Glenn Krauss on CDI program for healthcare

Ambient Generation of Structured Clinical Notes at ModMed - Healthcare IT Today Title: Ambient Generation of Structured Clinical Notes at ModMed #Introduction ModMed has developed a groundbreaking technology for the ambient generation of structured clinical notes, revolutionizing the way healthcare professionals document patient encounters. #What is Ambient Generation? Ambient generation is the process of automatically creating structured clinical notes in real-time as healthcare providers interact with patients, eliminating the need for manual documentation. #Benefits of Ambient Generation Ambient generation offers numerous benefits, including improved accuracy, efficiency, and workflow optimization for healthcare providers. #How Does ModMed's Technology Work? ModMed's technology utilizes advanced natural language processing and machine learning algorithms to analyze conversations between healthcare providers and patients, extracting key information to generate structured clinical notes. #Integration with EHR Systems ai.mediformatica.com #modmed #about #clinical #data #found #health #ambientclinicalvoice #cloud #community #database #healthit #healthcare #digitalhealth #healthtech #healthcaretechnology @MediFormatica (https://buff.ly/4blzyc0)

The NIH Clinical Center  NIH researchers have developed TrialGPT, an AI tool that streamlines matching patients with clinical trials on ClinicalTrials.gov. Published in Nature Communications, the study reveals how TrialGPT reduces screening time by 40% while maintaining accuracy, helping clinicians connect patients to trials faster and more efficiently. Read More: https://go.nih.gov/7hTWZTu #AI #HealthcareInnovation #ClinicalTrials

🌟 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝘃𝗲 𝗠𝗼𝗱𝗲𝗹𝘀 𝗳𝗼𝗿 𝗜𝗻𝘀𝘂𝗿𝗮𝗻𝗰𝗲 𝘂𝘀𝗶𝗻𝗴 𝗡𝗟𝗣, 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴, 𝗮𝗻𝗱 𝗗𝗲𝗲𝗽 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴.🌟 At 𝗡𝘂𝟭𝟬 𝗠𝗲𝗱𝗼𝘃𝗮, we are transforming how insurance companies assess risk and make decisions with Generative AI (Gen AI), using advanced technologies like NLP, Machine Learning, and Deep Learning. Here’s how these technologies can help insurance companies build more accurate Predictive Models: 🧠 𝗥𝗶𝘀𝗸 𝗔𝘀𝘀𝗲𝘀𝘀𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝗣𝗿𝗲𝗰𝗶𝘀𝗶𝗼𝗻: By analyzing medical records, claims history, and client profiles, we use 𝙉𝙇𝙋 𝙢𝙤𝙙𝙚𝙡𝙨 like 𝗕𝗶𝗼𝗕𝗘𝗥𝗧 and 𝗚𝗣𝗧 to predict health risks, leading to more personalized premiums and smarter underwriting. 🔍 𝗜𝗺𝗽𝗿𝗼𝘃𝗲𝗱 𝗙𝗿𝗮𝘂𝗱 𝗗𝗲𝘁𝗲𝗰𝘁𝗶𝗼𝗻: Machine Learning helps identify suspicious patterns in claims data, enabling insurers to catch fraudulent activities early and reduce losses. ❤️ 𝗖𝗵𝗿𝗼𝗻𝗶𝗰 𝗗𝗶𝘀𝗲𝗮𝘀𝗲 𝗣𝗿𝗲𝗱𝗶𝗰𝘁𝗶𝗼𝗻𝘀 Using 𝗟𝗦𝗧𝗠 and 𝗖𝗡𝗡 models, we predict the likelihood of chronic conditions like diabetes or heart disease, helping insurers better assess long-term risks. 🌱 𝗣𝗿𝗼𝗮𝗰𝘁𝗶𝘃𝗲 𝗪𝗲𝗹𝗹𝗻𝗲𝘀𝘀 𝗣𝗿𝗼𝗴𝗿𝗮𝗺𝘀: Data-driven insights allow insurers to offer targeted wellness programs, promoting healthier policyholders and reducing future health costs. These powerful AI technologies enable insurance companies to make smarter decisions, reduce risks, and deliver better outcomes for their customers. Ready to elevate your insurance process with Gen AI? Let’s connect and explore the future together! Website- https://medova.in/ Contact Email- [email protected] #Nu10Medova #Insurance #PredictiveModeling #RiskAssessment #MachineLearning #NLP #DeepLearning #InsuranceTech #HealthInsurance #AI

🦑 Clinical NER: A Comprehensive Guide to the NCER Leaderboard https://lnkd.in/dPjbvZUP

Matching patients to clinical guidelines with #Healthcare-specific #Visual #LLMs: Learn how to automatically read guidelines with flowcharts, decision tables, and free text to correctly answer clinical guideline questions. This webinar by Veysel Kocaman, PhD from John Snow Labs covers live examples and comparisons with docling, markitdown, Llama 3.2 VLM, and OCR, with and without #RAG techniques. https://lnkd.in/gErWF6gs #clinicalguidelines #clinicalpractice #ai #healthai #healthcareai #generativeai #llm #vlm #largelanguagemodels

