Beyond the Blueprint - Health Series

Beyond the Blueprint - Health Series
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Redefining Healthcare Through Design Dialogues with HIT professionals, clinical leadership, nursing, and industry innovators on integrating healthcare design into technology deployments to tackle complexity, drive innovation, improve patient care, & foment transformative change.

  1. Sep 23

    EP57 - 5G in Healthcare

    Healthcare is becoming more connected—and more mobile. But the networks supporting clinicians, medical devices, and patient data weren't built for care that moves. In this episode of Beyond the Blueprint, Gregg Malkary talks with Myron Wallace, President of ProMobix, and Dr. Allen Moore, Connected Healthcare Leader at T-Mobile, about where 5G fits into the healthcare technology stack. They dig into connectivity inside and beyond the hospital, clinical workflow, cybersecurity and resiliency, rural and critical-access infrastructure, and what health systems should consider as 5G adoption grows. Ultimately, it's a conversation about more than bandwidth: How do health systems ensure new infrastructure reduces burden on clinicians, holds up under real-world demands, and actually delivers the outcomes they're paying for? Key Takeaways Why connectivity becomes a clinical issue when clinicians, devices, and patients are in motion Where 5G can complement Wi-Fi across healthcare environments How unreliable connectivity drives clinician workarounds and undermines technology investments Why clinical workflows should shape infrastructure decisions—not the other way around How 5G can support connected care beyond the four walls of the hospital The role of connectivity in cybersecurity, resiliency, and continuity of care Why rural and critical-access health systems may have different infrastructure opportunities Building connectivity around clinical outcomes, care-team needs, and real-world performance Episode Highlights 00:00 | Telepreceptorship: Rethinking What Connected Care Can Make Possible 03:39 | When Does Connectivity Become a Clinical Risk? 08:07 | What Happens When Devices Drop and Reconnect? 10:06 | When Connectivity Fails, Clinicians Create Workarounds 12:37 | Can 5G and Wi-Fi Coexist in the Same Care Environment? 15:08 | Making Connected Care Simpler Beyond the Hospital 18:37 | Building the Infrastructure Around Clinical Workflow 20:36 | 5G, Cybersecurity, and Reducing the Attack Surface 25:18 | Can 5G Strengthen Continuity of Care? 28:52 | When Is 5G Worth Funding Ahead of Other Priorities? 31:59 | Build to the Outcome, Not the Technology 33:38 | Final Advice: Keeping Care Teams in Motion Guests: Myron Wallace, Allen Moore Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP57 - 5G in Healthcare

