Brett McReynolds’ Post

Healthcare data analytics focuses on deriving descriptive and diagnostic insights from patient data to better understand health conditions and outcomes. In contrast, AI leverages machine learning algorithms to go beyond current knowledge and power predictive analytics that forecast risks, suggest interventions, and enable more personalized care. Ultimately, data analytics provides the contextual grounding for AI applications in healthcare settings. #HealthcareDataAnalytics #DataRx The future is now. Enroll in Touro University Illinois' online MS in Healthcare Data Analytics. Click here: https://lnkd.in/eaHb-q6T

Last Chance to Register for the ACHE of Massachusetts Sponsor-Led Virtual Event: Decoding Healthcare Data with AI: Practical Tips for Unlocking Actionable Insights. This event is FREE for American College of Healthcare Executives members - A total of 1.0 hours of American College of Healthcare Executives Qualifying Education credits will be awarded to those who attend. Date: April 24 Time: 12Noon -1:15 PM ET Location: Virtual (Online) Join ACHE of Massachusetts sponsor Dimensional Insight for an insightful event where we’ll explore the practical applications of AI in healthcare data analysis. Keynote Speakers: - George Dealy President, Healthcare Applications, Dimensional Insight - Josh Lovering, Healthcare Business Intelligence Consultant, Dimensional Insight   Insights Covered: ·      Core functions of AI and machine learning tools & technologies ·      Differentiating between generative and quantitative AI methodologies ·      Selecting the most suitable AI approaches for healthcare information scenarios ·      Case study review: Applying AI and machine learning tools to practical healthcare information scenarios ·      How “citizen data scientists” and “data analysts” can harness AI to enhance their work Free for members; nonmembers - $10. For additional details and to register, visit - https://lnkd.in/ePea7dZm  

Hi re:invent attendees! At re:Invent 2023 as part of the Healthcare and Life Sciences Executive forum, there will be executive event on Data on the 27th November before the main event. Healthcare and Life Sciences organizations are at a turning point in how they use data science, analytics, machine learning (ML), and generative artificial intelligence (AI) to accelerate the development and distribution of lifesaving treatments, improve the time to and quality of diagnoses, and advance the transition to personalized health and precision medicine. But, harnessing the power of data and AI/ML requires executive leadership engagement and an end-to-end data strategy. Join us for this exclusive executive event to learn how industry leaders are reinventing their business with data, and unlocking patient-centric innovations with AI/ML in the cloud. Explore how they are using AWS’ comprehensive set of data capabilities to build scalable platforms that surface data-driven insights for their immediate challenges, while creating an innovation hub to plan for future needs. The event will feature keynotes, panel discussions, lightning talks, and product discussions across: - Applying AI/ML and generative AI to drug discovery & development, clinical decision support, and more -Unlocking access to and insights from first-party, third-party, and multi-modal data -Tools to democratize access to AI/ML and analytics Please go to the HCLS Symposium to sign up page below. https://lnkd.in/gb4UrMP7

Fundamentals of Machine Learning for Healthcare from Stanford University! "I am Excited to announce that I have completed the Fundamentals of Machine Learning for Healthcare course from Stanford University through Coursera🎓💻! Delving into the intersection of healthcare and cutting-edge technology has been an eye-opening experience. I've gained essential skills in preprocessing healthcare data, building and evaluating machine learning models, and navigating the unique challenges of deploying AI in clinical settings. Looking forward to applying these insights to drive innovation and improve patient outcomes in the healthcare field! #MachineLearning #HealthcareTech #ContinuousLearning" #StanfordCME#HealthcareIT

A review on this conversation where Todd shares his insights on the growing use of AI in healthcare across administrative support, knowledge augmentation, clinical decision-making, and revenue cycle management. Give it a listen. https://ow.ly/rwjt50Qew5w I enjoyed Todd’s perspective during this discussion on AI in Healthcare and was surprised by comments regarding the Healthcare workforce. Something I hope AI can mend. As ex-AWS, Todd’s “AWS sayings” on data and the customer resonated well with me. Data has Gravity. One of the challenges in healthcare was the amount of siloed data. Pulling it together from disparate sources was the lion’s share of the work, then normalizing it, and making sense of it.  With Inovalon’s large data sets, we’ve already done the heavy lifting liberating us to leverage capabilities in applying technologies like AI, ML, and predictive analytics. As well, that we’re both “Working Backwards” from real problems to develop solutions. I’m pleased to hear this as I investigate an area Todd mentioned, AI to assist in Social Determinants of Health (SDOH’s).  Hopefully we can change those small incremental improvements over large amounts of time, into the bursting he mentioned. There’s more great insights in our research series with Harvard Medical School on the research data and how it impacts us at policy level in our country. Stay tuned. It’s the MarkSza channel. This podcast is packed with great insights. Thanks Justine Giancola and Todd C. Sharp, MSci! #AWS #Reinvent2023 #ai #Inovalon

