Strategies to Improve AI Visibility in Healthcare

My AI lesson of the week: The tech isn't the hard part…it's the people! During my prior work at the Institute for Healthcare Improvement (IHI), we talked a lot about how any technology, whether a new drug or a new vaccine or a new information tool, would face challenges with how to integrate into the complex human systems that alway at play in healthcare. As I get deeper and deeper into AI, I am not surprised to see that those same challenges exist with this cadre of technology as well. It’s not the tech that limits us; the real complexity lies in driving adoption across diverse teams, workflows, and mindsets. And it’s not just implementation alone that will get to real ROI from AI—it’s the changes that will occur to our workflows that will generate the value. That’s why we are thinking differently about how to approach change management. We’re approaching the workflow integration with the same discipline and structure as any core system build. Our framework is designed to reduce friction, build momentum, and align people with outcomes from day one. Here’s the 5-point plan for how we're making that happen with health systems today: 🔹 AI Champion Program: We designate and train department-level champions who lead adoption efforts within their teams. These individuals become trusted internal experts, reducing dependency on central support and accelerating change. 🔹 An AI Academy: We produce concise, role-specific, training modules to deliver just-in-time knowledge to help all users get the most out of the gen AI tools that their systems are provisioning. 5-10 min modules ensures relevance and reduces training fatigue.  🔹 Staged Rollout: We don’t go live everywhere at once. Instead, we're beginning with an initial few locations/teams, refine based on feedback, and expand with proof points in hand. This staged approach minimizes risk and maximizes learning. 🔹 Feedback Loops: Change is not a one-way push. Host regular forums to capture insights from frontline users, close gaps, and refine processes continuously. Listening and modifying is part of the deployment strategy. 🔹 Visible Metrics: Transparent team or dept-based dashboards track progress and highlight wins. When staff can see measurable improvement—and their role in driving it—engagement improves dramatically. This isn’t workflow mapping. This is operational transformation—designed for scale, grounded in human behavior, and built to last. Technology will continue to evolve. But real leverage comes from aligning your people behind the change. We think that’s where competitive advantage is created—and sustained. #ExecutiveLeadership #ChangeManagement #DigitalTransformation #StrategyExecution #HealthTech #OperationalExcellence #ScalableChange

An Expert’s Strategic Roadmap to Unlocking AI’s Full Potential in Healthcare by Ainsley MacLean, M.D.! Artificial intelligence is transforming healthcare, enabling more accurate diagnoses, streamlined workflows, and enhanced patient care. Use cases range from breast cancer screening to diagnosis and medical transcription. But for AI to succeed in this high-stakes industry, its implementation must be strategic, ethical, and purpose-driven. Here are the key steps to strategically implement AI in healthcare: 1. Prepare Your Teams: - Gauge readiness by engaging physicians, nurses, and staff through surveys and conversations. - Educate teams on AI use cases while emphasizing it as a supportive tool, not a replacement for clinical expertise. 2. Define Clear Goals: - Identify organizational priorities—streamlining workflows, solving specific challenges, or becoming a leader in AI adoption. 3. Establish Robust Governance: - Develop accountability structures to oversee AI implementation and ensure ethical usage. 4. Choose the Right Tools: - Evaluate whether to adopt market-ready solutions or build custom tools. - Ensure AI integrates seamlessly with existing systems like EMRs, prioritizing data privacy and security. 5. Pilot and Iterate: - Start small with a technical rollout, then test with select, highly trained users. - Gather feedback and scale cautiously, refining processes along the way. 6. Measure Results Continuously: - Monitor KPIs aligned with your goals and track inputs and outputs for errors or biases. - Commit to using diverse datasets to maximize fairness and effectiveness. AI in healthcare is not a “set it and forget it” solution—it’s an ongoing journey. By strategically planning and continually refining, we can ensure AI truly enhances care delivery, empowering clinicians to focus on what matters most: the patients. Read the full Forbes expert guidance by Ainsley MacLean, M.D. from the Mid-Atlantic Permanente Medical Group | Kaiser Permanente: https://lnkd.in/eAWfA3nC What’s your perspective on AI in healthcare? Which use case excites you the most? #HealthcareInnovation #AIinHealthcare #Leadership

