How AI can Improve Operations in Healthcare

The AI hype vs. reality gap in healthcare - 3 practical ways we're actually using AI to improve patient care today While tech headlines promise AI doctors replacing humans, the real revolution is happening quietly behind the scenes. After implementing AI across multiple healthcare organizations, I've seen firsthand: the most powerful AI applications are the ones patients never see. 1/ Clinical documentation is being transformed ↳ Doctors spend 2 hours on documentation for every 1 hour with patients ↳ Our AI-powered ambient listening tools cut documentation time by 63% ↳ Notes are more accurate, capturing nuances human memory often misses ↳ Physicians regain 1-2 hours daily for direct patient care or personal time ↳ The impact: reduced burnout and restored physician satisfaction without changing the patient experience 2/ Risk stratification is becoming proactive ↳ Traditional risk models identify ~40% of high-risk patients ↳ Our AI systems correctly identify 78% of patients who will need acute intervention ↳ Models analyze thousands of variables across structured and unstructured data ↳ Flagging happens automatically, without requiring additional physician time ↳ The impact: earlier interventions for patients most likely to deteriorate, often before clinical symptoms are obvious 3/ Clinical workflow automation is eliminating waste ↳ Average physician receives 77 EHR notifications daily ↳ AI systems filter these to the ~20% requiring human attention ↳ Intelligent routing ensures tasks reach appropriate team members ↳ Smart scheduling optimizes patient flow based on real visit durations ↳ The impact: reduced cognitive load on providers and staff while delivering better care The most effective healthcare AI isn't replacing clinicians—it's removing the administrative burden that prevents them from practicing at the top of their license. While startups pitch expensive AI chatbots directly to patients, we're investing in AI tools that amplify human clinicians' capabilities without disrupting the therapeutic relationship. I've seen health systems chase flashy AI applications that patients can see, while ignoring the unsexy back-office applications that actually move the needle on outcomes, clinician satisfaction, and costs. The future won't be AI doctors. It will be human doctors empowered by AI systems that patients never need to see or interact with. ⁉️ What administrative tasks in healthcare do you think AI should tackle first? What work should remain firmly in human hands? ♻️ Repost to help cut through the AI hype and focus on practical applications that are working today. 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for more insights on the intersection of technology, neuroscience, and healthcare operations.

AI is transforming healthcare—but the most successful startups aren’t just building smart algorithms. They’re solving real-world problems with precision and practicality. Here are three key lessons: We can learn them from Qventus, Inc, a company revolutionizing hospital operations. Founder Mudit Garg and his team didn’t stop at predicting inefficiencies; they built AI that executes solutions—automating workflows, optimizing schedules, and ensuring critical tasks don’t fall through the cracks. Their guiding principles? 🔹 Solve High-Value Problems – Instead of chasing a grand AI platform, Qventus focuses on tangible Jobs to be Done: smoother surgery scheduling, better emergency care transitions, and real-time resource allocation. 🔹 Deep User Insight – AI only works if people use it. The team embedded themselves in hospitals, studying how nurses and doctors actually work. The result? A system that doesn’t just analyze data but seamlessly integrates into workflows. 🔹 Practical AI Over Hype – While cutting-edge models are exciting, reliability is non-negotiable in healthcare. Qventus builds strong guardrails to ensure AI outputs are trusted and actionable—because in hospitals, a 90% correct AI isn’t good enough. A similar approach helped Viz.ai disrupt stroke detection. Their machine-learning tool doesn’t just identify strokes—it alerts neurosurgeons almost instantly, integrating with existing systems to shave life-saving minutes off treatment times. Both companies prove that AI success isn’t about the flashiest model—it’s about execution, integration, and trust. For health AI entrepreneurs, the message is clear: Build solutions that work in the real world. Validate relentlessly. Win user trust. Because AI isn’t about predictions—it’s about action. See my new Forbes article, linked in the Comments section, “A Playbook for Health AI Entrepreneurs – Lessons from Two Start-Ups” #AI #Healthcare #Startups #Innovation #HealthTech #MachineLearning

