Cory Costley’s Post

💥 Meet AKASA Medical Coding: The first GenAI assistant for coders 💥 Earlier this spring, we introduced you to Authorization Advisor, the first in a suite of generative AI (GenAI) revenue cycle assistants. Today, we’re building on that effort. 🚀 AKASA Medical Coding™ is a new groundbreaking mid-cycle solution that puts the power of GenAI at medical coders’ fingertips, enabling them to work with unprecedented efficiency, accuracy, and comprehensiveness. The end result? More accurate coding, more efficient teams, and more appropriate revenue — all at a lower cost. Driven by our proprietary GenAI technology and trained on each health system’s clinical and financial data, AKASA Medical Coding: 📑 Reads every relevant clinical document for every encounter (clinical notes, imaging reports, clinical observations, and more) ✅ Identifies recommended codes 🧠 Provides justifications (linked to supporting medical text) for coders to accept or revise 📊 Shares AI confidence level indicators to make the approval process more efficient 💪 Addresses multiple service lines, within the most complex inpatient settings 📖 Combines clinical and EDI data to create a cohesive patient story Welcome to the next generation of medical coding technology. See AKASA Medical Coding in action: akasa.com/coding

Introducing…AKASA Medical Coding! Thrilled to share the biggest product launch in AKASA history. This announcement has been years in the making, marking the culmination of not just everything that it took to launch AKASA Medical Coding, but a company-wide shift toward generative AI that brought this groundbreaking product to fruition. This was a head-first dive into the world of healthcare technology, and I couldn't be prouder of our dedicated team whose forward thinking and hard work made this possible. The real journey now begins. AKASA Medical Coding is flipping the script on an operating model that affects millions of people every day, ultimately making one of the most critical elements of healthcare more efficient and enjoyable for patients and staff. Will be reporting from the ground level with more as new events unfold. Thank you Malinka Walaliyadde, Varun Ganapathi, Benjamin Beadle-Ryby and Andy Atwal for your vision that's brought us to this point. To Kelly Leo, Tiffany Smith, Janae Newman and Arpana Priyadarshini 🐞 for countless hours that went into this launch. To Amy Raymond, Zach Barrett, Carolyn Satenberg, Esq., Ryan Roland, Cory Costley and Sanjay Siddhanti for the leadership that's enabling AKASA Medical Coding to make the impact it's capable of. To Rick Tillar, Justin Barlow, Christian Carlson, Chris Gifford, Betty Wei, Angela Glenn, Linsey Morningstar, Atticus Lynch and Erik Asmus for empowering health systems across the US to reshape their coding landscape like they never thought possible. #AI #artificialintelligence #medicalcoding #healthtech #GenAI #HealthcareTech #Innovation #ProudMoment #Teamwork #FutureForward #productmarketing #productlaunch #product

Significant product release today! AKASA is live with GenAI coding powered by our purpose built Large Language Model. There’s no lack of coding tools and vendors out there. However, most lean on rules-based systems with distinct limitations, resulting in lower accuracy or inflexible service-line viability.  GenAI can interpret surrounding words and get context, then choose a code that fits the criteria. This allows GenAI-powered coding tools to scan entire documents and physician notes quickly, determine what the codes are, then enter them AND compose justifications. Instead of simple keyword scanning, it’s actually reading and using contextual clues — just like a seasoned coder would do. Coders will not be replaced by GenAI, but coders who do not use GenAI will be outproduced and outperformed by coders who do.

I cannot say enough good things about AKASA or this amazing coding tool. It is truly new and different and can really help coders dig deep into the medical record to find every supported code. Its thoughtful interface is user friendly and has many features that make us coders jobs easier. My favorite part is the accuracy of the codes suggested and the support shown from the documents for each of them. I am so excited to continue my work with this awesome tool!

