Nursing Care Models

Anyone who leads in healthcare has watched the same pattern unfold.   A new wave of investment arrives. The funding scales around what is easiest to scale. Large cohorts. Routine pathways. Clear playbooks. The patients whose conditions don't fit that template get left behind.   Anesthesiologist Lyndsay Hoy, MD was 28 and in her first week of training when she was diagnosed with lymphangioleiomyomatosis, a rare estrogen-sensitive lung disease that almost exclusively affects women of childbearing age. Her first questions were not about prognosis. Could she get pregnant? Would the only effective medication harm a fetus? Would pregnancy accelerate her lung destruction?   There was no coordinated framework to answer her. There still isn't.   Rare diseases are not rare in aggregate. More than 1 in 10 Americans lives with one, on par with diabetes. And yet reproductive care for women whose rare diseases hinge on hormones is improvised case by case. Nobody owns coordination across pulmonology, maternal-fetal medicine, reproductive endocrinology, and genetics.   Three things have to change.   Clearly defined triggers. A new rare diagnosis in a woman of reproductive age. Pregnancy intent. Approaching IVF or perimenopause. These cannot be left to the patient to flag.   A clearly defined multidisciplinary lane. The rare disease specialist, maternal-fetal medicine, reproductive endocrinology, primary care, and genetics, connected by design, not by luck.   Shared tools. A decision guide. A common counseling language. So no woman is met with "we don't know" as the end of the conversation.   There is proof of concept. Oncofertility built real infrastructure for ER-positive breast cancer under similar uncertainty. The model exists. The question is whether leaders fund the same coordination for rare disease patients now, before the next wave of investment locks them out for another decade.   Search "The Podcast by KevinMD" wherever you listen to podcasts.   What patient population in your organization is currently being served by improvisation rather than infrastructure?   #ThePodcastbyKevinMD #HealthcareLeadership #WomensHealth #RareDisease

If we’re only training students to follow checklists and memorize procedures, we’re failing to prepare them for the actual demands of clinical care. Real-world healthcare doesn’t happen in perfect steps. It unfolds through uncertainty, judgment calls, missed cues, and split-second decisions. That kind of thinking can’t be taught through slides. It has to be lived through mistakes—early, safely, and often. We need to give learners the opportunity to struggle in simulations where lives aren't at stake. Let them mess up. Let them come into class and say, “I almost killed that patient four times.” That moment of vulnerability is gold. It tells us they’re finally moving past surface-level confidence and into real clinical thinking. It means they’re starting to ask, not just how to draw a syringe, but why they’re doing it in the first place. What symptoms led them there? Did they listen to the patient or just follow a protocol? Did they ask the right questions or ignore the clues? Here’s what today’s healthcare training must start doing: ➡︎ Create learning spaces where failure is encouraged, not punished ➡︎ Teach students to make decisions based on context, not just checklists ➡︎ Replace routine questions with scenario-based inquiry and clinical reasoning ➡︎ Guide students to explore the "why" behind every action they take ➡︎ Focus on communication and judgment, not just tools and technique Because here’s the truth: every hospital has different tools, different pumps, different setups. What doesn’t change is the clinician’s ability to think, adapt, and communicate clearly. If we want to build a healthcare workforce that performs under pressure, we have to design education that prioritizes thought over task and curiosity over compliance. That starts with allowing failure in the classroom, so students can learn how to truly care for patients in the field. VRpatients #PhysioLogicAI #nursing #nurse #simulation #VR #MR #XR #AI #Workforce #WorkforceDevelopment #WorkforceReady #AlliedHealth

This will cost us millions of lives in the next decade. How to fix it before it’s too late: Healthcare is running out of PEOPLE. The workforce crisis is here - and it’s getting worse. Every hospital, clinic, and care center is feeling the pain: • Nurses are burning out and leaving in record numbers • Doctors are stretched thin, working double shifts • Support staff can’t keep up with demand • Rural areas are losing care altogether By 2030, America will be short 300k healthcare workers and 3 million counting caregivers and home health aides. Our rapidly aging population and rates of chronic disease are pushing the system to the edge. BUT, there is a way forward. I think we need to implement 3 urgent strategies to build a sustainable healthcare workforce: 1. Invest in People: Pay more, train more, support more. Raise wages to keep talent. Fund scholarships and fast-track programs for nurses, techs, and aides. Give staff mental health support and flexible schedules to fight burnout. 2. Embrace Smart Tech: Use AI, automation, and telehealth to do more with less. AI can handle paperwork, scheduling, and even early diagnosis. Tech-enabled robotics could soon deliver meds and supplies. Telehealth lets doctors reach more patients, faster, from anywhere. 3. Redesign the Work: Build teams that work smarter, not harder.  Let nurses and doctors focus on care, not admin. Use care teams with pharmacists, social workers, and techs to share the load. Shift simple tasks to AI assistants and digital tools. Healthcare is the backbone of society... if we don’t act now, the system will break. But with bold action, we can build a future where care is always there when we need it. The time to fix the workforce is NOW - millions of lives depend on it. ❤️ What do you think is the MOST important thing we can do to fix the healthcare workforce crisis that awaits?