  2. Sep 14

    EP56 - How We Learn Is Not How We Think We Learn

    "There are a lot of folks out there who are comfortable pretending that just because someone's butt is in a chair in a lecture hall, that person must be learning." As a cognitive psychologist and Senior Vice President of Research & Analytics at Amplifire eLearning, Matthew Hays, Ph.D., has spent much of his career exploring the gap between how people think they learn and how they actually learn. In EP56 of Beyond the Blueprint, he joins Tim Jones and Kris Baird to talk about how people learn, what helps knowledge stick, and how healthcare organizations can get a better return from the billions of dollars invested in training each year. Performance during training can be misleading. Matt uses a free-throw experiment to explain why. Practicing the same shot repeatedly produces better results during practice. Varying the distance forces the learner to make new adjustments with each shot. When tested later, the varied practice produces better performance, including from distances that weren't practiced. Matt describes the difference as practicing the correction to the last mistake versus practicing the underlying skill. Similar patterns show up in learning. Spacing material over time, retrieving information from memory, and delaying feedback can strengthen retention even when the experience feels harder. What feels productive during training doesn't necessarily predict what will stick. Clinicians also arrive at training with different specialties, levels of experience, and existing knowledge. Matt describes conventional instruction built for everyone at once as "one size fits none." Some learners already know the material. Others are uncertain. Some may hold incorrect information with complete confidence. Confidence becomes another piece of the learning process. Someone who knows they are uncertain can seek more information. Someone who is confidently wrong has little reason to question what they know. Matt discusses measuring confidence alongside knowledge to uncover misconceptions that might otherwise go undetected. The conversation extends into how health systems measure the return on training. Course evaluations and post-training assessments capture what happens close to the learning experience. Matt connects the investment to what happens afterward in clinical practice. Training designed to reduce infections, falls, or other patient safety events can ultimately be evaluated against those outcomes. AI introduces another set of questions. Matt's research has explored how computers can help people learn faster, remember longer, and transfer knowledge more broadly. Generative AI can support more personalized learning, but it can also do cognitive work the learner might otherwise have to do. As Matt notes, "the way the brain learns hasn't changed in the last five years or 50 years or 500 years." For healthcare leaders evaluating their own training programs, Matt brings the discussion back to the design of the learning itself: "Anyone who's willing to believe that this sort of one size fits none training is going to make a difference for learners with a wide variety of experiences and different areas of subspecialty, even within EHR training or within their clinical practices, on some level has to know that they're doing a disservice to all of the learners in the room at the same time." Key Takeaways Why performance during training can be a poor predictor of long-term learning How retrieval practice, spacing, and delayed feedback improve retention Why "one size fits none" training struggles with experienced clinical workforces How training can identify confidently held misinformation Why learner satisfaction and immediate post-tests can give leaders the wrong signal Connecting training investment to clinical behavior, quality, safety, and patient outcomes Designing education around how clinicians actually work and make decisions Where AI can support learning without doing the cognitive work for the learner Episode Highlights 00:00 | "Learning Happens in the Brain, Not the Butt" 05:43 | How We Learn Is Not How We Think We Learn 07:20 | The Free-Throw Experiment: When Better Practice Feels Worse 09:30 | Flashcards, Delayed Feedback, and the Science of Retention 11:03 | Billions Spent on Training: Where's the ROI? 12:09 | Why "One Size Fits None" in Healthcare Training 14:49 | Connecting Clinical Training to CLABSI Reduction 19:49 | The Forgetting Curve and When to Refresh Training 22:13 | The Risk of Being Confidently Wrong 23:27 | Why Asking the Question Before Teaching the Answer Can Help 24:56 | What Should Healthcare Leaders Measure After Training? 28:41 | Designing Training Around How Clinicians Actually Work 30:52 | Final Advice: Don't Mistake Performance for Learning Guest: Matthew Hays, Ph.D. Host: Kristin Baird - Baird Group Co-host: Tim Jones Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP56 - How We Learn Is Not How We Think We Learn

  3. Aug 31

    EP55 - AMDIS Roundtable - Before the Digital Front Door: How AI Is Reshaping the Patient Journey

    "If you're a practicing clinician, you are inheriting the output from these AI tools." Dr. Minal Shah knows that patients are increasingly making healthcare decisions before they ever reach a health system's website, portal, or app. They're using ChatGPT, search, wearables, and home monitoring tools to interpret symptoms, decide where to seek care, and arrive at appointments with recommendations already in hand. For clinicians, the output of consumer AI is already becoming part of the clinical encounter. In this AMDIS Roundtable, Gregg Malkary sits down with Dr. Minal Shah, Dr. Eve Cunningham, and Dr. Deepti Pandita to explore what happens when AI impacts if and how patients reach a health system's digital front door. The results can cut both ways. Dr. Shah has seen patients arrive better informed and clearer about their priorities, allowing the visit to begin "from a point of better knowledge for both parties" and creating more room for shared decision-making. Dr. Cunningham has also seen patients convince themselves something is wrong, including one who visited three different urgent cares in 24 hours after repeatedly turning to a chatbot for guidance. That creates a practical challenge for health systems: helping patients find useful guidance while protecting against unreliable recommendations, incomplete information, delayed care, and unnecessary utilization. Dr. Pandita sees an opportunity to build AI into tools patients already encounter, including pre-visit questionnaires, where it could add "depth and perception" while making information easier for patients to provide and clinicians to interpret. Dr. Shah describes a more structured handoff from these tools that could create value for both patient and provider. From patient navigation and data governance to quality oversight, clinical workflow, and workforce capacity, these three informatics leaders examine where AI is already helping, where caution is warranted, and how health systems can create value for patients and staff as these tools become part of everyday care. All roads leading to the digital front door are becoming more complex. Dr. Pandita sees the opportunity for AI to help patients navigate them: "Every step through the healthcare journey, it becomes your sort of partner in navigating the healthcare system."   Key Takeaways How consumer AI is shaping patient decisions before they reach the health system The benefits and risks of patients arriving with AI-generated guidance Why clinical judgment and trust become even more important with AI How AI can support pre-visit preparation and care navigation The role of governance and human oversight in patient-facing AI Using AI to reduce administrative burden and preserve clinical capacity Why AI must fit the workflow to create value for patients and clinicians Episode Highlights 00:00 | When Patients Bring AI to the Exam Room 02:49 | Before the Digital Front Door: Where Patient Decisions Now Begin 04:26 | AI Governance, Guardrails, and the Human in the Loop 08:11 | Clinicians Are Inheriting the Output of Consumer AI 11:05 | When AI Helps Patients—and When It Sends Them Off Course 14:49 | Four Questions to Ask About Patient-Generated AI Advice 16:21 | Building a Better Pre-Visit Handoff with AI 19:25 | Using AI to Prepare Clinicians Before the Patient Arrives 23:10 | Should AI Agents Be Treated Like Members of the Care Team? 25:29 | AI Agents, Agenda Setting, and the "Intelligent Front Door" 27:22 | Removing Low-Value Work to Extend Clinical Capacity 29:54 | AI Adoption, Skepticism, and Change Management 33:44 | Why Clinicians Are Going Outside the Workflow to Use AI 36:01 | Ambient AI as a Recruitment and Retention Tool 39:03 | Final Advice: Start with the Workflow, Then Layer on AI Guests: Minal Shah, Eve Cunningham, Deepti Pandita Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP55 - AMDIS Roundtable - Before the Digital Front Door: How AI Is Reshaping the Patient Journey

