Jean Barmash

Reimagining Healthcare Workforce Platfforms: Turning Capital Into Labor The future isn’t just about filling shifts. It’s about scaling labor intelligently, driving better outcomes for systems, staff, and patients alike. Andreessen Horowitz and Alastair (Alex) Rampell reinforced this Vision in a recent post and it applies to the next phase of healthcare workforce evolution. Healthcare workforce marketplaces plus AI are at the tipping point of transformation, evolving into systems of record that don’t just manage labor—they optimize it. Much like EHRs centralized patient care data, these marketplaces can centralize workforce operations, aligning talent supply with clinical demand. But there’s a bigger opportunity here: turning capital into labor. Here’s what I mean. Historically, industries (including healthcare) have focused on enterprise computing tasks—streamlining data entry, compliance, and back-office functions. While valuable, this approach often stops short of transforming the real-world, repetitive processes that make up the bulk of work in healthcare. Think beyond enterprise software. The next frontier lies in leveraging embodied AI and workforce marketplaces to automate and enhance labor-driven processes, transforming capital investments into productive labor outputs. For healthcare, this means turning staffing costs into scalable, dynamic systems that deliver value far beyond filling shifts: 1. Dynamic Labor Matching: Capital invested in marketplace platforms enables real-time matching of labor supply with patient demand, replacing outdated scheduling models with predictive, automated systems. 2. Workflow Automation: From credentialing to compliance and shift bidding, embodied AI can streamline traditionally manual tasks, freeing human resources for higher-value work. 3. Labor as a Strategic Asset: Workforce marketplaces shift the focus from static staffing budgets to proactive labor deployment, ensuring that every dollar invested in labor drives outcomes and efficiency. It is about the billions of repetitive, planned tasks that AI can improve. In healthcare, that means automating everything from staffing logistics to clinical task assignments, creating a horizontal system of engagement for labor (think Zendesk) that integrates seamlessly with vertical, workflow-specific platforms (think Toast for staffing operations). The result? Hundreds of billions in untapped market potential—and a healthcare workforce model that transforms how care is delivered. By turning capital into labor through smarter marketplaces and automation, we don’t just solve staffing challenges; we redefine what’s possible in healthcare operations and ShiftMed is leading the way with health systems moving beyond tired models. #capitalintolabor #systemsofrecord #healthworkforce #aiworkforce #healthsystems #hospitals #cno #hospitalceo #healthcareinnovation

11

2 Comments

One more intresting presentation of Paul Tremblot de la Croix "How Generative AI Can Help Startups Succeed" at the #VDS2024. And my note on how AI can drive success: 💬 speakers emphasized that data is the foundation. Generative AI applications are only as good as the data they're built on. The ability to combine real-time data with generative capabilities is a game-changer for startups. 🕵‍♂️ shared the importance of customization. Don’t adopt generative AI just because it's trendy. Tailor it to your business needs, ensuring it’s the right tool for your use case. 👮‍♀‍ discussed data privacy and security concerns, emphasizing GDPR and other regulations. Startups must handle customer data carefully and integrate strong security protocols when deploying generative AI tools. Key points: 1. Generative AI + RAG = Personalization. By using AI with RAG, startups can retrieve relevant real-time information or past interactions, ensuring personalization not just based on predictions but also on actual preferences, history or external knowledge sources. 2. Customization & Use Case Fit. It's crucial to pick the most efficient AI model, considering cost, latency and accuracy. Generative AI isn't one-size-fits-all! 3. From MVP to Production. Building an AI-powered product is just the first step. Measuring, tracking and fine-tuning AI in production is essential for maintaining customer trust👌 I appreciate your meaningful insights shared about scaling AI strategically. Also on keep security tight and focus on solving real customer problems and think about there needs 😇 #AI #Startups #VDS2024 #DataPrivacy #Cybersecurity

4

We're trying a new thing: Company spotlights to help people better understand what various health IT vendors actually do 🔦 We’ll be cutting through the marketing speak to give short, to-the-point descriptions of a company’s product and/or service + some context on how they see their differentiators. Tag any companies in the comments you'd like to see us cover (and follow Elion if you want to stay in the loop!)

35

13 Comments

Wondering about #PlatformEngineering hidden traps? I share some thoughts on the matter with health tech magazine. To me this reculumination of central SOA team with a twist. Your customer are internal developers and business users. DM if you would like to discuss more.