Investing in healthcare innovation initiatives is essential to the future success of our industry but at what cost? We are constantly asking ourselves "what is the ROI?" especially for digital health projects with artificial intelligence. Here are several ways we, as hospital innovation executives, are seeing return on investment with AI projects: (1) Work collaboratively with a technology vendor who can serve as a partner in refining a product to meet specific goals. We did this with our operating room ambient intelligence project and we have seen a 15% increase in our OR capacity without adding new staff members. (2) Implement change management procedures alongside new technology. When we first launched our virtual nursing program, the bedside nurses were skeptical because they thought their jobs were at risk. Within 10 days, every bedside nurse was asking for a virtual nurse to assist with admissions & discharges because it reduced their time spent on documentation activities and allowed them to better personalize care for their patients. We have since improved our admissions & discharge process leading to better patient & staff satisfaction, eliminated all contract nursing positions, and have added a fresh set of eyes on the patient floors where we have seen great catches in discrepancies. (3) Use AI responsibly with a human in the loop. One of our main goals with using AI technology is to lessen the burden of data mining and documentation for our clinicians. Our predictive analytics tools work in tandem with clinical teams to highlight the most important information in the EHR, saving them from having to dig into the patient's notes and extensive medical history. We have seen that the AI tools we use are 75% more accurate at projecting a patient's discharge date and can identify the highest risk patients who make up 80% of our adverse events so that we can better align the use of our clinicians' time. 👇 Read this Becker's Healthcare article quoting multiple health system leaders across the country sharing their top ROI on AI projects. https://lnkd.in/g9PqcbSq

🚀 The real opportunity with AI in healthcare isn’t just about making broken processes slightly better; it’s about reimagining them from the ground up. 💡 The question we should be asking: If AI can be a powerful teammate and assistant, how do we redesign healthcare workflows to be AI-first? And how do we empower humans to govern, support, and streamline these AI-driven processes? Let's explore this with some key workflows: Prior Authorization, Care Management, and Disease Management. 🤖 Prior Authorization: Today, prior auth involves frustrating back-and-forth, causing delays and adding to the administrative burden. AI can do more than just automate inefficiencies: 👉 AI-first means real-time authorization decisions, pulling EHR data, verifying histories, and cross-referencing guidelines instantly. 👉 Humans oversee, manage exceptions, and ensure safety and equity. 🩺 Care Management: Current care management relies on manual tracking and calls. AI copilots can change the game: 👉 AI can identify high-risk patients in real time, generate personalized care plans, and alert care managers. 👉 Humans focus on compassionate, high-touch care—no more drowning in spreadsheets. 🦠 Disease Management: Disease management is often reactive and rigid. What if it was AI-first? 👉 AI learns continuously, adjusting care plans and flagging issues before escalation, with tailored interventions. 👉 Humans refine models and handle patient concerns beyond AI’s reach. Shifting from “How can AI improve our old processes?” to “How do we reimagine these processes with AI?” requires bold thinking. Let AI handle the complexity, so humans can focus on care, compassion, and clinical expertise. This isn’t just theory—we’re already enabling parts of this vision at Autonomize AI today with leading health plans and providers. DM me if you’d like to learn more! What do you think? Are we ready for an AI-first healthcare mindset? #AIFirst #HealthcareAI

Americans have the shortest lifespans and the highest number of preventable deaths, despite ranking #1 on healthcare spending among developed nations. A recent Commonwealth Fund study revealed why. [Mirror, Mirror 2024: A Portrait of the Failing U.S. Health System] Our system breaks down in 5 key areas. Here’s how AI can help alleviate these problems: 1- Access to Care Millions cannot access basic care. > 26 million are uninsured. > 25% of working-age adults are underinsured. > After-hours care is hard to find. AI can transform access by predicting community health needs, streamlining patient-provider matching, and creating intelligent care navigation systems. 2- Administrative Inefficiency Administrative costs are three times higher than in other nations. > Insurance systems are complex. > Paperwork takes too much time. AI can streamline the entire administrative ecosystem. From automated documentation to intelligent billing systems – freeing up resources for actual patient care. 3- Care Process Healthcare delivery is fragmented. > Providers do not communicate well. > Patients struggle to navigate care. AI can create a unified care experience by connecting disparate systems, automating follow-ups, and ensuring seamless transitions between providers. 4- Equity Healthcare is unequal. > Income affects access. > Racial and ethnic gaps are wide. > Resources are not evenly distributed. AI can analyze population health data to identify care gaps, predict community needs, and help organizations deploy resources where they'll have the greatest impact. 5- Health Outcomes Outcomes are poor. > Life expectancy is the lowest. > Preventable deaths are the highest. > Chronic disease management is weak. AI can transform reactive healthcare into proactive care by identifying at-risk populations, predicting potential health issues, and enabling early interventions. There's a lot of things AI can do. But it's not a silver bullet. It can't fix every healthcare issue. Fixing the system also means addressing policies, culture, and inequities that go far beyond technology. But progress comes when we focus on what we CAN change. By improving the systems we control, leveraging tools like AI, and staying committed to building a fairer, smarter healthcare system, we take meaningful steps forward. Better healthcare isn't about perfection. It's about progress, one step at a time. #healthcare #healthtech #technology #innovation #ai

