LUKASZ KOWALCZYK MD’s Post

AI has the potential to transform health equity and care outcomes, but what groundwork needs to be laid? Healthcare experts weigh in on how AI can improve care for traditionally underserved communities. Dive into this insightful discussion: https://lnkd.in/ewkGhiQ5 Boston Globe Media #AIinhealthcare #HealthEquity #ResponsibleAI

Interview with Brian Anderson, MD, Coalition for Health AI (CHAI) CHAI is focused on the following guiding principles as they pioneer a safe, equitable future in healthcare: 1. Finding a secure approach to introducing AI into the clinical setting. Their strategy includes starting with less critical environments to evaluate the safety and accuracy of AI before expanding to more high-stakes settings, such as the ICU. 2. Engaging communities that have been historically excluded from digital health advancements. Coalition for Health AI (CHAI) is actively seeking ways to ensure these communities have the opportunity to participate and benefit from the digital health revolution. 3. Striking a delicate balance between advancing AI technology in healthcare and implementing necessary safeguards, guidelines, and guardrails. They acknowledge that healthcare is a field where patient lives are at stake, and therefore, they prioritize ensuring the safety and well-being of individuals. #CoalitionforhealthAI #safeAI #equitableAI #transparentAI #regulatoryaffairs

"AI in healthcare will never be fully automated because, at the end of the day, it’s about humans helping humans. Machines can enhance decisions, but the human touch remains essential." – Eben Esterhuizen, CTO & Co-Founder, Siftwell.   Last week, during a webinar hosted by The Root Cause Coalition, Trey Sutten, Eben Esterhuizen, and Kim W. Webb explored the practical applications of AI in healthcare, focusing on practicality and safety over hype.   Current AI applications in healthcare prioritize predictions and actionable recommendations over the "futuristic generative AI" that often dominates headlines. The mission is clear: turn historical data into meaningful insights to improve patient outcomes and optimize costs.   What does this look like in real-world scenarios? Siftwell's models can identify the root causes of health disparities—like food insecurity or lack of transportation—and recommend targeted interventions. For example, ensuring access to primary care can help reduce avoidable healthcare expenses, such as emergency department visits.   AI should never replace humans—it should enhance their abilities. As Kim said, "Knowing that 65% of a cohort has low adherence to medication doesn’t just tell us a problem—it informs solutions, like deploying community health workers in high-need areas."   At its core, AI in healthcare is a tool to support decision-making, with the ultimate goal of empowering people—patients, providers, and communities. [Watch the full webinar here 👉 https://lnkd.in/g96imEV9]

"AI in healthcare will never replace the human touch—it’s about enhancing decisions to empower patients, providers, and communities." – Eben Esterhuizen, CTO, Siftwell. Be sure to check out this webinar with The Root Cause Coalition where Siftwell leaders shared how AI is transforming healthcare by turning data into actionable insights, addressing disparities, and supporting smarter decision-making. From reducing ER visits to improving medication adherence, AI is a tool for real-world solutions. [Watch the full webinar here 👉 https://lnkd.in/g96imEV9]

According to a survey by Deloitte, patient use of generative AI (GenAI) in healthcare is decreasing, with only 37% of respondents using GenAI for health reasons in 2024, compared to 40% in 2023. The survey suggests that this decline is due to a lack of patient trust in GenAI. Three in 10 respondents said they distrust the information provided by GenAI, up from 23% in 2023. To address this issue, the report recommends that healthcare organizations tap into providers as trusted messengers to endorse and educate patients about GenAI. Additionally, community health partnerships could be instrumental in building patient trust and addressing barriers to GenAI adoption. The report concludes that with transparency, education, and alignment with patient values, GenAI has the potential to become a trusted ally in improving health outcomes and reducing healthcare costs. Impact on the generative AI industry: Negative. The decrease in patient use of GenAI and the growing distrust in the technology indicate a lack of confidence in its accuracy and reliability. This poses a challenge for the generative AI industry to address these concerns and build trust among patients. Question for discussion: How can healthcare organizations effectively educate patients about GenAI and address their concerns to build trust in the technology? #GenerativeAI #HealthcareTechnology #PatientTrust

52% of respondents of the EY Global Consumer Health Survey said they feel AI will be commonly used in health care in the next 10 years. However, 0 clinicians interviewed said they have access to analytic insights about their patient population to improve outcomes. Today AI is more prevalent in the back office – billing, claims, waste reduction and scheduling – which are perceived as safer realms for providers to get more comfortable with AI. It is clear that a highly mature level of governing, monitoring and quality oversight is needed before we see widespread clinical use of AI so as not to put patients in harm’s way. Explore more survey results and 5 key considerations for health executives to develop their AI and GenAI strategies in this white paper. 

We must refocus our efforts on how AI can be practically applied to improve health care – with a particular emphasis on operational areas and decision-making processes https://ow.ly/7TCP50RSpNL

💡Nice to see the AI discussion moving from hopes & ambitions to practicality & execution! 🤖 « Health care organizations, faced with an increasing deluge of AI solutions offerings, need a down-to-earth, executable framework that will enable setting-specific assessments of the appropriateness of AI-driven solutions. »

Love this framework! Curious how many healthcare organizations have the roles described in the NEJM (or equivalent) in their #AI governance councils: - **AI Solution Developer** (is this typically a vendor role?) - **Organizational Data Lead** - **MLOps Expert** (how is model deployment and monitoring managed?) - **Organizational AI Lead** (who drives AI vision and strategy?) - **Clinical Expert** (should this person also have AI expertise, or is clinical expertise sufficient?) How are these roles defined in your organization? Any additional roles you find essential? How does #informatics fit into this framework?

🔍 Exploring the Role of AI in Shaping the Future of Healthcare Policy 🔍 Artificial intelligence is reshaping our world at an unprecedented pace, and healthcare policy is no exception. As we look to the future, the integration of AI offers some exciting possibilities for more effective, efficient, and accessible healthcare systems. 🌟 Data-Driven Decision-Making AI can quickly analyse vast amounts of healthcare data, helping policymakers identify trends, predict outcomes, and make data-driven decisions. From tracking public health crises to optimizing resource allocation, AI enables us to make timely and well-informed policy adjustments that can ultimately save lives. 🌟 Personalized Public Health Strategies AI-driven insights can help develop tailored public health initiatives by identifying unique needs across different demographics. This personalized approach allows for more targeted healthcare solutions that can address specific community health challenges, improving overall population health outcomes. 🌟 Improving Access and Reducing Costs By streamlining administrative tasks and optimizing operational workflows, AI can lower healthcare costs. Automated systems in hospitals, clinics, and public health agencies allow healthcare professionals to focus on patient care, creating a more accessible and responsive healthcare system for all. 🌟 Predictive Modelling for Proactive Policies Imagine predicting outbreaks or disease trends before they even happen. AI-powered predictive models can anticipate health threats, empowering governments and agencies to act proactively rather than reactively. This shift can lead to more resilient healthcare systems prepared for future challenges. AI in healthcare policy isn’t just about enhancing efficiency; it’s about revolutionising healthcare delivery to be more equitable, precise, and proactive. While we must navigate ethical and data privacy challenges, the potential benefits are tremendous. Harnessing the power of AI responsibly could be the key to a healthier, more resilient world. 🌍💡 #AI #HealthcarePolicy #FutureOfHealthcare #DataDrivenDecisions #InnovationInHealthcare #HealthEquity #PublicHealth #HealthcareRevolution