7 years from FDA approval to Medicare reimbursement for AI healthcare devices. Most AI startups don't survive that valley of death. I've helped healthcare organizations implement 4 successful AI technologies during my 15 years building health tech companies. The difference wasn't the technology. It was the implementation strategy. Here's what separates success from failure: 1/ Start with workflow integration, not features ↳ Map current clinical processes before adding AI ↳ Identify where technology reduces work, not creates it ↳ Design around existing EMR systems and staff habits 2/ Build reimbursement strategy early ↳ Engage payers during development, not after launch ↳ Document value-based outcomes from day one ↳ Create temporary CPT code pathways when possible 3/ Choose clinical champions strategically ↳ Find early adopters who influence their peers ↳ Measure immediate benefits they can advocate for ↳ Let success stories drive adoption organically 4/ Focus on measurable ROI ↳ Track time saved, errors reduced, outcomes improved ↳ Connect AI insights to billing optimization ↳ Demonstrate cost savings within 90 days 5/ Plan for the long game ↳ Regulatory approval is just the beginning ↳ Real success requires sustained clinical adoption ↳ Revenue depends on proving ongoing value The healthcare organizations winning with AI didn't buy the flashiest technology. They invested in thoughtful implementation that solved real problems. Technology without deployment strategy is just expensive software. ⁉️ Are you struggling to implement AI technology in your healthcare organization? ♻️ Share if you know someone struggling with implementation. 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic takes on healthcare innovation.

AI is only as smart as its data. Bad data breaks everything. Good data builds the future. AI in healthcare is not magic. It is math, logic, and trust—stacked on a backbone of clean, connected data. Here’s the truth: • AI can’t fix broken data. • Automation fails if the data is a mess. • Connected care needs a solid data foundation. Think of data as the bones of a body. If the bones are weak, nothing stands. If the bones are strong, you can build muscle, move fast, and stay healthy. To build smarter AI and real connected care, start with these pillars: 1/ Data Quality:   Garbage in, garbage out.   Every record, every field, every update must be right.   No duplicates. No missing info. No errors.   Clean data is the first rule. 2/ Interoperability:   Systems must talk to each other.   Break down silos.   Use standards like HL7, FHIR, and APIs.   If your data can’t move, your care can’t connect. 3/ Privacy and Security:   Trust is everything.   Encrypt data.   Control access.   Follow HIPAA and GDPR.   Patients own their data—protect it. 4/ Governance:   Set the rules.   Who can see what?   Who can change what?   Audit trails, clear roles, and strong policies keep data safe and useful. 5/ Infrastructure Flexibility:   Cloud, on-prem, or hybrid—pick what fits.   Scale up as you grow.   Don’t get locked in.   Your data backbone must bend, not break. 6/ Continuous Improvement:   Data is never “done.”   Check, clean, and update all the time.   Train your team.   Make data quality a habit, not a project. When you get these right, you unlock: • Smarter automation • Real-time insights • Scalable AI that learns and adapts • Seamless patient care across systems The best AI in the world can’t save bad data. But with the right data backbone, you build care that connects, scales, and lasts. Start with better data. Build the future of healthcare—one clean record at a time.

