Christina Busmalis

🌟 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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✍ Crafting an Effective AI Council Charter in Healthcare & Life Sciences Continuing our ongoing discussions on AI Council for GenAI acceleration (post 1 - https://lnkd.in/eW2QhTKR; post 2 - https://lnkd.in/egbJmTps), let’s dive deeper into what might be literally the first task for AI Council i.e. establishing and maintaining a well-defined AI Council Charter. A well-crafted charter not only mitigates risks but also drives responsible AI innovation. The healthcare and life sciences sector faces unique challenges, including bias in AI algorithms, data privacy concerns, and the complexities of regulatory compliance. Addressing these challenges, as part of their charter, is critical for the success of the AI Council. Drafting a charter as the AI Council's first deliverable sets the tone for expectations and its effectiveness in leading GenAI adoption. This process may benefit from engaging a partner or consultant for a smooth experience. Below, I am sharing my mental model on charter development, giving insight into what it takes to deliver. As a new AI Council of diverse internal leaders, expect to go through norming, storming, and forming stages while drafting a charter. For a common understanding, I recommend members review key documents from government and NGO entities, such as FDA's AI & Medical Products white paper, White House's AI Bill of Rights, WHO's AI health regulations, and EU's AI Act. Give members ample time to familiarize themselves with these materials. Next, invite them for collaborative charter-defining workshops, ideally offsite to avoid distractions. During this 2-4 day event, use design thinking to gather inputs on purpose, scope, goals, challenges, roles, responsibilities, deliverables, KPIs, structure, and alignment with medical/scientific rigor. This iterative process should allow time for idea generation, sharing, grouping, and prioritizing. Using prioritized ideas, facilitate a session for participants to collaboratively draft the charter, combining key elements identified. Define the team's Purpose, Goals, Roles, Responsibilities, KPIs, Deliverables, and Structure. Allow ample time to ensure alignment and commitment from your cross-functional AI Council. Next, seek council feedback on clear, measurable KPIs (e.g., AI implementations, compliance rates) to gauge effectiveness. Align on audit cadence and PMO support. Consider additional measures: communication strategy, surveys, ethical incident analysis, impact assessments, and benchmark comparisons. These ensure the charter evolves with the AI landscape, remaining a living document. 💬 Have you defined your AI Council's charter? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #AIinHealthcare #ResponsibleAI #LifeSciences

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ECOSYSTEM VISUALIZATION OF THE DIGITAL HEALTHCARE INDUSTRY ⬇ ▫️Market segments have been depicted as actors for better readability. ▫️The grey rectangles around multiple market segments represent generic roles. ▫️To further improve readability and reduce complexity traditional generic roles have been omitted (manufacturer, purchaser, investors and consultants, political and humanitarian groups, researchers, and regulators) as well as the value streams between cloud service provider and information platforms, fiscal and market intermediaries, and services for remote and on-demand healthcare. ✍ Hermes, S., Riasanow, T., Clemons, E.K. et al. The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Bus Res 13, 1033–1069 (2020). DOI: 10.1007/s40685-020-00125-x

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🛣 Navigating Ethical Dilemmas in Responsible AI As we continue to integrate GenAI into healthcare and life sciences, the role of responsible and ethical committees has never been more crucial. These committees face a myriad of ethical dilemmas that require careful consideration and proactive strategies. Continuing our discussions about Responsible AI from yesterday (https://lnkd.in/gxcv5BX6), let’s explore the ethical dilemmas that your Responsible & Ethical AI committee needs to weigh in on and how they might impact your corporate data & security strategy. The questions below are some that I have heard from internal and external stakeholders during my interactions, but this is not an exhaustive list. These ethical dilemmas require life sciences leaders to carefully navigate complex decisions, balancing technological advancement with ethical responsibilities, regulatory compliance, and societal impact. Data Privacy and Security: Balancing the need for data access to improve AI models while ensuring patient confidentiality and compliance with regulations like HIPAA, GDPR, and GxP. This becomes challenging when the data you need resides in another LoB. Who should have access to what data? What is allowed, and more importantly what is NOT allowed? Algorithmic Bias: Ensuring AI systems do not perpetuate or amplify existing biases in healthcare decision-making and drug development. The industry has been dealing with this for a while now and have a good grasp here when it comes to structured data that could be easily analyzed. But what about unstructured data that is now being unleashed through GenAI applications? Transparency and Explainability: Making AI decision-making transparent for high-stakes medical decisions and regulatory submissions. Until we can capture and economically save LLM parameters during inference for audit and reproducibility, full transparency remains elusive. How do we balance this, and what will your organization accept on behalf of their stakeholders? Accountability: Establishing responsibility in AI-assisted patient care and research. Does your organization think of LLMs as an assistive tool for humans or as a tool that will replace humans in business functions? Not only is the ethical dilemma perplexing, organizations also need relatively simply and efficient technology solutions to adhere to the corporate policy at scale. 💬 What ethical dilemmas have you encountered with use of GenAI in healthcare & life sciences? 📢 Subscribe to my newsletter: https://lnkd.in/g3bdneR7 #genai #ai #aws #ResponsibleAI #AIEthics #HealthcareAI #LifeSciences #DataPrivacy #EthicalDilemmas

