John Harbison

I appreciate these comments from Mitch Kwiatkowski. While I'm a big proponent of AI opportunities in Healthcare, I also believe there needs to be more transparency. It's true we're beginning to move in the right direction but it's also true that we need more faster too. Practicing Responsible AI efforts are part of the solution but industry requirements and regulation need to have a role too. #aiethics #responsibleAI #dataethics

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🌟 JUST DROPPED 🌟 Tune in as Charlie Harp explores the taxonomy of the PIQI, Patient Information Quality Improvement, framework. 📊 He breaks it down into two primary categories and four subcategories to show how they are used to analyze and address healthcare data quality challenges. Listen to the full episode now: https://hubs.ly/Q02xhsst0 Stay tuned for our next episode, where Charlie talks about Simple Assessment Modules (SAMs) and the elegant simplicity of recursive failure. #HealthTech #HealthcareIT #DataQuality #Interoperability

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Buried lately so a bit tardy with my monthly EAI newsletter here on LI, but it's coming along. Thought I would share a clip that may be if interest--> As the Big Techs grew rapidly over the past 25 years, so did their R&D budgets, so it should be no surprise that most of the world’s R&D spend in advanced IT revolves around the strategic interests of a small group of Big Tech companies. In 2022, for example, Amazon, Alphabet, Meta, Apple and Microsoft collectively spent $202 billion in R&D, with Amazon leading at over $73B followed by Alphabet at nearly $40B. The next five biggest R&D spenders listed on NASDAQ only spent only $20B, which includes Nvidia at $7.3B. For perspective, the entire R&D budget for the U.S. Federal Government in 2022 was about $180B across all agencies. So, when people describe Big Techs as sovereigns (as in nations), the tech oligopoly is actually larger than the largest economy in terms of R&D, and it's of course invested in their strategic interests to protect moats, avoid disruption, and continue to grow. LLM chatbots are the latest and perhaps greatest example of extreme behavior in strategic investments by Big Tech companies.

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Learning some Python Dash. Which sent me down the rabbit hole of Python Decorating. This guide was super well written, and super thoughtful. https://lnkd.in/gN-25FHK

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Starting July 1st, 2024, hospitals across the United States will need to comply with new mandates to the CMS hospital pricing transparency laws. One of the critical requirements is the standardization of data elements and formats. Hospitals' Machine-Readable Files (MRFs) must now conform to the CMS template layout and data specifications (180.50(c)(2)). This change is aimed at ensuring that pricing information is presented in a clear, consistent, and accessible manner for patients. Furthermore, standardization will allow CMS to better monitor compliance through automating filtering for compliant vs. complaint form factors of pricing transparency data. Automating this compliance is our specialty at SumHealth! We understand the complexities and challenges that come with these new regulations. To help hospitals meet these requirements effortlessly, we have developed an advanced system designed to ensure automatic compliance while anticipating future mandates and preparing your care system to enforce those as well. Our solution simplifies the process by automatically formatting and submitting pricing transparency data in the form of mandated “Machine Readable Files” or MRFs according to CMS specifications, thereby reducing the administrative burden on hospital staff and mitigating the risk of extensive fines. We Are Doing This by Automating the Important Key Features of the New Regulations: 1. Standardized Template: Hospitals must use the CMS template layout for their MRFs, ensuring uniformity across the board. 2. Data Specifications: All data must adhere to CMS-defined specifications, making it easier for patients to understand and compare pricing information. 3. Comprehensive Data Elements: Hospitals are required to provide detailed information, including hospital and MRF specifics, each type of standard charge (payer-specific negotiated charges by payer and plan), item/service descriptions, and relevant billing codes (180.50(b)(2)). By choosing SumHealth, any care system can rest assured that their pricing transparency data will always be up to date and compliant with the latest CMS requirements. Our system not only addresses the current regulations but also anticipates future changes, ensuring that hospitals are always ahead of the curve. Let us help you navigate these new laws with confidence and ease, so you can save on fines and invest in both your patients and care teams. #Healthcare #AI #technology #Compliance #pricetransparency #SumHealth

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The single MOST important news in AI for this month, perhaps this year & a new defining moment for this decade is here - Safe SuperIntelligence Inc. Ilya Sutskever - the brain behind some of the most advances within AI in past decade, who failed to oust his co-founder Sam Altman at OpenAI last year, started this new company ssi.inc where they plan to not only showcase that #AGI is within reach, but also how #SSI is within reach while keeping it "Safe". "Safe" - a word spoken far less than #AI & #LLM in past two years but is considered critical for safety of future humans - is becoming the basis of this start-up. This is the first tangible step anyone has taken in past 1.5 years towards AI safety. While many have raised alarms, none took any concrete steps. And I loved its website containing just one short webpage write-up. Worth a read. Shows the current state of world of AI & world. #Elon started another company #xAI - a competitor to OpenAI. Got one of the largest funding in Series B - $6bn. Has now setup the largest data center "Gigafactory of Compute" along with Dell Technologies with upto 100K NVIDIA Hoppers-100 which is nearly 4 times larger than largest GPU clusters till date, like Meta's. But he hasn't mentioned a thing about safety - which he vehemently spoke about last year to halt all work on AI development when #ChatGPT was released. Hopefully, he is also leading the work in this direction (apart from for-profit work of utilizing all the data on Twitter/X and leveraging it to build one of the largest financial institution of the future) #AISafety