AI in Healthcare: Stop piloting, Start solving Healthcare’s AI challenge isn’t tech—it’s fragmentation. Over the last 30 days, I’ve talked to CIOs who see it clearly: AI isn’t scaling because data is trapped in siloed systems and quality of data isn't great, creating chaos, not clarity. One CIO put it perfectly: “We don’t need another AI tool. We need AI that works with what we have—unlocking data, not adding tech debt.” The reality: -Unstructured mess: Caregiver notes, voice logs, PDFs, images—locked up and disconnected in different systems. - Siloed systems: AMS, EHR, claims data don’t sync, leaving teams stuck in manual mode. - Burnout crisis: 70% caregiver turnover from admin overload and bad scheduling. - Claims pain: 20% ACA denials, 10-15% rejections eating margins. Data is in observation mode—insights in dashboards while execution stays manual. How to fix it: 1. Make data AI-ready: Turn observations, notes, scheduling, PDFs, and voice logs into structured knowledge building context. 2. Clean the mess: “John Smith, 55” shouldn’t be three people across systems. Need governance. 3. Embed AI in workflows: Match caregivers to clients smarter, using real-time data to predict flags and interventions reducing ER and re-admissions. Act, augmenting the team- don’t just flag: Auto-fix claim errors pre-submission to slash denials. Deploy AI as an execution layer: Bridge AMS, EHR, and claims—pulling, validating, acting seamlessly. Automate scheduling, claims, compliance—no more manual patches. The payoff: *20% fewer denials: AI catches claim fails early. *70% lower turnover: Smarter scheduling keeps caregivers sane. *70% faster action: Predictive analytics cuts ER visits and readmissions. One CIO saw documentation time drop from hours to minutes—giving back time to caregivers to focus on what they love- providing care. That’s the goal: AI running silently across workflows, boosting teams, driving outcomes. Better care, less burnout. Period. What’s the biggest barrier you’re seeing to making AI work in healthcare? Let’s talk. At Inferenz, we’re all in on Agentic AI to improve patient outcomes and lighten caregivers’ admin workload. Gayatri Akhani Yash Thakkar James Gardner Brendon Buthello Kishan Pujara Amisha Rodrigues Patrick Kovalik Joe Warbington Michael Johnson Chris Mate Elaine O’Neill

I would like to flag a strategic imperative for both Medical Affairs and Commercial leaders to drive innovation and market leadership. AI's full potential requires a strategic approach that addresses key challenges and capitalizes on emerging trends. Leveraging interdisciplinary collaboration and human skills to overcome barriers in AI-driven research, ultimately will enhance both scientific integrity and market competitiveness. I: A Competitive Advantage Reproducibility in AI-based research is not just a scientific imperative—it's a commercial necessity. The "black-box" nature of many AI models can lead to skepticism among HCPs and regulatory bodies, potentially impacting product adoption and approval processes. By championing transparent, reproducible AI methodologies, medical affairs leaders can: 1. Build trust with key opinion leaders and healthcare providers 2. Streamline regulatory submissions and approvals 3. Enhance the credibility of marketing claims and scientific communications Business leaders should recognize that investing in reproducible AI research can differentiate their products in a crowded market, providing a strong foundation for marketing strategies and stakeholder engagement. II: Interdisciplinary Collaboration To unlock AI's transformative potential, medical affairs and commercial teams must foster collaboration between AI specialists and domain experts. This interdisciplinary approach can: 1. Accelerate drug discovery and development processes 2. Identify novel biomarkers and therapeutic targets 3. Optimize clinical trial design and patient selection 4. Enhance real-world evidence generation and analysis By breaking down silos between departments and encouraging cross-functional projects, business leaders can create a culture of innovation that drives both scientific advancement and commercial success. III: Investing in Your Team's Skills To stay competitive in the AI-driven healthcare landscape, medical affairs and commercial leaders must prioritize skill development within their teams. Key areas of focus should include: 1. AI literacy and data science fundamentals 2. Ethical considerations in AI-driven healthcare 3. Regulatory compliance in AI-based research and applications 4. Effective communication of AI-derived insights to diverse stakeholders By investing in these skills, business leaders can ensure their teams are equipped to leverage AI technologies effectively, from early-stage research through to market access and commercial strategies. IV: Conclusion For medical affairs and commercial leaders, embracing AI-driven research is not just an option—it's a strategic imperative. By addressing reproducibility challenges, fostering interdisciplinary collaboration, investing in emerging skills, and enhancing the employee experience, leaders can position their organizations at the forefront of scientific innovation and market leadership. #CommercialExcellence #MedicalAffairs #GoToMarket #pharma