💥 Introducing AKASA Medical Coding™ 💥 It's FINALLY here and I've been waiting for years to talk about it! A bit of back story- AKASA has been researching AI in the coding space since I started here over four years ago. So when the newer advances in LLMs and GenAI came out, we were already deep in the topic and well poised to take full advantage of them. We've created a superior foundation model, informed by our customers' specific data, which yields trustworthy, reliable, and transparent code recommendations. It's shifting coders' work from reading the entire chart and finding codes to doing high-level validation of intelligent and informed comprehensive code recommendations. For all my mid-cycle friends:   💡 What if you could actually trust code recommendations from your coding tool? No more deleting everything and starting over! 🧠 What if your tool wasn't just built on tons of rules that need to be maintained and updated (or not updated because they get lost in the shuffle), but recommendations informed by actually reading and analyzing the entire patient record? No more ignoring codes because you don't know if all the rules have been updated! 🤓 What if in addition to all that, your tool provided transparency into its recommendations — citing quotes and pieces of the record justifying the recommendations, catching coding edits, and allowing easy navigation links through the record...all in one place? No more hunting and pecking for the right quote, the right piece of data to justify a code. A huge shoutout to the many people who have worked relentlessly to build this product (too many to name). But a special shout out to our coding SME Mindy Harris, RHIA, CCS, CDIP, CPC, CPMA, CRC for all your hard work here! #genai #revenuecyclemanagement #midcycle #coding #cdi #llm #ai #productivity #newproduct #launch #revenuecycleautomation # Benjamin Beadle-Ryby Malinka Walaliyadde Carolyn Satenberg, Esq. Andy Atwal Varun Ganapathi Cory Costley Zach Barrett Ryan Roland

Proud of the AKASA team for the monumental effort that went into developing our new GenAI-powered Medical Coding! Tune in to our Beckers webinar today at 1 CT to learn more: https://lnkd.in/geuGgWuU Among many thoughtfully designed aspects, it’s worth highlighting: a) The AI’s ability to show its work b) The AI’s rich understanding of each patient’s underlying clinical data and its ability to stitch together a cohesive patient journey — key for effective coding On point (a), I believe the key driver of AI adoption in healthcare will center around building trust in its suggestions. That’s why we invested heavily in ensuring our AI auto-generates a full justification for every code recommendation it makes — and takes it a step further by providing links to direct quotes from specific documents to support its “thought process.” These abilities are critical for both compliance and generating user confidence in the AI. It’s been deeply satisfying to show off AKASA Medical Coding and these features to health systems, and see their visible surprise and delight. Our new suite of GenAI-powered products is having a tremendous impact on the revenue cycle. More to come! Varun Ganapathi, Benjamin Beadle-Ryby, Andy Atwal, Amy Raymond, Carolyn Satenberg, Esq., Ryan Roland, Zach Barrett, Benjamin Brown, Kelly Leo, Tiffany Smith, Janae Newman

Embrace AI in medical coding, or face the risk of obsolescence. In the world of medical coding, AI is not just an advantage, it's a necessity for survival. According to the Certified AI Medical Coder-Must for Medical Coders (CAIMC), AI is transforming the medical coding landscape, and those who don't adapt may be left behind. Medical coding is a critical component of healthcare, and accuracy is paramount. With AI, medical coders can improve accuracy, increase efficiency, and reduce costs. AI can also identify patterns and trends that may not be apparent to human coders, leading to better patient outcomes. To stay competitive in the ever-evolving healthcare industry, medical coders must embrace AI. Don't be left behind - learn more about the benefits of AI in medical coding by visiting www.pmbausa.com.