Causal machine learning is changing how we make clinical decisions. Unlike traditional machine learning that predicts outcomes, causal machine learning explains the "why" and "what if" behind those predictions. This shift from correlation to causation allows for personalized medicine at its best. Imagine knowing not just which treatment could work, but why it’s the best option for a specific patient. 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲𝘀 𝗯𝗲𝘁𝘄𝗲𝗲𝗻 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗟 𝗮𝗻𝗱 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗟: 𝟭. 𝗙𝗼𝗰𝘂𝘀 𝗼𝗻 𝗖𝗮𝘂𝘀𝗮𝘁𝗶𝗼𝗻 𝘃𝘀. 𝗖𝗼𝗿𝗿𝗲𝗹𝗮𝘁𝗶𝗼𝗻: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Identifies correlations and makes predictions based on patterns observed in data. It's primarily concerned with "what" happens. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Looks to establish cause-and-effect relationships, answering "why" something happens and "what if" different interventions are applied. 𝟮. 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗔𝗽𝗽𝗿𝗼𝗮𝗰𝗵: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Often uses algorithms that can be black boxes, focusing on accuracy and pattern detection over interpretability. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Employs models that incorporate causal assumptions, making them more transparent and interpretable, as they mimic human reasoning more closely. 𝟯. 𝗗𝗮𝘁𝗮 𝗥𝗲𝗾𝘂𝗶𝗿𝗲𝗺𝗲𝗻𝘁𝘀: - 𝗖𝗹𝗮𝘀𝘀𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: : Can work effectively with large volumes of observational data. - 𝗖𝗮𝘂𝘀𝗮𝗹 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴: Often requires structured experimental data or sophisticated designs to infer causality, such as randomized control trials or longitudinal studies. Recent studies highlight its power: Causal machine learning can potentially reduce trial and error in treatment plans, increasing efficiency and patient outcomes. This method is not just about data, but about making the data work for us in the most impactful ways. 🔗 Link to study in the comments below. For those in evidence generation, this means richer, more actionable insights that can accelerate innovation and patient care. The potential for this technology to improve lives is immense. 👉 Follow xCures Read our LinkedIn Newsletter: https://lnkd.in/dnNJV2ti https://xcures.com/ 👀 #HealthTech #AIinHealthcare

Good morning and TGIF! A little birdie told me that there are a lot of registrants for this upcoming webinar so ""check it out and don't miss out""! Join Dimensional Insight to learn about decoding healthcare data with AI at the end of this month! DECODING HEALTHCARE DATA WITH AI: PRACTICAL TIPS FOR UNLOCKING ACTIONABLE INSIGHTS REGISTER HERE: https://lnkd.in/eUUBfsnw AI and machine learning offer immense potential for unlocking insights from healthcare data. While generative AI has garnered all the buzz in the headlines, quantitative AI works behind the scenes on many of healthcare's real-world data challenges. In this webinar, we’ll explore the automation power of analytics models and algorithms to tackle high-impact healthcare use cases. You'll learn how AI can help to forecast patient volumes, predict wait times, optimize staffing, and more. You’ll walk away empowered to assess data readiness at your organization and become a “citizen data scientist.” Join to discover practical approaches to data-driven AI that can help advance actionable change at your healthcare organization. Presenters: George Dealy and Josh Lovering

This is set to be a thought-provoking luncheon on Healthcare in the Age of #AI! 🌐 Discover how #artificialintelligence is revolutionizing the #healthcare industry and gain insights from industry experts, including SAIC Director of Data Science, Shweta Mulcare.

Canada’s health care system is not immune to needing to adapt to a changing landscape due to technological advancements like artificial intelligence.   Join University of Waterloo's top AI and public health experts at the latest offering from WatSPEED at the University of Waterloo “Leveraging AI to Improve Health Care in Canada.”   The course is designed for senior leaders and executives in the health care space to explore the implementation and implications of integrating AI and analytics tools into the sector. It was built based on feedback from some of Canada's top leaders in health care and health data from both the public and private sector.    Combining virtual and in-person modes of learning, the course will provide actionable strategies for navigating AI adoption while prioritizing patient outcomes and organizational outcomes and organizational success.   Learn more at https://lnkd.in/gYVfa5k3

GenAI is becoming prevalent across various industries, streamlining business operations, enhancing product and service quality, and transforming the future of work. However, the widespread adoption of AI-driven predictive analytics in Healthcare is hindered by challenges such as data standardization, integration of disparate data sources, and semantic interoperability. Mayo Clinic addressed these issues by partnering with Google to implement machine learning solutions. Their efforts to integrate, harmonize, and analyze diverse data sources to enable AI predictive analytics in their radiology department show promise but highlight that there is still a long journey ahead, with human oversight remaining crucial. But when this evolves further....should AI prescribe without a medical license?