  4. Aug 17

    EP54 - Communication Breakdown: Who Owns the Next Step?

    "Pilots lie." Chris Talbot isn't knocking proof-of-value projects. He still believes in them. But he's blunt about why they create a false sense of readiness. Pilot units represent the hospital at its best: engaged staff, freshly configured devices, vendor support, and extra IT attention. Strip that support away, put a float nurse on the floor at 3 a.m. with half the devices dead off the charging rack, and that's the real test. As Chris puts it, "What breaks first is almost never the software. It's that consistency underneath." When Gregg Malkary brought Chris Talbot (SMG3 Partners), Ron Remy (Mobile Heartbeat), and Angus Douglas (GE HealthCare) together to discuss what happens after AI identifies a problem in the hospital, the conversation quickly moved beyond the technology itself and into something harder: who owns the next step, how work gets coordinated across the enterprise, and what happens when no one acts in time. Ron Remy points out that even an urgent clinical message is still "one of a hundred signals that the recipient receives." Without context or prioritization, a stalled discharge competes for attention alongside every other notification arriving in the same inbox. More alerts and more data don't automatically produce better decisions. Sometimes they simply create more noise. Angus Douglas frames the stakes clearly. Sending a bad alert can be just as damaging as missing a good one, "because once you do that and staff stop trusting something, you really lose that adoption momentum." AI is already helping health systems stratify emergency department admissions and identify discharge opportunities, but, as Angus notes, "I don't think they're flipping a switch at all yet." Clinical judgment still matters, and trust remains difficult to earn back once it's lost. The conversation also explores the execution-layer realities that AI can't solve on its own: device fleets where 15 to 20% are dead, missing, or misconfigured on any given day; the difficulty of getting data out of enterprise clinical systems and into the workflows where action happens; and why, as Ron argues, no deployment succeeds without sustained investment in change management. This episode asks a practical question that extends well beyond AI: what does it take to move from a successful pilot to a capability an entire health system can depend on? Communication, workflow ownership, clinician trust, and organizational discipline all prove to be just as important as the intelligence of the technology itself. Key Takeaways Why pilots can create a false sense of readiness Who owns the next step after AI identifies a problem? Managing signal, noise, and clinical attention Earning clinician trust in AI-assisted workflows The execution layer behind enterprise transformation Why technology alone doesn't change organizations Building the organizational discipline to scale innovation Episode Highlights 00:00 | "Pilots Lie": Why Proof of Value Isn't Enough 02:06 | Who Owns the Next Step After AI Spots a Problem? 05:33 | "Documentation Isn't Motion" 06:20 | One of a Hundred Signals: Why Messages Still Get Missed 10:11 | Alert Fatigue, Trust, and AI Adoption 15:02 | The Handoff Problem AI Can't Solve 21:12 | Responsible AI and the Governance Challenge 27:10 | Can AI Prioritize Communication? 35:22 | What Six Months of Hospital Messages Revealed  38:52 | Lightning Round: Advice for Healthcare Leaders Guests: Angus Douglas, Ron Remy, Chris Talbot Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP54 - Communication Breakdown: Who Owns the Next Step?