Medical AI can't earn clinicians' trust if we can't see how it works - this review shows where transparency is breaking down and how to fix it. 1️⃣ Most medical AI systems are "black boxes", trained on private datasets with little visibility into how they work or why they fail. 2️⃣ Transparency spans three stages: data (how it's collected, labeled, and shared), model (how predictions are made), and deployment (how performance is monitored). 3️⃣ Data transparency is hampered by missing demographic details, labeling inconsistencies, and lack of access - limiting reproducibility and fairness. 4️⃣ Explainable AI (XAI) tools like SHAP, LIME, and Grad-CAM can show which features models rely on, but still demand technical skill and may not match clinical reasoning. 5️⃣ Concept-based methods (like TCAV or ProtoPNet) aim to explain predictions in terms clinicians understand - e.g., redness or asymmetry in skin lesions. 6️⃣ Counterfactual tools flip model decisions to show what would need to change, revealing hidden biases like reliance on background skin texture. 7️⃣ Continuous performance monitoring post-deployment is rare but essential - only 2% of FDA-cleared tools showed evidence of it. 8️⃣ Regulatory frameworks (e.g., FDA's Total Product Lifecycle, GMLP) now demand explainability, user-centered design, and ongoing updates. 9️⃣ LLMs (like ChatGPT) add transparency challenges; techniques like retrieval-augmented generation help, but explanations may still lack faithfulness. 🔟 Integrating explainability into EHRs, minimizing cognitive load, and training clinicians on AI's limits are key to real-world adoption. ✍🏻 Chanwoo Kim, Soham U. Gadgil, Su-In Lee. Transparency of medical artificial intelligence systems. Nature Reviews Bioengineering. 2025. DOI: 10.1038/s44222-025-00363-w (behind paywall)

AI adoption in clinical workflows is HARD Despite AI’s potential, its adoption falters when workflows aren’t seamlessly aligned. Technology alone won’t transform care.  The reality of clinicians’ daily routines matters just as much. I’ve said it many times, but the adoption of AI depends as much on workflow integration as on algorithm accuracy. Healthcare organizations must prioritize AI solutions that complement, not disrupt, clinical workflows if they truly want to benefit from AI. For instance, if you talk to clinicians, they want tools that: - Assists clinicians to improve, not replace, decision-making - Integrate seamlessly with existing systems - Easy user interface for adoption and better outcomes The best AI solutions might even seem invisible to the user. So when integrating AI into clinical workflows, don’t: - Overlooking clinician input and user experience - Neglect data quality in workflow integration - Underestimate training needs for AI tools - Rush deployment without pilot testing Instead,  - Include clinical teams in your stakeholders - Ensure clean, relevant datasets - Prioritize intuitive interfaces - Provide ongoing training and support - Conduct phased, real-world pilots Integration should be a part of transformation. How is your organization making sure AI fits naturally into clinician workflows rather than adding burdens? For when you are planning AI integration, I put together a list of 10 resources for integrating AI into clinical workflows. 1) Critical activities for successful implementation and adoption of AI in healthcare: https://shorturl.at/Q9GX9 2) Exploring the complex nature of implementation of Artificial intelligence in clinical practice: https://shorturl.at/8u0qO 3) Establishing responsible use of AI guidelines: https://shorturl.at/WhFqh 4) Trust in AI–Based Clinical Decision Support Systems Among HCPs: https://lnkd.in/e96ekp_u 5) FUTURE-AI: Guideline for trustworthy and deployable AI in healthcare: https://shorturl.at/BmrUI 6) NHS England: AI and Machine Learning Guidance: https://shorturl.at/ql97a 7) Toward a responsible future: recommendations for AI-enabled clinical decision support: https://igit.me/Xx11G 8) A comprehensive overview of barriers and strategies for AI implementation in healthcare: https://igit.me/zoheC 9) Opportunities, challenges, and requirements for AI implementation in Primary Health Care: https://igit.me/mtU6g 10) AI in critical care: A roadmap to the future: https://igit.me/EwURU Which ones did I miss?

