically do you see whitespace to develop patient-facing solutions? I think there’s still a lot of opportunity on the patient-facing automation side. A great example actually is automated lab results. There has been an issue for the last 3 years that lab results automatically get released to patients, and they see something's abnormal. The patient is trying to figure out what’s going on and what they've read. Part of this is explaining the result, which is something Elaborate is working on, and in some cases, actually, it's really useful for the patient to get that notice immediately with follow up steps, like scheduling a diagnostic. Another area is AI that can autonomously make workflow changes within a local system. Viz.ai is working on this automated workflow piece in the context of situations like stroke care coordination and hypertrophic cardiomyopathy. This requires not only automation in terms of being able to understand the context of multiple datasets, but also make the requisite workflow changes (e.g. escalating to the right specialist). These are particularly useful in time-sensitive areas. 🟩 Q: Any other AI whitespaces? Monitoring of AI models. It's one of the areas that's still unknown in terms of how do you give peace of mind to your buyer? It becomes really hard to actually evaluate and audit a lot of these AI algorithms without some form of data exhaust. I feel like one of the things that will happen is you'll see some form of certifying entity emerge that can give a gold standard to hospitals. 🟩 Q: What else are you thinking about as it pertains to AI? I do think that pricing is one of the more interesting components here. For example, in AI scribing, how you think about pricing changes a lot depending on if you’ve got a human in the loop or not. Because adding a human layer goes from near zero marginal cost to cost scaling linearly. One of the ways that companies will be able to differentiate themselves — especially from incumbents — is the pricing model.

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Discover how healthcare payers can harness data-driven insights to promote health equity. Alok Mandal, Vice President of Technology, SBU leadership, provides insights on how technology enablers can provide excellent healthcare at low costs. Read here: https://splr.io/6049YBXt1 #HealthEquity #DataDrivenHealthcare #EngineeringFirst

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Sudhanshu Bhatnagar, Lukas Grabner and Jun-An Tan interviewed 15 health system executives in Germany, France and the U.K. discussing the challenges their health systems are facing. A few key insights below and the detailed whitepaper is a highly recommended read: 1 - Optimizing Operations: Hospitals need solutions that streamline processes, reduce skill dependency, and maintain high standards of care. Efficiency in admissions, readmissions, and resource utilization is paramount. ⚙️ 2 - Shifting Sites of Care: There's a significant move towards outpatient procedures and remote care, driven by reforms and national strategies. 🌐 3 - Cost Management: With rising costs and budget constraints, hospitals demand investments backed by financial and clinical outcomes.💰 4 - Strategic Partnerships: Hospitals are moving towards deeper, more collaborative partnerships. These partnerships must bring forward-thinking solutions, robust supply chains, and sustainable practices to the table. 🤝

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Amidst the evolving healthcare landscape, healthcare value analysis is proving to be a source of informed decision-making, joining clinical efficacy with fiscal responsibility to elevate patient care standards. The integration of advanced analytics and ML in health data management is pointing toward a future where proactive care pathways and optimized resource allocation redefine healthcare efficiency and effectiveness. #Healthcare #ML #PatientCare #HealthcareData

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