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🌞 Will MedTech Support Health Providers' Digital Adoption? ✒Deloitte survey findings reveal how medtech companies are using (and not using) digital technologies to transform their businesses. https://lnkd.in/dKA-PgF9 How medtech VPs see innovative technologies’ greatest value: ✔Better patient/customer experiences: 76% ✔Greater connectivity of devices (e.g., RPM): 57% ✔New product development (e.g., digital therapies): 55% ✔XaaS (“as-a-service”) business models: 53% Challenges facing medtech are similar to all Health industries: 📌Leadership alignment around value: 65% (the No. 1 gap preventing medtech from achieving digital maturity) 📌Dedicated funding: 61% 📌Better digital innovation strategy: 58% 📌Talents with data expertise: 55% #DigitalTransformation, #SmartHospital, #SmartClinic, #PatientCentered, #ValueBased, #DigitalFirst, #AIEnabled, #TechnologyAdvanced, #HyperConnected, #AI

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What’s happened this week in the healthcare & AI world? Here is the interesting news and research: 𝐍𝐞𝐰𝐬: 🗞 GE are leading a consortium working on synthetic data generation and their use in healthcare AI. 🗞At their investment summit the Gov: 💰 Released their 10-year industrial strategy, highlighting a growth to build a stable NHS; 💰Announced £6.3bn investment in UK data centres for AI; and 💰Stated in a speech from Starmer: we need to be “running towards” the opportunities of AI. 🗞 NHSE published lessons learned from the AI Evaluation rewards such as: 🔹 Deployment & evaluation plans should be co-produced; 🔹 Quasi-experimental methods were appropriate; and 🔹 Future programmes should more explicitly focus on impact. 𝐑𝐞𝐬𝐞𝐚𝐫𝐜𝐡: 👩‍💻 A study looking at using Bing (a search engine chatbot) responses to questions about common prescribed drugs in the US found 66% of responses to be potentially harmful. 👩‍💻 A survey of medical students in Kazakhstan found 95% had no AI education in medical school, and 74% received information about AI from the media. 👩‍💻 AI survey of 38 Doctors in Indonesia found socio-technical blindness and AI configuration significantly influence anxiety about AI replacing their job.

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Regard using AI to maximise the value from patient data Regard, a Los Angeles-based clinical decision software company, has secured $61 million in Series B funding to advance its AI-powered clinical-insights platform, scale its reach in healthcare, and invest in research on healthcare LLMs. Regard's tool utilizes AI to evaluate patient history, generate clinical decisions and documentation, and facilitate clinician-to-clinician communications. This is important because the World Economic Forum estimates that less than 3% of patient data is used. Unlocking the value in the extra 97% is a huge opportunity to improve healthcare outcomes for patients and support the clinicians. This is yet another example of the continued investment in AI for the health sector, and the innovations we can expect in the future. While not the only company doing this work, It's great to see Regard pushing the boundaries of what's possible with AI in healthcare. I can't wait to see what innovations they come up with next! Congratulations to Eli Ben-Joseph and the team at Regard on the success of the funding round. https://lnkd.in/ggD2NpX7