Boosts Productivity and Revenue with AI Medical Coding Automation. Faced with complex challenges in emergency medical coding, a top U.S. health system partnered with CorroHealth. The result? A staggering $504M increase in annual revenue with a 98% surge in coding accuracy. This milestone exemplifies the potential of AI in enhancing operational efficiency, revenue integrity in healthcare, and the power of partnership. Whenever you see an AI solution touting coding productivity and accuracy with increased revenue, one must ask the question of whether the increased revenue, in this case, $540 million constitutes gross or net revenue. The old adage applies here, just because you code and bill for a specific DRG does not mean the payer pays as billed. Coding accuracy is not synonymous with optimal net patient revenue given the fact AI medical coding is only as good as the physician documentation available for the software to use as the basis of coding. Despite the fact that the CDI has been deployed in the majority of the hospitals and health systems throughout the country, in most instances the profession has not driven or achieved better physician documentation aside from capturing of CCs/MCCs that are often refuted by the payer. Why? Because CDI is focusing upon the wrong problem that leads to processes that delivers the wrong solution. If any hospital desires greater net patient revenue, then focus upon the culprit preventing the capture of net patient revenue, that is level of care downgrades, medical necessity denials, clinical validation denials, and DRG downgrades. Once physician documentation is complete and accurate, then "astounding" generation of optimal net patient revenue less prone to denials and costly financial takebacks will be achieved whether through AI coding or old fashioned coding by coders. Reach out to me if you want to learn how to transform a CDI program from reactive to proactive, focusing upon achieving proactive preemptive denials avoidance documentation that supports quality care and high performing revenue cycle. There truly is a better way to CDI. #codingproductivity, #codingaccuracy, #AI, #netpatientrevenue, #denials #denialsavoidance https://lnkd.in/efYtg4eh

Artificial Intelligence (AI) has been making significant strides in the healthcare industry, but it is unlikely to replace medical coders anytime soon. Here are some statistics and reasoning to support this claim: Medical coding is a complex task that requires human expertise and judgment. While AI can automate certain tasks, it cannot replace the human ability to interpret medical records and assign the appropriate codes accurately AI is not yet advanced enough to handle the nuances of medical coding. Medical coding requires a deep understanding of medical terminology, anatomy, and physiology, which is difficult for AI to replicate Medical coding is not just about assigning codes; it also involves reviewing medical records for accuracy and completeness. This requires human judgment and expertise, which AI cannot provide AI is a tool that can assist medical coders in their work, but it cannot replace them entirely. AI-assisted coding systems can help reduce the complexity of manual coding, but they still require human oversight and intervention The healthcare industry is highly regulated, and medical coding is subject to strict compliance standards. Human coders are better equipped to ensure compliance with these standards than AI In conclusion, while AI has the potential to transform the healthcare industry, it is unlikely to replace medical coders anytime soon. Medical coding is a complex task that requires human expertise, judgment, and oversight, which AI cannot provide at this time. However, AI can assist medical coders in their work and help improve the accuracy and efficiency of medical coding processes.

AI in healthcare is known for generating multiple successful outcomes like enhanced diagnosis and treatment, personalized medicine, efficient coding, predictive analytics and early intervention, and effective healthcare operations among others. Despite these, many healthcare providers are not able to integrate AI into their systems, especially to reduce repetitive tasks like medical coding. The reason behind slow adoption is the following misconceptions about AI. ❌Misconception #1: AI will replace medical coders. While AI automates certain tasks in medical coding, it won't replace medical coders entirely. The complexity of medical coding demands human expertise, encompassing in-depth knowledge of medical terminology, coding guidelines, and the healthcare ecosystem. AI, though powerful, is still evolving and cannot replicate the nuanced decision-making capabilities of human coders. ❌ Misconception #2: AI is only good for simple tasks. AI's potential extends far beyond simple data extraction. AI is evolving. It can now analyze complex medical data, identify patterns, and even predict patient outcomes. All of this is empowering healthcare professionals to make more informed decisions and provide personalized care. ❌Misconception #3: AI is not safe. Safety concerns around AI in healthcare are valid; however, AI works alongside human experts, augmenting their abilities rather than replacing them. As AI is still in development, extensive checks and balances are in place to ensure patient safety. Responsible implementation and ongoing collaboration between AI and human professionals remains paramount. AI is already revolutionizing patient care in various ways. At MEDICODIO we recognize the potential of AI to enhance medical coding outcomes. Our AI-based medical coding software empowers coders with increased efficiency and accuracy. If you're eager to explore how AI can improve healthcare, visit our website or reach out to us. We're ready to address any questions or concerns you may have.