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
Henry Watson’s Post
More Relevant Posts
-
Autonomous medical coding has been viewed as the province of large academic medical centers that could afford to experiment with cutting-edge technology. Today it is starting to be viewed as a necessary tool for all health systems. #AI #healthtech https://hubs.ly/Q02nQyRB0
How AI is transforming medical coding for physicians and coders
healthcareitnews.com
To view or add a comment, sign in
-
I've been diving into the world of #AI lately, and I'm excited about its potential to transform various fields, including medical coding. Implementing AI tools could help coders efficiently sift through essential details in medical records, leading to improved productivity and accuracy, as well as enhanced job satisfaction. However, I urge caution when exploring new AI applications. The term "AI" encompasses a wide range of technologies, and it's crucial to thoroughly evaluate and refine use-cases. We must also remember that the human element in this process is vital and should never be underestimated. #GenAI #AI #RCM
Revenue cycle leaders see gen AI's medical coding potential
beckershospitalreview.com
To view or add a comment, sign in
-
AI might seem like an appropriate tool for medical coding, but so far the results haven't been promising. "The investigators reported that all of the studied large language models...showed limited accuracy (below 50 percent) in reproducing the original medical codes, highlighting a significant gap in their usefulness for medical coding." https://lnkd.in/etGBBiw2 The bottom line? If you want accurate medical coding, you need certified, experienced coders: https://lnkd.in/g2tFSDat
AI models fall short in medical coding accuracy
news-medical.net
To view or add a comment, sign in
-
Director of the Generative AI Research Program, Division of Data-Driven and Digital Medicine (D3M) at Mount Sinai
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
Chief, Division of Data Driven and Digital Medicine (D3M) and Director, Charles Bronfman Institute of Personalized Medicine at the Mount Sinai Health System | AI | Healthcare | Data Science | Digital Health
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
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
ai.nejm.org
To view or add a comment, sign in
-
Chief, Division of Data Driven and Digital Medicine (D3M) and Director, Charles Bronfman Institute of Personalized Medicine at the Mount Sinai Health System | AI | Healthcare | Data Science | Digital Health
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
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
ai.nejm.org
To view or add a comment, sign in
-
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.
Chief, Division of Data Driven and Digital Medicine (D3M) and Director, Charles Bronfman Institute of Personalized Medicine at the Mount Sinai Health System | AI | Healthcare | Data Science | Digital Health
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
Large Language Models Are Poor Medical Coders — Benchmarking of Medical Code Querying
ai.nejm.org
To view or add a comment, sign in
-
I'm thrilled to share the latest blog post I wrote for Qantev, focusing on automated medical coding—the crucial task of assigning ICD and CPT codes based on clinical text. This piece particularly explores the use of foundational Large Language Models (LLMs) straight out-of-the-box, along with their intrinsic capabilities in medical contexts. "[...] in November 2023, Microsoft published an article comparing models trained with special-purpose tuning (like Google’s Med-PALM 2, BioGPT) to generalist foundation models such as GPT-4, utilized directly for medical-related queries. Up until then, Google’s Med-PALM 2 had reached an impressive 86.5% on the MedQA dataset, which features questions from the US Medical Licensing Examination. The latest insights, however, set a new benchmark: GPT-4 achieved a 90% score by leveraging innovative prompt engineering techniques known as Medprompt." In this post, we investigate how foundational LLMs can be adapted for this intricate task and discuss some of the pioneering developments we are working on at Qantev in this area. I hope you enjoy it!
🚀 Exciting blog article from Qantev AI Research Team! 🚀 We've just published the latest installment in our blog series on the transformative power of AI in medical coding. In this deep dive, we continue our exploration of the burgeoning field of automated medical coding, comparing traditional supervised models like PLM-ICD with innovative strategies leveraging Generative AI and prompt engineering. Our findings discuss the significant advancements and potential benefits of using foundational Large Language Models (LLMs) for medical code inference, especially in handling rare codes and navigating complex ICD ontologies without specialized tuning. This approach not only improves accuracy but also enhances efficiency in medical coding processes. Discover how Qantev is at the forefront of this technology, shaping the future of health insurance operations with AI-driven solutions. We're not just following the trends—we're creating them! 🔗 https://lnkd.in/eHADbppc #AI #HealthTech #MachineLearning #MedicalCoding #Innovation #healthinsurance #healthclaims
Automated Medical Coding — Part II
medium.com
To view or add a comment, sign in
-
🚀 Exciting blog article from Qantev AI Research Team! 🚀 We've just published the latest installment in our blog series on the transformative power of AI in medical coding. In this deep dive, we continue our exploration of the burgeoning field of automated medical coding, comparing traditional supervised models like PLM-ICD with innovative strategies leveraging Generative AI and prompt engineering. Our findings discuss the significant advancements and potential benefits of using foundational Large Language Models (LLMs) for medical code inference, especially in handling rare codes and navigating complex ICD ontologies without specialized tuning. This approach not only improves accuracy but also enhances efficiency in medical coding processes. Discover how Qantev is at the forefront of this technology, shaping the future of health insurance operations with AI-driven solutions. We're not just following the trends—we're creating them! 🔗 https://lnkd.in/eHADbppc #AI #HealthTech #MachineLearning #MedicalCoding #Innovation #healthinsurance #healthclaims
Automated Medical Coding — Part II
medium.com
To view or add a comment, sign in
-
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
AI Will Not Replace Medical Coders
aapc.com
To view or add a comment, sign in
-
President at CareAllies – a Cigna company | Focused on connecting care between patients, providers, and payers through value-based care solutions
Jay Aslam’s important work in AI, and his views on its evolving value and application, is worth noting. Business processes, coding being one, are areas where I believe we will see the greatest initial application of AI, even as we develop the guardrails that are needed to ensure it can be trusted and effectively applied in more clinically-oriented use cases. Healthcare IT News https://bit.ly/3Ti3MW7
How AI is transforming medical coding for physicians and coders
healthcareitnews.com
To view or add a comment, sign in