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Will you be using Med-Gemini to diagnose diseases? Or will you use it to help healthcare professionals diagnose diseases? I do not know why the benchmarking involves 'complex diagnostic challenges from NEJM clinicopathological conferences.' What are you trying to create here? A model that will apply for a medical license? I know GPT4o with multimodality gives this message: "I'm not a medical professional, but I can try to provide some general information. However, it's important to consult a doctor or healthcare provider for an accurate diagnosis and appropriate treatment. They can offer personalized advice based on a thorough examination and your medical history." in the interest of patient safety when asked to diagnose symptoms. Medical professionals need so much help to be able to do stuff more easily that Gen AI could help them. Time-consuming negotiations with Insurance companies, unending documentation, prior auths, keeping a tab on operations KPIs, siloed managerial objectives, lack of proper flow, lack of proper discharge instructions and follow-up.... so much that a talking seeing learning bot could help with. But you try benchmarking with something it would never be allowed to do?

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3 noteworthy acquisitions were announced in #DigitalHealth last week, moving the total to 76 acquisitions in 2024 till date. - On Tuesday, Switzerland-based Mediktor announced the acquisition of Sensely, a San Francisco-based digital healthcare provider that pioneered an empathy-driven conversation platform to support hospital systems and insurance services with world-class member engagement. - On Thursday, Carevīve was acquired by Health Catalyst. The acquisition strengthens Health Catalyst's position in the digital healthcare market, expanding its capabilities in patient engagement and care coordination. - On Friday, Doktor.se announced that it acquired competitor Doktor24. The combined entity will provide a comprehensive suite of digital healthcare services, including telemedicine consultations, patient engagement platforms, and data analytics capabilities. Compared to last year, the total number of acquisitions is still trending significantly lower. Till date, the M&A activity in Digital Health has been 14% lower as compared to the same period last year. #Merger #Acquisitions #healthcarefinancing Source: All data from Alpha Edge, the business intelligence tool in HealthTech Alpha

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👉🏼 Concordance of ChatGPT With American Burn Association Guidelines on Acute Burns 🤓 Sumaarg Pandya 👇🏻 https://lnkd.in/eJhe8Bxp 🔍 Focus on data insights: - 📈 The study involved twelve acute burn care questions, demonstrating the potential for AI in clinical settings. - ✅ ChatGPT responses scored an average of 4.56 ± 0.65, indicating exceptional quality and high concordance with established guidelines. - 🧑‍⚕️ Three burn surgeons independently evaluated responses, underscoring the reliability of AI-generated information compared to expert human evaluations. 💡 Main outcomes and implications: - 🤝 ChatGPT can be integrated as a complementary tool in medical decision-making and patient education. - 📚 The findings suggest AI could streamline processes and enhance the delivery of care in burn management. - 🚀 There is potential for broader applications of AI in various medical fields, possibly setting new standards for information accuracy and accessibility. 📚 Field significance: - 🌐 This research opens pathways for incorporating AI technology into healthcare frameworks, emphasizing the need for robust evaluation of AI tools. - ⚖️ It highlights the balance between human expertise and automated systems in achieving optimal patient outcomes. - 🔄 As healthcare evolves, integration of AI may transform practices and improve patient education methods, positioning technology as a critical ally. 🗄️: [#ChatGPT #BurnCare #HealthcareAI #ClinicalGuidelines #PatientEducation #MedicalDecisionMaking #ArtificialIntelligence #ClinicalPractice]

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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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In healthcare, data alone doesn’t mean much. The real magic happens when we turn that raw data into "actionable insights"—and that’s where the real impact on patient care begins. At NirogGyan | Smart Health Reports & AI, I’ve personally seen how converting complex lab data into simple, meaningful insights can completely change how patients and doctors interact. It’s not just about presenting results—it’s about giving people the power to understand their health and take action. Whether it’s catching early warning signs or fine-tuning treatment, those insights are what drive proactive healthcare. Data is just the start. What we do with it is what truly makes a difference. #HealthData #PatientCare #ActionableInsights #NirogGyan #HealthTech

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This is our first step in an important journey. We understand that operating at scale is important. To modernize healthcare our customers want to equip every patient room to benefit from virtual care workflows. We think sending huge amounts of audio/video media back to the cloud to be processed by an AI engine is problematic on a number of fronts for healthcare - including network bandwidth, security and patient privacy. As a result, we are taking a different approach by doing computer vision processing in our device in the patient room at the network edge. We are onto something... take a look...