You probably read the predictions. For a decade, they peddled the same sunny promise: Someday AI will help make medical Staff and Doctors smarter and faster. Someday has dawned. Nearly 80% of health care organizations now use AI. In fact, AI is already helping to reduce repetitive clinical tasks and enable more precise treatments. Check this Detailed #AI Healthcare Study case by SavvyHub https://lnkd.in/gjJdTRKC #Saudi_Healthcare #Saudi_doctors #AI

Navigating Financial Pressures in Healthcare: How #AI and Predictive Modeling Enhance Patient Flow

Exploring Large Language Models (LLMs) in Healthcare Everyone's been hearing this famous term "LLM". So, in order to understand this, I came across this pretty interesting case study below and decided to implement this. Large Language Models (LLMs) have revolutionized many industries by enabling machines to understand and generate human-like language. In the healthcare space, LLMs offer exciting potential for solving complex problems, such as matching patients with clinical trials. PROBLEM -- Patient recruitment is a critical and time-consuming challenge in clinical trials. Medical professionals must sift through numerous patient records and trial criteria to identify the right candidates. This manual process can delay important research and potentially limit access to life-saving treatments for patients. SOLUTION -- The solution leverages an advanced LLM-based framework designed to automate and improve the accuracy of patient-to-clinical-trial matching. This solution evaluates each patient's eligibility by parsing through clinical notes and trial criteria on a criterion-by-criterion basis.For more clarity, a patient note is a summarization of his/her conditions, history, etc., and every clinical trial has some inclusion and exclusion criteria like the one stated in this link -> https://lnkd.in/dSpvKN-b. Have a look at the sample patient note below: - An 8-year-old boy presents with a 2-day history of fever (39°C), dyspnea, and cough after returning from a 5-day trip to Colorado. He initially had loose stools but no upper respiratory symptoms. On exam, he's in respiratory distress with bronchial sounds on the left, and a chest x-ray shows bilateral lung infiltrates. The model not only predicts whether a patient qualifies for a trial but also provides explanations for its decisions—making the process faster and more transparent for healthcare providers. With results showing a 42% reduction in screening time and a matching accuracy of over 87%, this technology has the potential to significantly enhance clinical trial efficiency while ensuring more patients are matched with the trials they need. Head over to my GitHub repository to get more insights --> https://lnkd.in/djzUSsvd #AI #HealthcareInnovation #LLM #ClinicalTrials #DataScience #MachineLearning

📢 Excited to share the demo video of our AI-Optimized Nurses Led Quality Improvement Project! 📢 The video showcases how we combined AI and LLM technologies to revolutionize the traditional Nurse-Led Quality Improvement (QI) processes. Our goal was to enhance speed, collaboration, and efficiency, making it easier for healthcare professionals to implement and manage QI projects. 🎯 Key Features Demonstrated in the Video: 1. Search Query Formation through LLM   - Our project utilizes Large Language Models (LLM) to automatically generate smart and precise search queries, allowing healthcare professionals to find relevant evidence and resources faster than ever before. This feature optimizes the research process, reducing the manual effort involved in searching and reviewing academic literature. 2. Seamless Collaboration    - Collaboration is at the heart of our platform! The system allows multiple users to easily collaborate on QI projects, ensuring that nurses, doctors, and other healthcare professionals can contribute collectively to improving patient care. 3. Automated Email Notifications    - To keep the project owner informed, the platform sends real-time email notifications whenever a new collaboration request or project update occurs. This ensures that team members stay on top of project progress without missing important updates. 4. Summarized Table of Evidence     - Our platform automatically generates a summarized Table of Evidence based on the data and findings from research in a fraction of the time it would typically take. This simplifies decision-making and project planning, enabling nurses to focus on impactful actions rather than time-consuming documentation. 💡 These features not only accelerate the QI process but also ensure a seamless and efficient workflow, enabling healthcare professionals to make faster, data-driven decisions that can improve patient outcomes. A special thanks to: - My group mate Syed Zulqarnain H. and our mentor Muhammad Waseem for their constant support throughout the project. - Dr. Humayun (Hugh) Rashid for his visionary leadership in empowering young minds with cutting-edge technology and innovation. - Muhammad Usman Ghani Khan, Chairman of the Department of Computer Science, UET Lahore, for his guidance and oversight. Check out the video attached for a closer look at how this project works in practice! 🚀 #AIinHealthcare #NursingInnovation #QualityImprovement #LLM #GenerativeAI #HealthcareCollaboration #Innovation #TableofEvidence #UETLahore #Xavor #HealthcareTech #NurseLedQI #ProjectDemo #FutureOfHealthcare

#LLMforHealth – Article of the Day 📚 What happens when we evaluate #LargeLanguageModels for use in #healthcare? ▶ In the article below published in SpringerOpen, the authors assessed the potential of #LLMs to accurately and concisely synthesize ICU discharge summaries. ▶ Key elements of this approach include the #prompt used to generate concise clinical summaries, the #LLMs employed, and the criteria for adjudicating the summaries. ▶ This article highlights the growing significance of LLM experimentation as it addresses a critical medical need. However, just as well-defined endpoints are crucial for the success or failure of a #clinicaltrial, the same principle applies to #LLM experimentation.  ▶ There is a real need to standardize this type of approach to achieve the most accurate and effective results.