🚀 Harnessing the Power of AI for Healthcare Automation: Opportunities for AI Agents🏥🤖 🌟 Healthcare automation is revolutionizing the medical industry by addressing critical challenges and opening new doors for innovation. From automating repetitive tasks to enhancing clinical decision-making, AI agents are transforming how healthcare is delivered, making it more efficient, accurate, and patient-centric. Here’s how AI is reshaping healthcare: ✨ 1. Transforming Administrative Tasks 📋 AI-powered agents streamline administrative processes like appointment scheduling, insurance claims, and patient referrals. By leveraging RPA and Natural Language Processing (NLP), these agents reduce manual effort, minimize errors, and speed up workflows, freeing up time for healthcare professionals to focus on patient care. 💡 2. Enhancing Clinical Efficiency 🩺 AI agents provide clinicians with powerful decision-support tools by analyzing EHRs, medical imaging, and lab results. These tools assist in making accurate diagnoses, detecting conditions earlier, and creating personalized treatment plans—revolutionizing specialties like radiology, oncology, and cardiology. ⚙️ 3. Addressing Resource Shortages ⏱️ Resource constraints are a constant challenge in healthcare. AI agents optimize workflows, predict hospital admissions, and manage staffing, ensuring better resource allocation. Chatbots and triage systems handle basic inquiries, allowing human professionals to focus on urgent and complex cases. 🔒 4. Ensuring Compliance and Data Security 🛡️ Navigating HIPAA, GDPR, and other regulations is critical. AI agents ensure compliance by monitoring data access, flagging potential violations, and maintaining secure audit trails. AI also strengthens cybersecurity by detecting and mitigating risks in real time, protecting sensitive patient data. 💰 5. Delivering Cost Savings 💵 The U.S. healthcare system could save $360 billion annually through AI-driven automation and analytics. By cutting inefficiencies and reducing errors, AI agents allow organizations to reinvest savings into patient care, research, and innovation. 👩⚕️ 6. Improving Patient Engagement and Outcomes 🤝 Virtual health assistants and predictive analytics foster better patient engagement by personalizing communication, offering reminders, and guiding care journeys. This proactive approach boosts adherence to treatment plans and improves overall outcomes. 🌐 7. Driving Innovation Through Collaboration 🤖👩🔬 AI agents are fostering a collaborative ecosystem between humans and machines. By integrating cutting-edge technologies like blockchain and decentralized systems, healthcare organizations are improving transparency and scalability. #Healthcare #Automation #AIAgents

🌟 Unlocking Cost-Effective AI for Healthcare 🌟 Exciting developments in leveraging LLMs at health system scale! A recent study published in npj Digital Medicine explores how LLMs like GPT-4-turbo-128k and Llama-3–70B can optimize clinical workflows while reducing costs. Key findings: 📋 Efficiency with Scale: By grouping multiple queries for clinical notes, costs dropped up to 17-fold while maintaining accuracy. ⚙️ Resilient Performance: High-capacity models handled up to 50 simultaneous tasks effectively, showcasing their robustness in complex medical scenarios. 💡 Clinical Potential: From generating patient summaries to improving hospital resource reports, LLMs can streamline operations across healthcare. 💰 The Challenge: High computational loads and costs are barriers, but strategies like query concatenation unlock new pathways for scalability. This research opens doors for safer, cost-efficient integration of LLMs into healthcare systems. The future of AI in medicine is not just about innovation but making it accessible and sustainable. 🔗 Read the full study for detailed insights as attached. #AIHealthcare #DigitalMedicine #LLMs #HealthTech #Innovation #GPT4 #HealthcareEfficiency #AIIntegration #MedicalAI #CostOptimization

🔍 Groundbreaking research reveals the true potential of AI in healthcare operations at scale. Mount Sinai's comprehensive study tested 10 AI models across 300,000+ experiments using EMR data to accurately process and extract information as the volume of data and number of questions increased. Findings of interest: 1. High-capacity models like GPT-4-turbo-128k and Llama-3-70B maintain 90%+ accuracy under significant workloads. 2. The game-changer: Bundling multiple clinical queries together instead of processing them individually. This approach cuts costs by 17x while maintaining reliability for up to 50 simultaneous tasks. 3. Key applications span the healthcare ecosystem: - Population-scale questions involving a wider range of individuals engaging with the data - Clinical trial matching - Resource utilization (i.e. bed availability, etc.) - More effective handoffs - Aggregation of clinical summaries across system As always, a key caveat: Model selection matters significantly. Lower-capacity models struggle with complex healthcare tasks, despite lower costs. The study's roadmap: Invest in robust models, bundle queries efficiently, and scale thoughtfully. 🔗 Read here: https://lnkd.in/gUnD5ufD #HealthcareAI #hospitals #DigitalHealth #HealthTech