13

New study suggests that gpt-o1 achieves "superhuman performance" on clinical benchmarks that include differential diagnosis, diagnostic reasoning, and crafting care management plans: * On Management Decisions, o1 outperformed both GPT-4 and clinicians by ~40% in generating accurate and actionable management plans. * On differential diagnosis, o1 included the correct diagnosis in 78.3% of cases, and its first suggestion was correct in 52% of cases. * On 70 diagnostic cases, o1 produced a response with an exact or very close correct response in 88.6% of cases. This was done with relatively simple zero-shot prompts. During the early 20th century, x-ray machines gradually went from "technical novelty" to "it's unethical to diagnose without them" for a range of medical conditions. It would be no surprise if the industry went through a similar transition to require use of medical generative AI over the next 2-3 decades. https://lnkd.in/gd3RhFbr #ai #generativeai #healthcare #healthcareai #healthai #datascience

44

1 Comment

Unlike direct computer control, this feature is actually available in the Claude web app.

4

Across the healthcare industry, thousands of staff hours are spent handling documents and manually entering data. But no longer. In our latest blog for #AWS, we explain how Acentra Health used AWS Cloud technology to help Medicaid agencies cut document processing times by more than 50% and lower costs by 40%. This allows staff to focus time on their more important mission: helping citizens. #Content #writing #AWS #IDP #AI https://lnkd.in/eAvuvAzV

1

🤔 A Fascinating Question: How could LLMs transform medical coding? Here's a data-driven hypothesis worth exploring... The Challenge: Medical coders today spend hours translating clinical notes into ICD-10 codes, juggling multiple reference guides and databases. It's like trying to translate a book while simultaneously checking three different dictionaries. Here's what an LLM-powered solution could look like: Think of it as a smart highlighter that could: - Extract key medical terminology from clinical notes - Map common diagnoses to preliminary codes - Create documentation checklists Based on current LLM capabilities and publicly available healthcare data, we could potentially see: - 25-30% faster processing for routine cases - Improved documentation completeness - Reduced lookup time for common codes The Technical Framework: - Fine-tuned LLM on medical terminology - Integration with existing systems - Human oversight for complex cases Why This Matters: For a hospital processing 200 records daily, even a 20% efficiency improvement means 40 more patients getting faster care. It's not about replacing human expertise - it's about augmenting it. 💡 Key Insight: The real value isn't in automation, but in reducing cognitive load. Like having a knowledgeable assistant who handles the initial research while experts focus on critical decisions. Question for healthcare professionals: Which parts of the coding workflow would benefit most from this kind of AI assistance? #HealthTech #AI #Healthcare #Innovation --- What specific challenges would you want this system to address first? Share below 👇

4

5 Comments

🎬 Matt Damon and 🏈 Colin Kaepernick have invested in it, and so has 💰Andreessen Horowitz... Function Health's executive team has "engineered a first-of-its-kind approach that focuses on prevention and empowerment"—very much in line with what we aimed to achieve with Carebook when we started it over six years ago. Could they be the ones to fulfill their lead investors' prophecy (link at the bottom) and Dr. Eric Topol, MD's premonitions in his books "The Patient Will See You Now" and "Deep Medicine"? They have just secured a $50 million grant. Let's hope this is not another Theranos and that we are truly witnessing the rise of the next generation of direct-to-consumer health companies. These companies will be key players in the "transition from reactive to proactive" health care that the system—and we—desperately need. What do you think? Links: 📝 Investment round press release: https://lnkd.in/eUG87qkf 🔗 Andreessen Horowitz's prophecy: https://lnkd.in/eZATCf4f

4

➰ APIContext's latest insights on cloud-based API performance have been featured on Nordic APIs! In the 2024 report, we delve into crucial aspects affecting API efficiency: 🔐 Authentication is key. Ensuring robust authentication mechanisms in APIs is crucial, yet offloading this function can introduce complex challenges. 📖 DNS configurations matter. How you handle DNS settings can significantly impact API performance, particularly when tied to authentication processes. 🌐 The role of third parties. Critical third-party services play a significant role in 🔗 API transactions, underscoring the need for careful integration and monitoring. ⛅ Not all clouds are created equal. Differences in cloud services can drastically affect API performance, an essential consideration as APIs account for over 80% of all internet traffic. #APIs #CloudComputing #TechInsights #APIContext