Healthcare doesn’t just need more AI. It needs systems that can think, adapt, and collaborate. As AI adoption accelerates, one truth is becoming clear: 1) Prompt engineering alone won’t get us to transformation. 2) We need agentic architectures — AI systems that don’t just automate a task, but take initiative, collaborate with other agents, reflect on their own output, and use tools to reason in real-time. 3) This excellent framework from Rakesh Gohel outlines six core design patterns that can shape the next generation of enterprise AI. Systems that think, reason, self-correct, and collaborate across workflows. And in healthcare, this shift couldn’t come soon enough. Here’s how each design pattern shared by Andreas Horn maps directly to real enterprise use cases: 1) ReAct Agent AI that recommends a care plan, evaluates new lab results, and adjusts — like a clinician in looped decision-making. → Think: chronic care optimization. 2) CodeAct Agent Executes live scripts to reformat or query data on demand. → Think: transforming raw clinical data into structured FHIR for payer processing. 3) Modern Tool Use Orchestrates across eligibility APIs, pricing engines, and clinical policy libraries. → Think: claim packet assembly in seconds — not days. 4) Self-Reflection Detects and corrects its own hallucinations or rule violations. → Think: validating a denial reason before it’s ever sent. 5) Multi-Agent Workflow Plays the roles of auditor, compliance officer, and coordinator — each with its own function. → Think: automated appeals built by a team of agents. 6) Agentic RAG Retrieves real-time payer policy and medical evidence — not static model memory. → Think: real-time guideline validation during prior auth. If we want AI that operates at the level of human teams, this is the design language we must learn. Not prompts. Patterns. 📷 Visual below: “6 Design Patterns for Agentic AI in Healthcare” 👇

The Healthcare AI Trap: Why a "Single Blade" Strategy Fails In 2026, the industry is obsessed with Foundation Models and LLMs. But if your AI strategy starts and ends with a chatbot, you aren’t building a clinical solution—you’re just buying a shiny new blade and ignoring the rest of the knife. A recent report from Chief Healthcare Executive highlights that while 2025 was the year of "LLM experimentation," 2026 is the year of "Strategic integration". Experts warn that "no-clinical-context LLMs" are already hitting a ceiling. Forward-thinking health systems are now pivoting toward multimodal systems—like the recent Stanford study where AI integrated sleep recordings (physiological data) with EHRs to predict 100+ health conditions with 80%+ accuracy. As a Chief AI Officer (CAIO), the goal isn't "How do I use an LLM?" It’s "How do I solve a high-stakes clinical problem safely and accurately?" Case Study: The Hybrid Approach to Pressure Injuries. Consider the challenge of predicting and managing hospital-acquired pressure injuries. A generic LLM can summarize a nursing note, but it cannot "see" the risk or the wound. A truly effective solution requires a "Hybrid AI Strategy": Computer Vision: To analyze skin integrity and wound progression directly from clinical images. Structured EHR Data: To cross-reference lab values, mobility scores, and comorbidities for real-time risk stratification. Synthetic Data: To bolster training sets where rare clinical presentations are scarce, ensuring the model performs across diverse patient populations without compromising privacy. The "Swiss Army Knife" Advantage: By combining these "blades"—Images + EHR + Synthetic Data—we move from a text-based curiosity to a life-saving tool. Often, a focused, "simple" ML approach tailored to a specific dataset outperforms a massive, generic model while being significantly more cost-effective and explainable. The CAIO Mindset: Don't let the tool define the problem. Start with the clinical challenge, then open the right combination of blades. One tool is a toy; a hybrid kit is a transformation.