  5. Aug 3

    EP53 - AHIMA's Jennifer Mueller: Trusted Health Information

    Health Information professionals rarely interact directly with patients, yet their work influences nearly every patient encounter. From clinical documentation and coding to privacy, patient identity, and information governance, they help ensure that healthcare decisions are built on information that can be trusted. As Jennifer Mueller notes, the profession has traditionally worked "in the basement"—or, as she jokes, "the garden level"—even though trusted health information has always been foundational to quality healthcare. In this episode of Beyond the Blueprint, Jennifer Mueller, Senior Vice President of Health Information, Career Advancement, and Academic Affairs at AHIMA, joins co-hosts Keith Washington and Kristin Baird for a conversation about the evolving role of Health Information professionals and the workforce behind one of healthcare's most essential functions. Jennifer discusses why healthcare leaders should think beyond the traditional view of Health Information as "the department that manages records" and recognize its growing role in information governance, data quality, interoperability, and preparing organizations for an AI-enabled future. The conversation explores how AHIMA is helping prepare the next generation of Health Information professionals for a rapidly changing healthcare landscape while supporting today's workforce through education, credentials, and practical resources. Jennifer also shares why successful AI adoption begins with understanding the problem an organization is trying to solve, establishing thoughtful governance, and ensuring the quality and integrity of the information that powers every technology. As healthcare continues to evolve, trusted health information remains the foundation of quality healthcare. Key Takeaways Why trusted health information remains the foundation of quality healthcare Looking beyond medical coding to the strategic role of Health Information Preparing healthcare organizations for AI through governance, not just technology Building workforce skills for an information environment shaped by AI Improving documentation, patient identity, and data quality before deploying AI Bringing Health Information professionals into leadership conversations earlier Why the future of AI depends on information that clinicians and patients can trust Episode Highlights 00:00 | "We've Been in the Basement... or the Garden Level" 01:23 | Introducing Jennifer Mueller and AHIMA's Mission 04:36 | Advancing Trusted Health Information 07:14 | Health Information vs. Health IT: What's the Difference? 11:22 | AI Readiness Starts with Governance 14:38 | Will AI Replace Health Information Professionals? 16:58 | Why "Soft Skills" Are Becoming Power Skills 20:10 | Patient Portals, Identity, and Trusted Records 23:50 | Ambient AI, Coding, and the Human-in-the-Loop 25:49 | The Future of the Health Information Profession 27:50 | Looking Beyond "The Department That Manages Records" 29:06 | Why the Future of AI Depends on Trusted Health Information Guest: Jennifer Mueller Host: Kristin Baird - Baird Group Co-host: Keith Washington - Companions in Courage Foundation Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP53 - AHIMA's Jennifer Mueller: Trusted Health Information

  6. Jul 20

    EP52 - Acting Before the Patient Deteriorates: Enterprise Clinical Surveillance and the Internet of Medical Things (IOMT)