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🏥🌟 Common GenAI Adoption Challenges Generative AI (GenAI) is transforming healthcare and life sciences, but adoption poses various challenges. Despite these hurdles, progress has been made, and the industry's resilience proves we can turn challenges into opportunities. With continued collaboration and commitment to responsible GenAI adoption, we can accelerate innovation and improve patient outcomes. Here's an honest and sincere look at navigating common GenAI barriers: 1. 🔎 Legal Challenges: Many enterprises are concerned about legal issues with large language model (LLM) providers using their data and prompts to train models. In most cases, this is not happening. For a detailed discussion, reach out to your technology partners. Easy to resolve through a structured deep dive session. 2.💡 Intellectual Property: Protecting IP is crucial. Companies are developing clear guidelines and collaborating with IP experts to navigate licensing, patenting, and copyright issues. Most technology partners are are offering some level of indemnification for customers using their GenAI LLMs. Important to dive into the details of protection offered. 3. 📜 Regulatory and Compliance: While complex, industry leaders are working with legal experts to ensure compliance and shape the regulatory landscape. Commitment to ethical guidelines and data privacy is key for responsible GenAI adoption. 4. 📚 Skill Gaps: Continued emphasis on upskilling and reskilling will ensure we thrive in the age of GenAI. As always, I would recommend you speak with your technology and GSI partners to understand how they can help upskill your business and technology employees. 5. 💼 Lack of Clear ROI: We've made progress in demonstrating GenAI's value through successful use cases. To strengthen the business case, let's focus on quantifying outcomes and using key metrics to monitor success - https://shorturl.at/lwBQU 6. 🐙 LLM Selection: As the industry continues to define needs and evaluate options, collaborations with AI experts and technology providers are essential for making informed LLM decisions. Refer to my earlier post on LLM KPIs to consider - https://shorturl.at/fjGTZ 7. 🛡 Fear of Reputation Damage: Prioritizing responsible GenAI use, transparency, and ethical decision-making can mitigate reputation risks. As mentioned previously, it's advisable to initially leverage GenAI for internal operational and administrative use cases. This allows you to experiment safely and build operational expertise before exploring GenAI for use cases involving your key stakeholders, both internal and external. 💬 Share your experiences and insights on navigating GenAI challenges in healthcare and life sciences 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 #HealthcareInnovation #LifeSciences

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📢 New colleague or gimmick hurdle? A user-centric review of the barriers and facilitators of robots (or #healthtech / AI in general) in hospitals Healthcare systems are under immense pressure—facing rising costs, personnel shortages, ageing populations, and a surge in chronic diseases and global health challenges. How can we keep up with these demands while improving care quality? Enter robotic technologies and artificial intelligence (AI). These tools offer immense potential to enhance efficiency, reduce costs, and improve working conditions in healthcare. But their integration into hospitals isn't without challenges. A recent review of 501 studies distilled insights from 40 in-depth, user-focused papers, highlighting key barriers and facilitators in implementing robots in hospitals: 🚧 Barriers: Organisational misalignments Resistance to change Concerns about privacy, confidentiality, and anthropomorphisation 🌟 Facilitators: Improved workflows Reduced workload for staff Enhanced patient outcomes The takeaway? The road to robotic healthcare isn't just about advancing tech; it's about redesigning hospital systems, workflows, and user adoption. To ensure success, future research must explore the delicate interplay between hospital employees, institutions, and emerging robotic technologies. 💡 A deeper understanding of these dynamics could pave the way for innovative healthcare solutions that are more accessible, efficient, and human-centric. Here is the great insights and read by Mathias Kofoed Rasmussen, Anna Schneider-Kamp, Tobias Hyrup, Alessandro Godono for PLOS: https://lnkd.in/e4cSFExi #HealthcareInnovation #AI #Robotics #FutureOfCare #DigitalHealth #HealthcareFuturist #digitalhealth #changemanagement

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