Streamline Health的动态

The buzz around artificial intelligence is reaching new heights! According to an Oliver Wyman-New York Stock Exchange Survey, 96% of executives from NYSE-listed companies see AI as a business opportunity, not a risk. In the health and life sciences sector, 100% of respondents view AI as a significant opportunity. Healthcare professionals are particularly optimistic about AI's potential to enhance workforce productivity (88%) and operational efficiency (75%). The automation of manual tasks is expected to drive most of these gains, with additional benefits in diagnostics and personalized treatment recommendations. AI holds the promise of improving health outcomes, medication adherence, reducing unnecessary utilization, and ultimately contributing to longer and healthier lives.

???? Assessing the accuracy and readability of AI chatbots in answering patient questions about low back pain ?? Simone P S Scaff ???? https://lnkd.in/ecVgbGGg ?? Focus on data insights: - ?? 55.8% of chatbot responses were accurate, indicating moderate reliability. - ?? Treatment and self-management topics had the highest accuracy rates. - ?? Responses were deemed 'reasonably difficult' to read, with a mean Flesch Reading Ease Score

???????? π???????????? ????????! I genuinely believe in the benefit of the use of responsible AI in general, but especially in healthcare, for our society. Therefore, I strongly think that it needs to be easy to design, develop, test, deploy, and use AI - in a responsible way. That’s why?I?joined QuantPi, almost two years ago. The best word to describe my daily work at QuantPi is probably - diverse :) My daily work ranges from: ?? staying up to date with responsible AI regulations, requirements, best-practices that are of relevance to organizations (EU AI Act, NIST, CHAI, FDA Diversity Action Plan, HHS playbook, …), ?? making them quickly actionable and valuable within QuantPi’s solution, the AI Trust Platform, ?? staying up to date with AI (and the corresponding challenges in leveraging AI for organizations) ?? to eventually be able to understand the specific needs and challenges of organizations in operationalizing the responsible AI process at scale and identifying how we can support them ?? and last but not least to exchange internally with our incredible and very diverse team to improve our very diverse and holistic solution: process-wise, content-wise, and testing-wise. ?? and all the other tiny things we all do in between … Oh yes, I also usually work in very diverse locations within our office - on the floor, at a random desk, or at the roof top :) What can I say - I enjoy it :) Thank you my dear QuantPi Team for making work such fun ?? You join because of the mission, but you stay because of the people and culture.

RAG is a promising approach to reduce hallucinations in LLMs, but it's not perfect. Even with RAG, LLMs can still produce information that's unsupported or contradictory to the context. This can cause big problems in important areas like: ?? Medical misdiagnosis from wrong health information ?? Poor financial decisions based on fabricated data ?? Spread of misinformation in news and research Let's welcome Lynx by Patronus AI: The new leading in hallucination detection model. The new state-of-the-art hallucination detection model outperforms GPT-4, Claude-3-Sonnet, and other leading LLMs in hallucination detection. ?????? ????????????????????????: ? Lynx (70B) outperforms GPT-4o by 8.3% on medical inaccuracy detection ? Lynx (8B) beats GPT-3.5 by 24.5% and Claude-3-Sonnet by 8.6% on HaluBench ? Lynx (70B) surpasses GPT-3.5 by 29.0% across all tasks ?????? ???????? ???? ????????: ? they developed HaluBench: 15k benchmark covering real-world domains like finance and medicine ? they fine-tuned Llama-3-70B for explainable, human-like reasoning ? they implemented Chain-of-Thought approach to catch subtle hallucinations Congrats to Rebecca Qian and Anand Kannappan for launching this great new model!

????????????????????! This new publication from a team at Kaiser Permanente is packed with insights on using ambient AI for documentation. One aspect I particularly appreciate is the inclusion of these 10 factors used to evaluate AI scribes: Using a modified version of the Physician Documentation Quality Instrument (PDQI-9), participants rated these AI-powered scribes on a scale from 1 (?????? ???? ??????) to 5 (??????????????????) based on the following attributes: ?? ???????????????? – The note is true. It is free of incorrect information. ?? ???????????????? – The note is complete and free from omission and documents all of the issues of importance to the patient. ?? ???????????? – The note is extremely relevant, providing valuable information and/or analysis. ?? ?????????????????? – The note is well-formed and structured in a way that helps the reader understand the patient’s clinical course. ?? ???????????????????????????? – The note is clear, without ambiguity or sections that are difficult to understand. ?? ???????????????? – The note is brief, to the point, and without redundancy. ?? ?????????????????????? – The note reflects the AI scribe’s understanding of the patient’s status and ability to develop a plan of care. ?? ???????????????????? ???????????????????? – No part of the note ignores or contradicts any other part. ?? ???????? ???????? ?????????????????????????? – The note is free of hallucination and only contains information verifiable by the transcript. ?? ???????? ???????? ???????? – The note is free of bias and contains only information verifiable by the transcript and not derived from characteristics of the patient or visit. ?????? ???????????? ???????? Leverage this survey tool for rapid and uniform assessment of AI documentation technologies. It simplifies gathering team feedback and ensures purchasing decisions are well-informed. ?? (Link to the source in the comments.)

Could AI revolutionize the public sector? Forrester's Principal Analyst Sam Higgins shares real-world examples of how public sector orgs are using AI today and the biggest challenges they face.

Could AI revolutionize the public sector? Forrester's Principal Analyst Sam Higgins shares real-world examples of how public sector orgs are using AI today and the biggest challenges they face.

Could AI revolutionize the public sector? Forrester's Principal Analyst Sam Higgins shares real-world examples of how public sector orgs are using AI today and the biggest challenges they face.

Could AI revolutionize the public sector? Forrester's Principal Analyst Sam Higgins shares real-world examples of how public sector orgs are using AI today and the biggest challenges they face.

Given the widespread discussion around GenAI, it's clear that its impact is profound and far-reaching. Yet, adopting generative AI has been a significant challenge for many organizations. Gartner's insight highlights this struggle, predicting that at least 30% of generative AI projects will be abandoned throughout the next year due to issues like poor data quality and unclear business value. This prediction alone paints a picture of the adoption hurdles. Email: [email protected] #homemedicalsupplies #healthcare #rcmservices #backlog #claim #medicalcoding #cptcoding #hcccoding #hcpcs #businesspartner #outsource #business #offshoreservices #medicalbilling #ushealthcare #insuranceclaim #hme #DME #RCM