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⚡️Digital Health Wire 319 is now live! -Bessemer Venture Partners’ Key Benchmarks for Health Tech -Notable Introduces Flow Builder -Medical Body Cams  + digital health news from: CredibleMind Nsumba Solomon Ludwig Schmidt Kelly Michaelsen Panda Health Hello Patient Function Health Thrive Global Nabla Aaron Boussina Gabriel Wardi Hayden H. Pour Amy Sitapati Chad VanDenBerg, FACHE Karandeep Singh Christopher Longhurst Shamim Nemati https://lnkd.in/emrVgW3D

UC San Diego Health drives innovations that build the future of health care. As an academic health system, we quickly find practical applications for leading-edge technologies that can improve health outcomes for our patients. Leveraging artificial intelligence (AI) tools helps us to enhance patient safety and quality, while streamlining workflows to allow physicians to focus on what matters most—direct patient care. The results of a pilot study led by a team of UC San Diego School of Medicine researchers and published by the New England Journal of Medicine AI showed the potential of an AI system using large language models (LLMs) to make the reporting of hospital quality measures more efficient and reliable. Compared to a manual chart review, LLMs were found to make the reporting process faster and minimize human error. The technology could speed up quality improvements and reduce the time physicians spend on reporting tasks so they can spend more time practicing medicine. Read more about the study at the link below. https://lnkd.in/gftMtqbu

Our most recent Dean’s Council of Advisors meeting energized me greatly. Why? Because I could feel new opportunities being created at the intersection of engineering and the actual practice of healthcare. (Thank you to all speakers and to everyone who attended!) My excitement stems from moments of direct dialogue between our UC San Diego Jacobs School of Engineering faculty and physicians from the Joan & Irwin Jacobs Center for Health Innovation @ UC San Diego Health (JCHI). One of this Center’s early successes is the roll out of an artificial intelligence (AI) model in the emergency departments at UC San Diego Health in order to quickly identify patients at risk for sepsis infection. The term actionable information returned again and again throughout the Dean’s Council meeting. We identified many opportunities to collaborate in order to arrive at actionable information that could be leveraged to save lives, while also improving the practice of healthcare. At one point during the Dean’s Council meeting, the conversation turned specifically to what kinds of wearable health monitoring systems could be game changers for improving how patients interact with their healthcare provider. The consensus was that non-traditional research collaborations are required to get engineering faculty at the same table as the teams with influence on what flows into electronic health records.  At that moment, I felt something. The engineers, the physicians and Dean’s Council members – many with hard-won wisdom from industrial R&D experiences – all clicked onto the same wavelength. There are huge opportunities to be created here. In the coming months, we will stand up an institute for healthcare engineering here at the Jacobs School of Engineering. This institute will help to empower our Jacobs School faculty to partner with JCHI and others on projects aimed at delivering actionable information in healthcare contexts. If you are inspired to learn more or get involved, please reach out. Behind the scenes work has begun, and I look forward to sharing much more next year. Read my full Dean's column and see the entire November newsletter in the link below: https://lnkd.in/gVEQcVTE

For hospitals facing immense pressure to report quality measures accurately while managing tight resources, implementation of large language models (LLMs) could prove incredibly useful. Currently, quality reporting is both time-consuming and costly—some estimates show it can cost hospitals millions annually and require thousands of work hours. By automating elements of the process with LLMs, the authors of the NEJM AI Case Study believe that hospitals can drastically reduce the manual workload involved in reviewing patient charts, freeing up staff to focus more on patient care. If properly implemented and monitored, LLMs can help streamline the complex, 63-step SEP-1 reporting process, making it easier for non-clinical staff to gather the required data quickly and accurately. For clinical staff, this means fewer disruptions in their workflow, less time spent on data entry, and more timely feedback on patient outcomes. This can lead to faster implementation of quality improvements, ultimately improving patient care. Moreover, the authors envision that by using LLM-based systems human error and variability in reporting can be reduced, which would ensure consistency across different staff members and hospital sites. While these advancements can boost reliability and can provide hospital administrators with more accurate data for decision-making - it’s not just about efficiency - it’s about creating a more reliable and less stressful environment for hospital staff, enabling them to dedicate their energy to improving health outcomes. Kudos to Aaron Boussina, Rishivardhan K, Kimberly Quintero for their insights and interests in leveraging technology to make health systems more efficient and ultimately better for patients.

A Johns Hopkins study found that measuring 162 quality metrics cost over $5 million. Our team at UC San Diego Health, led by Drs. Aaron Boussina and Shamim Nemati, found that large language models are a great way to scale quality measurement, which is a key prerequisite to achieving a learning health system. Paper: https://lnkd.in/eURgCJa8 Story: https://lnkd.in/eWNYuEDd Christopher Longhurst Atul Malhotra Rishivardhan K Gabriel Wardi Kimberly Quintero Hayden H. Pour Nicholas (Nick) Hilbert, MSN, RN Amy Sitapati Mike Hogarth, MD, FACMI, FACP Chad VanDenBerg, FACHE Shreyansh Joshi

Lots of hype at HLTH Inc. about #AI models and their effective deployment in clinical settings. 💡 👇 Exciting news from NEJM AI: A UC San Diego study led by Drs. Aaron Boussina and Shamim Nemati found that #LLM models are a great way to scale #quality measurement, which is a key prerequisite to a learning health system. A Johns Hopkins Medicine study found that measuring 162 quality metrics costs hospitals over $5 million annually. The evolution of quality metrics through the adoption of #interoperability standards and AI offers a promising way to reduce costs associated with manual chart reviews, thereby reallocating precious time to health care quality initiatives. #healthcare #artificialintelligence #qualitymetrics #innovation #hlthusa The story: https://lnkd.in/eWNYuEDd The paper: https://lnkd.in/eURgCJa8

Congrats to Jonathan Lam for his recent study published in JAMIA, highlighting the need for timely diagnostic lab assessments to improve #sepsis diagnosis and patient survival. In collaboration with Aaron Boussina, Supreeth Shashikumar, Bob Owens, and @Chris Josef. #Sepsis #Healthcare #MedicalResearch #DataScience #PatientCare #JAMIA

Great to see KP on the list of health systems in the US "that are pioneers in demonstrated, outcomes-based AI solutions." While so many companies are tripping over themselves in positioning themselves as "AI-first" the secret for KP's success is that we're decidedly NOT AI-first. We're a people-first organization. Our core mission is delivering high-quality, affordable and equitable care for our 12.5M members and to make KP the best place to work for our 230k+ employees. We deploy technologies to drive those outcomes and we use AI, sparingly and cautiously, when simpler technologies can't get the job done. AI is also a team-sport and KP's success in this area reflects the work of thousands of employees across the organization. People like Jennie Shin who led the national IT team deploying our ambient scribe technology to 25,000 clinicians or Anna Davis and Carol Cain who led our quality assurance testing for our roll-out. And it's particularly gratifying to be recognized alongside my close colleague and friend Vincent Liu. His leadership in AI at KP has been both sustained and truly outcomes-driven. There are many other physician leaders that also deserve to be recognized - Khang Nguyen, Kristine Lee, MD, Brian Hoberman MD MBA, Dr. Ainsley MacLean, Chris Cable, MD, Dinh Nguyen, MD, MSHI and so many more.