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Payers grapple with inaccurate data like other organization. Unfortunately it's a common problem, and it's the first barrier to care. If you can't contact someone, you can't engage, and #HealthOutcomes can suffer. Read this white paper to learn about next generation data and how other organizations are using it. https://splr.io/6040lK1Vj

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Have you been following the news about compromised credentials being exploited to quickly steal tons of data from Snowflake customers, most notably Ticketmaster and LendingTree? Snowflake's response has been to point back at their shared responsibility matrix and say customers chose not to enable MFA, leading to compromise. You can read more details here at TechCrunch: https://lnkd.in/gFwm92Ag ❓ So here's my question❓ Do you think cloud and SaaS vendors should mandate MFA or force their customers to opt out of standard security controls? Or should customers still be required to opt in during setup? In my opinion, Snowflake is completely overdue for making its default settings more secure. In light of these breaches, it should do more, like making compromised and unused accounts inactive. Why? Fundamentally, Snowflake's business strategy targets large, highly critical, highly sensitive data sets. Snowflake's success has come in part from growing partnerships with Health-tech and Healthcare companies over the last several years. It is a relied-upon tool for accessing and analyzing critical data sets quickly and easily, sometimes including sensitive health and financial data. That's why it's disconcerting to see Snowflake boast about significant partnerships with leading and emerging cybersecurity companies at #RSAC last month, while seemingly reluctant to be a true partner in security by not doing more to enforce best (read: standard) security practices. Last, this isn't a lesson that needs to be learned the hard way. The major cloud vendors and productivity apps have all already learned this lesson in very public ways and have come to similar conclusions - the more the modern economy relies on your product the more responsibility you have in protecting your customers, even from themselves. What do you think? Should SaaS companies should be held more responsible for securing customer data the more critical/successful they become, even if that means giving customers less choice? A recent example is Slack requiring MFA. Clearly, I do, and that's why I'm so thankful for the Blackwell team working on #MHXDR - we're focused on threat detection across all tech critical to Healthcare and the response needed to quickly identify and close gaps in standard security practices. #cybersecurity #securebetter #securitypartner #blackwellsecurity

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I just finished presenting at the HL7 FHIR DevDays, Minneapolis about why openEHR makes more sense for primary data capture, and storage of clinical data, and how this data can then be mapped to different FHIR profiles. Lots of questions, and lots of interesting discussions. I have another talk tomorrow again comparing the data modelling approaches using openEHR templates and FHIR Profiles. Health IT experts on LinkedIn, given the recent announcement on the collaboration of HL7 and openEHR, do you have any ideas on how the two communities can work together? Grahame Grieve, Ewout Kramer, Josh Mandel, MD, Virginia Lorenzi, Sebastian Iancu, Thomas Beale, Ian McNicoll, Heather Leslie, Nikolai Ryzhikov, Pawan Jindal, MD, Dan Caron, Tomaz Gornik, Bostjan Lah, Diego Boscá Tomás, Birger Haarbrandt, Christian Chevalley, Erik Vermeulen, Erik Sundvall, Joost Holslag, Dr. Sebastian Garde,

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Safety is a TOP priority here at RevDoc, and that is why you can rest assured knowing all RevDoc Providers go through a rigorous verification process before seeing any patients. This process includes: ✅ An in-depth background check ✅ Verification of all licenses and education ✅ HIPAA Training Only medical providers which pass all required steps of verification are allowed to see RevDoc patients. Patient safety comes first 🧡 Download the RevDoc App to schedule with a verified Provider today! 📲 #RevDoc #DoctorAtYourDoorstep #health #safety #wellness

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I have an amazing team of data scientists at Labcorp. Many of them can work shoulder-to-shoulder with data scientists at the big tech companies, but they see our mission to improve health and improve lives and know we are onto big things. With that being said, the most interesting thing about the GenAI revolution is that: 1) the longest tenure of anyone with GenAI skills is 18 months. 2) the skillset is brand new, and NOTHING like previous gen AI where we spent our time training models using convoluted python code and linear algebra, not consuming models. 3) I like to think that teachers have the greatest pre-requisite skill set: being concise, clear, and setting objective expectations is the key to working with GenAI. And I'm not talking about using GenAI to write your emails or send a snarky response to an incompetent coworker (promise I've never done this!). GenAI seems scary to many - but the skills required to use daily are within reach of virtually anyone. My advice: find a course and get started. 😎