    Every monitor in a hospital is good at its job. A bedside monitor understands physiologic signals. An infusion pump knows medication delivery. The EHR tracks documentation. Each system generates valuable data, but each sees only one part of the patient's story. As Matt Grubis observes, "maybe it's actually not information yet." Meanwhile, nurses are doing what they've always done: connecting those pieces in their heads while moving from room to room, responding to alarms, administering medications, and constantly deciding who needs them first. For the Chief Clinical Informatics Officer, success is measured after the alert fires. Did it reach the right clinician? Did it fit into their workflow? Was it trusted? Did someone take ownership, and did the right action follow? Those are the questions that separate technology that helps clinicians from technology that becomes one more interruption in an already demanding shift. In this episode of Beyond the Blueprint, host Gregg Malkary is joined by: Jason Atkins, Vice President & Chief Clinical Informatics Officer, Emory Healthcare Matt Grubis, Chief Engineer, Monitoring Solutions, GE HealthCare Ryan Fox, Digital & IoMT Strategy Lead, GE HealthCare Together, they explore how the Internet of Medical Things (IoMT) is helping health systems connect isolated streams of clinical data into a fuller picture of the patient. Matt illustrates the idea with a simple analogy: a thermostat that knows not just the room temperature, but also the position of the sun. The conversation covers enterprise clinical surveillance, where AI fits when it disagrees with a clinician's bedside judgment, and why "human in the loop" isn't going away anytime soon. It also explores what real vendor partnership looks like when there's actual skin in the game for clinical adoption instead of another tool handed off for the hospital to implement. Because everyone shares the same goal: helping care teams recognize what matters, coordinate their response, and act before a patient's condition deteriorates. Key Takeaways Why more patient data doesn't always create better clinical decisions Connecting isolated devices into a more complete picture of the patient Helping nurses separate meaningful signals from clinical noise Where AI fits when bedside judgment and algorithms disagree Why clinician adoption matters more than technical capability Moving from software implementation to shared clinical outcomes Designing workflows that help care teams act before patients deteriorate Episode Highlights 00:00 | Why Detection Still Doesn't Become Action 02:45 | The Nurse's Reality: Too Many Signals, Too Little Context 04:29 | Prioritizing Two Patients Who Need You at the Same Time 07:26 | "Maybe It's Actually Not Information Yet" 10:11 | Alarm Fatigue, Cognitive Burden, and the Promise of IoMT 12:00 | Why Change Management Is Harder Than Technology 14:25 | Turning Signals into a Clinical Work Queue 17:03 | The Thermostat That Knows Where the Sun Is (love this analogy) 21:00 | When AI Disagrees with the Bedside Clinician 27:15 | Why Human-in-the-Loop Isn't Going Away 33:40 | Building Trust Before Building Automation 39:10 | Clinical Adoption and Vendor Accountability 44:30 | If You Build It, Will They Come? 46:50 | Final Advice for Healthcare Leaders Guests: Jason Atkins, Ryan Fox, and Matthew Grubis Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP52 - Acting Before the Patient Deteriorates: Enterprise Clinical Surveillance and the Internet of Medical Things (IOMT)

  7. Jul 6

    EP51 - AMDIS Roundtable - The AI Decisions Missing From the Medical Record

    AI is already influencing clinical decision-making, but what happens when those decisions occur outside the systems hospitals can monitor? In this AMDIS Roundtable, Gregg Malkary is joined by Dr. John Lee, Dr. Paul Sutton, and Dr. Stephon Proctor to examine the growing use of consumer AI tools like ChatGPT, Claude, and Gemini at the point of care. As physicians increasingly turn to these tools for clinical questions, documentation, differential diagnoses, and workflow support, health systems are facing a new challenge: understanding how AI is shaping care when those interactions never appear in the medical record. The discussion explores where clinicians find value in consumer AI, the risks of shadow AI, and why governance can't focus solely on restricting access. Instead, the panel examines how health systems can build trusted, enterprise-ready AI tools that fit naturally into clinical workflows while protecting patient privacy, preserving clinical judgment, and supporting better care. Whether you're a CMIO, informatics leader, physician executive, or healthcare technology strategist, this conversation offers practical insight into one of the most important—and least visible—AI challenges facing healthcare today. Key Takeaways Why physicians are turning to consumer AI tools during clinical care The operational and governance risks of shadow AI Privacy concerns when protected health information enters public AI platforms Where enterprise AI solutions still fall short of clinician needs Balancing clinician autonomy with organizational oversight How AI may reshape documentation, decision support, and clinical workflows Why transparency—not prohibition—will be essential for responsible AI adoption Episode Highlights 00:00 | Shadow AI Is Already at the Point of Care 03:12 | How Physicians Are Using Consumer AI Behind the Scenes 07:08 | Why ChatGPT Feels Different Than Traditional Medical References 11:18 | OpenEvidence, MedPearl, and AI at the Bedside 15:46 | Real-World Uses for AI in Emergency Medicine 21:04 | Why Chart Summarization May Be AI's Biggest Win 24:07 | Inside Epic's New Agent Factory 28:42 | The Promise—and Risk—of Agentic AI 34:18 | Why Data Governance Must Come Before AI 38:11 | Ambient AI, Chart Summaries, and the Lowest-Hanging Fruit 40:58 | Hallucinations, Clinical Judgment, and the Human in the Loop 43:02 | Are We De-Skilling the Next Generation of Physicians? 45:34 | How Do We Replace Shadow AI with Trusted Enterprise Tools? 47:49 | Final Advice for Healthcare Informatics Leaders Guests: Dr. John Lee, Dr. Paul Sutton, and Dr. Stephon Proctor Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP51 - AMDIS Roundtable - The AI Decisions Missing From the Medical Record