15

1 Comment

I’ve spoken to 50+ CMIOs/CIOs over the past year. Here are 5 thoughts about AI Scribes I'm hearing that many people are NOT talking about👇 1️⃣ Distribution matters more than Product. Most are just buying Nuance because it’s easier to buy. It’s a known entity. It’s an add-on to the Microsoft contract which means less internal hoops. Key question for who wins long-term: will the startups get distribution faster than the incumbent gets innovation? Will the EHRs eventually just release their own native AI scribes? (my belief is yes… which Oracle/Cerner has already done). 2️⃣ There is a clear “phenotype” for the clinicians who actually use AI Scribes: it’s clinicians who don’t care to have control over how their notes look and sound. But for those who really care about controlling the output? Those folks will keep writing their own notes. 3️⃣ The impact of AI scribes on medical education and trainees is very unclear. Some believe trainees should get exposure to AI scribes early on. Others worry that trainees who don’t learn to write notes are going to miss a critical skill that helps clinicians think. And what happens if there’s a cybersecurity incident and the EHR goes down? Will future clinicians not know how to document manually? 4️⃣ ROI is still hard to truly measure. Few see any improvement in “productivity” (i.e. more patients seen). The real value is in improving clinician well-being. However it’s hard to prove AI scribes are lowering staff churn since that is a lagging indicator many steps away and affected by many other factors. 5️⃣ The per clinician pricing model used by most vendors breaks down at scale. If I have clinicians who would only use the AI scribe for 30% of their patient encounters, am I going to buy a license for them? Probably not. What other less talked about learnings or insights are you hearing behind the scenes?

316

119 Comments

We used #AWS Bedrock, Lambda, S3, AWS DynamoDB, and SQS to speed up clinical referral processing by 80%, achieving 95% accuracy. Over 100 million referrals are requested annually, yet nearly half are incomplete. Consequence—incomplete transfers of patient records, test results, and other critical information between primary care providers and specialists, resulting in delayed patient care! To address this issue, we introduced Addy Healthcare, an automated solution designed to streamline the referral process, improve efficiency, and patient care. Learn more on AWS Marketplace: https://lnkd.in/dBXAG2sj #AWSPartner #AWSBedrock #Lambda #AWSDynamoDB #SQS #AWSReinvent

16

As the CEO of an AI-driven health-tech company, Labops, I’m constantly evaluating how technology can create the most impact. While Large Language Models (LLMs) are making headlines, I believe Small to Medium-sized Language Models (SMLs) deserve more attention—especially for businesses operating in niche domains like healthcare. Why do I say this? 1️⃣ Tailored Solutions: In healthcare, where precision and specificity are critical, SMLs (40–50B parameters) are more adaptable to domain-specific needs. 2️⃣ Cost-Effectiveness: For startups, running and fine-tuning a SML is more economical than relying on large, resource-intensive LLMs. 3️⃣ Ownership & Flexibility: Open-source SMLs allow businesses to build and deploy models on their terms, giving greater control over data and performance. At Labops, where we’re building solutions to transform diagnostic services, the ability to leverage AI that aligns with our unique requirements is crucial. SMLs strike the right balance between scalability and practicality, making them a natural fit for startups like ours. LLMs have their place, but they’re not always the answer for every use case—especially in sectors where focus and efficiency matter more than sheer size. If you’re in healthcare or another specialized industry, I encourage you to explore the potential of SMLs. They might just be the competitive edge you’re looking for. What’s your perspective on SML vs. LLM? Let’s discuss! 💬 #AI #HealthTech #LLM #SML #Startups #Innovation #Labops

5

1 Comment

I am torn by this as well. When I see a new platform shift, I see the opportunity for a shift that benefits the end user. The ultimate end user in healthcare is the patient. In the current model, the structure is to chase revenue by driving it up or maximizing profit. The AI platform shift, targeting low-hanging fruit like administrative efficiencies, follows this path. It's reminiscent of the famous quote: "We wanted flying cars, but we got 140 characters." The same thing is happening all over again, without truly challenging the status quo. Maybe AI can help people get less sick, prevent disease, and navigate spending less money in the first place. But in the current model, it's difficult to get penetration. Ultimately, everyone needs short-term metrics and traction to raise another round of funds. I am not criticizing those who play this game. I respect them for playing it well. However, they shouldn't position themselves as lowering costs for patients by decreasing administrative costs. The trickle-down effect simply doesn’t work.