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Meta delays plans to train AI using European user data | ITPro This is great for the EU, but what about the US? Truth is, once you’ve trained AI on a given set of data, it’s in there and its not coming out of the LLM (Large Language Model) upon which the AI relies, any more so than you can distill and extract the baking soda from a cake mix, after all the ingredients have been blended together. This is an especially interesting – and challenging issue – since we already know that many websites are “scraping” data to train their AI models, arguing that anything posted publicly (i.e., publicly available) is fair game for training an AI model. But the law is by no means settled in this scenario, and there are plenty of lawsuits still winding their way through courts on whether information posted on social media can be “scraped” by other websites. After all – as some courts have observed – posting on your Facebook profile for all to see, may not involve the same intent as expecting that info to be scraped up and used to train AI, since once that scraped data is integrated into the AI’s LLM, no privacy right, no state law and no lawsuit is going to ever be able to reverse that action. Returning again to my cake analogy, trying to remove scraped data after its been integrated into an LLM and used to train an AI model is like trying to identify, distill and pull out the vanilla extract from a cake, after its been mixed, baked and frosted. You know its there – and you can even taste it – but you’ll never be able to precisely identify, target and remove it from the baked and frosted dessert. #AI #LLMs #scraping #datascraping #privacy

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Literacy matters. If you don’t trust a dashboard because it shows broken data why would you trust a GenerativeAI model that was trained with the same data? GenerativeAI models are built from data and we all know that garbage in, garbage out. If you want high quality AI models, aim for high quality data as a foundational piece of your data and AI strategy.

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Take your practice to the NEXT LEVEL with Nextech's EHR software! Discover how Connecticut Retina Consultants optimized their practice. Mary Baglin, Office Administrator, shares, "We love the template setup in Nextech’s EHR software. We can individualize templates for each of the doctors within the practice. Nextech just understands what ophthalmologists need in their practices." Explore their success story in our full case study! https://ow.ly/HFCN50RFQ9F #ophthalmology #ehr #healthcare #casestudy

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Have you been tuning in to the Data Quality in Healthcare: PIQI framework podcast series? If not, you should be! 🌟 The 5️⃣ part series covers several use cases for this framework and emphasizes the need for a flexible system that can evaluate data using various criteria. Catch up now as our host, Charlie Harp essentially builds the PIQI framework throughout each episode in this series providing an innovative tool that identifies patterns in data to pinpoint quality-related issues. https://hubs.ly/Q02CvS3b0 #HealthcareIT #DataQuality

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To fully appreciate what document management software can offer, you need to know what's at risk - not just for your patient, but for your organization as well. Here's what can occur if you violate HIPAA regulations. https://hubs.li/Q02rbWTY0 #DMS #InformationSecurity #HIPAA #HealthTech

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Today, we're diving into the cutting edge of Retrieval-Augmented Generation (RAG), a paradigm shift that's turbocharging LLMs with the vast knowledge of your BigQuery data. Think about it: 🤯 - LLMs are powerful, but they're only as good as the data they're trained on. - BigQuery is a data powerhouse, holding the keys to your unique insights. Enter RAG! By seamlessly integrating BigQuery with your LLMs, we unlock a whole new level of intelligence. 🧠 We dive deep into the mechanics, in this blog post we cover: - Understanding RAG's potential: Explore how it transforms LLMs from generic models to highly specialized experts in your domain. - Practical implementation: Learn how to seamlessly connect BigQuery and your LLM, leveraging Google's tools for effortless integration. - Real-world use cases: Discover how RAG unlocks powerful applications, from personalized recommendations to cutting-edge research. Click the link below to read the blog post and discover how to unleash the full potential of your LLMs: https://lnkd.in/e8jacn9a Also see the other blogs in the series and watch this space for more: 1 - Multimodal input to Gemini to BigQuery Schema 2 - Creating marketing campaigns using BigQuery and Gemini models https://lnkd.in/enJ5rxec Watch the video https://lnkd.in/ekzeNcs9 hashtag#RAG hashtag#BigQuery hashtag#LLMs hashtag#AI hashtag#DataAnalytics hashtag#GoogleCloud hashtag#Innovation hashtag#FutureofAI hashtag#LLM hashtag#Gemini hashtag#BigQuery hashtag#datawarehousing hashtag#Cloud hashtag#marketing hashtag#cooldemos hashtag#bigdata hashtag#lakehouse hashtag#genai hashtag#ai hashtag#artificialintelligence hashtag#datamodelling hashtag#google hashtag#dataengineering hashtag#analytics hashtag#ml hashtag#GenerativeAI hashtag#BigQuery hashtag#DataAnalytics hashtag#AIInnovation hashtag#GoogleCloud hashtag#DataScience hashtag#MachineLearning hashtag#gemini hashtag#vertexai hashtag#agents

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