  8. Jun 22

    EP50 - Treat the Patient, Not the Alert: AI, Alert Fatigue, and Nursing Decision Support

    AI can identify a patient at risk. But what happens when the nurse receiving the alert is already caring for another patient? As hospitals continue adopting AI-powered clinical decision support tools, nursing leaders face a practical challenge: identifying risk is only part of the equation. Acting on it requires people, workflows, communication, and clinical judgment. In this episode of Beyond the Blueprint, Gregg Malkary is joined by Daniel Gracie, Carolyn S. Harmon, and Troy Seagondollar to explore how AI-driven alerts intersect with the realities of nursing practice. The conversation examines alert fatigue, cognitive overload, accountability, workflow design, and the limits of technology when resources and staffing remain constrained. The discussion also explores why AI should support -- not replace -- clinical judgment, how poorly designed workflows can undermine even the most advanced tools, and what healthcare organizations should consider before introducing new AI-driven alerts into patient care environments. Whether you're a nursing leader, informatics professional, healthcare executive, or technology innovator, this conversation offers a grounded look at what it takes to translate AI insights into meaningful action at the bedside. Key Takeaways AI can identify risk, but it can't create capacity Clinical decision support is not clinical decision making Alert fatigue remains a significant patient safety challenge The right alert at the wrong time may not change care Experience still matters when prioritizing patient needs Technology can't fix broken workflows Escalation pathways matter as much as the alert itself Frontline nurses should help design AI-enabled workflows Better data leads to better clinical decision support AI should augment clinical judgment, not replace it Episode Highlights 00:00 | AI Alerts Are Decision Support Tools, Not Decision Makers 04:29 | When a Nurse Can't Respond to an Alert Right Away 06:09 | Why the Same Alert Means Different Things to Different Nurses 07:40 | Alert Fatigue, Cognitive Load, and Patient Safety 11:15 | What Happens When a Nurse Leaves One Patient to Respond to Another 14:12 | "Treat the Patient, Not the Alert" 16:04 | Do We Need Air Traffic Control for Clinical Alerts? 16:26 | Why AI Can't Fix Poor Workflow Design 18:25 | AI as Decision Support, Not Decision Making 20:30 | Lessons from Telemetry Monitoring and Alarm Fatigue 21:57 | Who Is Accountable When an Alert Is Missed? 23:07 | Why Technology Can't Fix Broken Processes 24:12 | The Cost of Automating Bad Workflows 28:16 | Can AI Be Personalized for Different Clinical Environments? 29:51 | Why Data Quality Determines AI Performance 30:32 | Translating AI Research into Clinical Practice 32:23 | What Nursing Leaders Should Do Before Implementing New AI Tools Guests: Daniel Gracie, Carolyn S. Harmon, Troy Seagondollar Host: Gregg Malkary - Lighthouse Healthtech Sponsored by: Simplifi Medical  Audio/Video: Tim Jones - Health Nuts Media Marketing: Josh Troop - Troop-Creative Learn More at: www.beyond-blueprint.com

    EP50 - Treat the Patient, Not the Alert: AI, Alert Fatigue, and Nursing Decision Support
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Redefining Healthcare Through Design Dialogues with HIT professionals, clinical leadership, nursing, and industry innovators on integrating healthcare design into technology deployments to tackle complexity, drive innovation, improve patient care, & foment transformative change.

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