HealthCare Resolution Services, Inc.’s Post

Healthcare system first goal shall be finding the solution for detection the disease of a patient which is suffering from, within 30 days and not after 12 month and visiting 19 experts.

I am thrilled to share our recent publication in NEJM AI, which explores the use of large language models (LLMs) like GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b in medical coding. This collaborative study benchmarks LLMs performance in generating accurate medical billing codes and highlights both the potential and current limitations of AI in healthcare. While GPT-4 showed the most promising results, it is clear that further model fine-tuning, the use of advanced techniques like Retrieval-Augmented Generation, and the development of robust regulatory frameworks are necessary to safely integrate AI technologies into healthcare administrative pipelines. I invite you to read our full study and join the conversation: How can we further refine AI applications in healthcare to ensure better patient outcomes and operational efficiency? Robbie Freeman Ali Soroush, MD, MS Ben Glicksberg Alexander Charney Eyal Zimlichman, MD Yiftach Barash Girish Nadkarni 🔗 Link to the full study https://lnkd.in/e2VWsyun

Utilizing #genai for medical coding is considered low-hanging fruit. However, it is crucial to assess the capabilities and limitations of LLMs) like GPT-3.5, GPT-4, Gemini Pro, and Llama2-70b in medical coding tasks. We performed a comprehensive benchmarking analysis of 'out of the box' LLMs for performing medical coding. Methods 📜 We extracted 12 months of unique ICD and CPT codes from a large health system. We provided LLMs with a code description and a prompt to generate a billing code. We then calculated similarity metrics with the code. 🔍 Main Findings: Performance: GPT-4 outperformed other models with the highest exact match rates across ICD-9-CM, ICD-10-CM, and CPT codes. However, even the best results were under 50%, highlighting a significant accuracy gap. Error Analysis: LLMs frequently generated codes that were either imprecise or completely fabricated, raising concerns about their current utility in clinical settings. Factors Influencing Performance: Shorter codes and descriptions with higher frequency in electronic health records generally correlate with better performance. 🚀 Future Directions: To harness AI's full potential in healthcare efficiently, further research must focus on: Model Training and Fine-tuning: Tailoring LLMs to understand better and generate medical codes through advanced training methods. Hybrid AI-Coder Systems: Developing systems that combine AI's computational power with human expertise to enhance accuracy and reliability. Regulatory Frameworks: Establishing robust guidelines to ensure the safe integration of AI technologies in medical documentation processes. Addressing these challenges, we can pave the way for more reliable and efficient medical coding solutions, ultimately improving patient care and operational efficiency. 🔗 Link to the full study https://lnkd.in/e2VWsyun Let's discuss how we can turn these insights into actionable solutions. #HealthTech #ArtificialIntelligence #MedicalCoding #DigitalHealth Eyal Klang Robbie Freeman Ali Soroush, MD, MS Ben Glicksberg Alexander Charney

CAIMC - Certified AI Medical Coder www.pmbausa.com The Certified AI in Medical Coding (CAIMC) credential is essential for medical coders in today's healthcare industry because it signifies proficiency in integrating artificial intelligence (AI) with traditional medical coding practices. As healthcare systems increasingly rely on AI for efficient data management and analysis, medical coders with CAIMC certification are better equipped to ensure accuracy, compliance, and optimization in coding processes. This certification reflects an understanding of AI's role in predictive analytics, natural language processing, and automation, enabling coders to enhance operational efficiencies and reduce errors. Additionally, CAIMC-certified professionals can navigate the complexities of medical billing and coding with an innovative approach, ensuring they remain at the forefront of industry standards and technological advancements. This makes them invaluable assets to healthcare organizations striving for excellence in patient care and financial management.