4

Want to see a simple (non-production) example of how to add in your own information to a LLM in a notebook environment? (I've been late to the game in LLMs). In the below notebook created in preparation for an upcoming lecture, you can see how you can use one of the open models (Mistral-7B) deployed on hugging face and then insert your own custom/hyper-specific data to it (demo purposes only). https://lnkd.in/gevWpB2M First we ask 'does hants williams work at stony brook university?' And it answers that it is unsure. What we do next to help the answer, is get my URL on my faculty website, and add it using a web based loader with the help of langchain to a local db (vector database chroma). Now when we ask the same quesiton, we get: "Yes, Hants Williams is a faculty member at Stony Brook University, specifically in the School of Health Professions and the Applied Health Informatics program." If you want to play around with this you can, clone/copy the notebook then just update your prompts and the URL you want inside of WebBaseLoader(X) #LLM #test #python #googlecolab

13

John Snow Labs #Healthcare #NLP & #LLM 5.3 is now out! This release completely overhauls entity resolution - mapping medical entities in text to standard medical ontologies, in context - greatly improving accuracy. Full release notes: https://lnkd.in/gsiGF26i Install software: https://lnkd.in/gsTqkWKT #ai #generativeai #healthcareai #healthai #datascience #ontologies #medicalcoding

39

1 Comment

“Over one million new articles are added to PubMed annually—about two per minute. No physician can keep up.” This highlights a challenge that PCPs face daily, managing countless chronic and acute conditions while adapting to ever-evolving clinical guidelines. We can’t expect our clinicians to act like machines. At RhythmX AI (https://rhythmx.ai), we address this by revolutionizing primary care with our generative AI-native precision care platform, developed collaboratively by our physicians, specialists, and AI teams. Our platform integrates advanced AI and longitudinal data to deliver patient-specific, evidence-based recommendations directly within clinical workflows. These hyper-personalized insights help physicians make informed decisions at the point of care, bridging the gap between research and practice. Trust is at our core. Our physicians rigorously validate every recommendation to ensure clinical-grade reliability, empowering clinicians to confidently rely on the insights we provide. #PioneerPrimaryCare #Hyper-PersonalizedCare #PCPBurnOut #HealthAI #ResponsibleAI

32

𝐓𝐡𝐞 𝐍𝐞𝐱𝐭 𝐁𝐢𝐥𝐥𝐢𝐨𝐧-𝐃𝐨𝐥𝐥𝐚𝐫 𝐀𝐈 𝐂𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐖𝐢𝐥𝐥 𝐁𝐞 𝐢𝐧 𝐀𝐩𝐩𝐬 🚀 Like cloud and mobile revolutions gave rise to giants like Meta and Uber, AI's next $1B+ companies will emerge at the application layer. With foundational players like OpenAI and AWS stabilising, the focus shifts to apps with advanced agentic capabilities—reasoning, problem-solving, and delivering outcomes. 💡 What industries stand to benefit most from agentic AI apps? 💡 Are we ready for the cognitive leap in applications? 💡 What role will trust and ethics play in their adoption? #AI #Innovation #AgenticApps #FutureOfTech

9

Wave 1 Observability companies like Datadog & Splunk created a haystack problem. So what happens when the haystack grows so big that it becomes so difficult to search it? The challenge shifted from data collection to efficient storage, retrieval, and analysis. The second wave of observability solutions like Cribl emerged as a direct response to twin challenges that threatened to overwhelm enterprises: exponentially growing data complexity and skyrocketing costs. Wave 2 found success (and are still finding it) because they addressed the traps Wave 1 created. They provided ways to manage the flood of data, create cohesive views across disparate systems, and ask more complex questions about system behavior. They helped organizations cope with the complexity and cost introduced by first-wave solutions and cloud-native architectures. Furthermore, Wave 2 companies all largely fail to address the most important challenge, which is the labor challenge. Threading through both these waves is a critical shortage of SREs; All of this tooling has created significant engineering overhead to manage tools and control costs and increased cognitive load on senior engineers. These highly skilled professionals, commanding salaries well into six figures, spend up to 30% of their time merely triaging alerts. More alarmingly, despite investments in tools and talent, the MTTR for critical incidents still averages 4-5 hours. This human bottleneck represents an enormous hidden cost and efficiency drain. The haystack is still growing faster than our ability to find the needle, setting the stage for the next wave of observability innovation. And here are 50 companies working to solve that! https://lnkd.in/gmrYVzrY

202

15 Comments