AI is an efficiency driver, and healthcare conversations can benefit incredibly – here’s how: 📞 Shorter calls: At Infinitus, our AI Agent makes conversations 30% shorter than human interactions, allowing healthcare staff to focus on what truly matters – patient care. ⏰ Round-the clock availability: No more 9-5 limitations! An AI agent can be there 24/7, ensuring that essential tasks are completed and patients can get answers to important questions without delay. 📲 Real-time updates: Imagine a system that keeps patients informed about their prior authorizations in real-time – no more endless waiting or uncertainty, similar to a "healthcare tracker." AI can get us there. ✋ Reducing manual work: Automating routine tasks has drastically cut down manual work for healthcare staff, reducing burnout and boosting overall morale and productivity. 🔗 Seamless integration with EMRs: At Infinitus, our AI integrates efficiently with EMRs, streamlining data exchange and ensuring that workflows remain smooth and hassle-free.  📋 Customer-centric onboarding: Tailored onboarding processes mean that healthcare entities can quickly adapt and incorporate our AI into their existing frameworks, usually within just 45-60 days. 🧠 Smarter Communication: AI can guide efficient and informative conversations with payors and PBMs, providing accurate information and speeding up healthcare processes (like benefit verifications and even prior auth). Pioneering these advancements makes me incredibly proud of the strides we’re making at Infinitus. Here’s to a future where AI not only supports but also enhances patient experiences and transforms healthcare.

AI in healthcare offers transformative potential—better diagnoses, streamlined operations, and personalized care. But navigating this safely and ethically hinges on our most vital investment: our PEOPLE. AI is a powerful tool; human expertise must guide its application. I propose we strategically cultivate and deepen four key skills within our clinical teams to ensure they can partner effectively with AI: 1️⃣ The 𝗖𝗿𝗶𝘁𝗶𝗰𝗮𝗹 𝗔𝗣𝗣𝗥𝗔𝗜𝗦𝗘𝗥: Equipping staff to 𝗙𝗥𝗔𝗠𝗘 the AI output through a critical lens: 𝗙it for Purpose? (Is the tool validated for this specific scenario, patient, and context?) 𝗥eliable & Relevant Data? (Is input data accurate, complete, current, and truly representative? How might missing data skew AI outputs?) 𝗔ligned with healthcare Knowledge? (Does AI output align with your vital expertise for this patient or scenario?) 𝗠echanism Clear & Fair? (Is AI reasoning sufficiently transparent?) 𝗘ffect on Decision (Safe & Beneficial)? (What's the holistic impact, balancing potential benefits with any risks in this individual’s or condition’s specific circumstances?) 𝗦tewardship of Clinical Responsibility? (Does the clinician team maintain ultimate accountability, using AI as an assistive tool?)   2️⃣ The 𝗗𝗶𝗹𝗶𝗴𝗲𝗻𝘁 𝗘𝗗𝗜𝗧𝗢𝗥: Training teams to refine AI-generated content, ensuring it's 𝗦𝗘𝗧 for responsible clinical use: 𝗦crutinize for Accuracy: (Meticulously verify AI text for factual correctness, clinical appropriateness, and the absence of errors, omissions, or 'hallucinations'.) 𝗘valuate AI's Uncertainties: (Critically assess any AI's low confidence areas; independently verify or correct these findings before any clinical application.) 𝗧ransparency with Patients: (Ensure appropriate AI disclosure in patient communications, fostering transparency, trust as necessary.) 3️⃣ The 𝗘𝗻𝗴𝗮𝗴𝗲𝗱 𝗥𝗘𝗩𝗜𝗘𝗪𝗘𝗥: Fostering robust, workflow-integrated feedback loops for continuous AI tool improvement and usability. Encourage sharing specific, contextual insights (e.g., issues, successes, workarounds) via the designated channels. This real-world feedback is invaluable for iterative refinement and ensuring tools are genuinely supportive. 4️⃣ The 𝗖𝗼𝗻𝘁𝗶𝗻𝘂𝗼𝘂𝘀 𝗟𝗘𝗔𝗥𝗡𝗘𝗥: Committing dedicated time to ongoing education and professional development in the rapidly evolving AI landscape. This includes building AI literacy, staying current on ethical considerations, new functionalities, and evolving regulations. Pairing advanced AI with highly skilled, critical-thinking, and empowered clinicians is vital for translating AI insights into genuinely improved patient outcomes. How is your staff being upskilled to use AI tools? #HealthcareAI #AISkills #CMIO #Informatics #HealthcareLeadership #Upskilling, #EHR