Can #LLMs excel in medical coding? Yes, with rich semantics and #clinicalAI. By leveraging rich clinical terminology and IMO Clinical AI to train LLMs, the accuracy of #medicalcoding can be greatly improved. Learn more: https://bit.ly/3yO2w6H #AIinHealthcare #HealthIT

Exciting read alert! 🚀 Check out this fascinating article on how AI is revolutionizing medical coding for physicians and coders. Dive in to discover: 🤖 The transformative power of AI in streamlining medical coding processes 💡 Insights into how AI technology is enhancing accuracy and efficiency 🏥 Real-world examples showcasing the benefits for healthcare organizations 🔍 Opportunities for leveraging AI to optimize coding workflows Don't miss out on this insightful exploration! #AI #MedicalCoding #HealthcareInnovation

Study Finds LLM Errors in Clinical Coding "Unacceptably Large" A new study from the Icahn School of Medicine at Mount Sinai has found that currently available (LLMs) like GPT-3.5, GPT-4, Gemini Pro and Llama2-70b are not sufficiently accurate to automate the assignment of medical codes for billing and research purposes. The researchers compared these state-of-the-art LLMs on their ability to match over 27,000 diagnosis and procedure codes to their official text descriptions from Mount Sinai patient records. They found that all models scored below 50% accuracy, with GPT-4 performing best at around 45% for diagnosis codes and 50% for procedure codes. The researchers determined the extent of errors was still "unacceptably large" for using LLMs to fully automate medical coding without human oversight. What does this mean? General-purpose LLMs have limitations for specialized healthcare tasks like medical coding. There is a need for more specialized, tailored AI models and tools specifically designed and refined for healthcare use cases. Integrating LLMs with expert clinical knowledge could improve their accuracy. Rigorous evaluation and iterative refinement will be critical before deploying AI The implications point toward a future where AI will likely play an increasingly important role in healthcare, but via specialized models that are thoroughly validated. General AI may augment human expertise, but is unlikely to fully automate complex clinical tasks anytime soon without significant refinement tailored to the healthcare domain. More like this? I invite you to my LinkedIn group, “Artificial Intelligence in Mental Health”—a vetted, science-based forum focused on the intersection of AI and mental health care, free from promotions or marketing. Link in comments. #ai #aihealthcare #healthcare #coding #LLM The Study: https://lnkd.in/gmaf_4Z8

Can #LLMs excel in medical coding? Yes, with rich semantics and #clinicalAI. By leveraging rich clinical terminology and IMO Clinical AI to train LLMs, the accuracy of #medicalcoding can be greatly improved. Learn more: https://bit.ly/3yO2w6H #AIinHealthcare #HealthIT

We tend to hear a lot about AI applications these days, and healthcare is no exception. But in areas like coding, the reality is that there's still a long way to go for this technology to be accurate. According to AAPC, "Unlike a well-trained medical coder, current AI systems do not have the contextual awareness required to decipher and extract pertinent information" from medical records. It's also worth noting that much of the information coders review can be subjective. https://lnkd.in/eFiFR5q7 The bottom line? AI is good at augmenting work, but not replacing it. If you need coding assistance, we have certified professionals who are ready to start a program. https://lnkd.in/g2tFSDat

𝐋𝐋𝐌𝐬 𝐚𝐫𝐞 𝐧𝐨𝐭 𝐫𝐞𝐚𝐝𝐲 𝐭𝐨 𝐚𝐮𝐭𝐨𝐦𝐚𝐭𝐞 𝐜𝐥𝐢𝐧𝐢𝐜𝐚𝐥 𝐜𝐨𝐝𝐢𝐧𝐠, 𝐬𝐚𝐲𝐬 𝐌𝐨𝐮𝐧𝐭 𝐒𝐢𝐧𝐚𝐢 𝐬𝐭𝐮𝐝𝐲 Researchers proposed that integrating AI with expert knowledge could automate medical coding to enhance billing accuracy and reduce administrative costs, but they found available large language models vague and error-prone. #healthcareprofessionals #medicalandhealthcare #Healthcareitnews #blsmedicalbilling #womensinhealth #healthcareit #rcm #doctors