Some things in medicine are easy: bone sticking out of the skin? That's gonna need an x-ray, and a reduction, and antibiotics, and a cast, and an orthopedist. 𝗣𝘂𝗹𝗺𝗼𝗻𝗮𝗿𝘆 𝗲𝗺𝗯𝗼𝗹𝗶𝘀𝗺 𝗶𝘀 𝘁𝗵𝗲 𝗼𝗽𝗽𝗼𝘀𝗶𝘁𝗲. The symptoms are slippery, the stakes are high, and even once you’ve confirmed the clot, management ranges from “go home with a DOAC” to “ICU + procedure.” So it's really cool to see the new 2026 PE guidelines use recommend clinical scores so highly — pulmonary embolism might be one of their best examples. The guidelines also get the philosophy right: scores don’t replace judgment. 𝘛𝘩𝘦𝘺 𝘥𝘪𝘴𝘤𝘪𝘱𝘭𝘪𝘯𝘦 𝘪𝘵. The clinician still owns the decision—because clinicians know the stuff the score can’t: whether the patient can get the blood thinner, take it, afford it, follow up, or is quietly telling you they’re about to crump the second they walk out the door. Which is exactly the model AI needs to follow in healthcare. Help the doctor stay current, stay informed, and weigh all the important factors about the patient. The moment we treat tools — whether it's a risk score or a GenAI output — as permission slips (“the score says discharge” / “the model says no CT”), support turns into outsourcing. Just like sPESI can’t account for “can’t swallow your DOAC,” AI can’t account for that alarm bell that says: something here doesn’t fit—don’t send this person home. But it can make that human judgment more consistent, more informed, more explainable (especially to the patient!), and easier to execute as a team. Bravo Geoffrey Barnes Jay Giri Debabrata Mukherjee and EM colleague Lauren Westafer! (Couldn't find the other chairs and co-chairs on LI)

Transforming Diabetic Foot Care: Insights from the DEFINITE Care Program Diabetic foot ulcers (DFUs) are among the most debilitating complications of diabetes, significantly impacting patients’ quality of life and healthcare systems globally. Recognizing this, Singapore’s Diabetic Foot in Primary and Tertiary (DEFINITE) Care program offers a beacon of hope. A recent study published in BMJ Open Diabetes Research & Care evaluated the clinical and economic outcomes of this multidisciplinary, integrated care model. The findings are compelling: A 9% reduction in mortality and a 5% increase in amputation-free survival within 1 year. Significant reductions in hospital admissions (0.98 fewer episodes) and length of stay (5.5 fewer days). Long-term cost-effectiveness, with an incremental cost-effectiveness ratio (ICER) of USD $22,707 per quality-adjusted life year (QALY), well within Singapore's healthcare thresholds. The program's strengths lie in its integrated approach, spanning primary to tertiary care, and leveraging patient-centered digital tools. By focusing on proactive, limb-salvaging strategies, DEFINITE Care not only improves clinical outcomes but also demonstrates financial sustainability—a critical consideration for healthcare systems worldwide. This study underscores the transformative potential of multidisciplinary care models. As healthcare leaders, policymakers, and clinicians, we must explore how such integrated programs can be scaled and adapted to different healthcare settings globally. For those in public health, healthcare management, or clinical research, this is a case study worth reviewing. It reinforces the power of collaboration, innovation, and patient-centered care in tackling complex chronic conditions. Let’s continue the conversation: How can we implement similar models in our regions to improve outcomes for patients with diabetes? Share your thoughts! #HealthcareInnovation #DiabetesCare #HealthEconomics #IntegratedCare #PublicHealthLeadership

💡 Pretty ground breaking work in guiding population health care management through reinforcement learning (basically using longitudinal trajectories+ learning models) to support complex decision-making with and for patients. 🚨 Bottom line: SARSA-guided care management (CM) reduced acute care events by 12% points Sanjay Basu, MD, PhD Bhairavi Muralidharan Sadiq Y. P. JMIR Publications published on a state-action-reward-state-action (SARSA) reinforcement learning model that moves beyond traditional individual care manager experiential judgement to guide outreach of medically and socially complex patients. This tool learns from longitudinal trajectories to prevent adverse outcomes through recommender systems, provides suggestions of what interventions patients may need, and output can guide smarter judgement by CMers. Background: 💠 CM programs have notoriously been hard to evaluate reliably due to enormous variation in implemention, staffing, and training-- leaving the door open for missed identification of interventions or bias 💠 CMers are increasingly community health workers and unlicensed staff, and these programs have spread to support millions of Americans 💠 Most programs are implemented using locally created workflows based on clinical guidance and EHR documentation 💠 The more efficient CM outreach is in identifying the patients which will benefit from specific interventions, the greater the efficiency of staff deployment - less time prepping for outreach so staff can spend more time directly with patients (quality) and greater patient reach per staff (quantity). Here's how it worked, they: 💠 Evaluated 3175 Medicaid beneficiaries in CM programs across 2 states from 2023 to 2024 💠 Compared alternative approaches for recommending “next best step” interventions: the standard experience-based approach (status quo) and a state-action-reward-state-action (SARSA) reinforcement learning model 💠 The analysis of as robust and included: - Clinical impact metrics, - Counterfactual causal inference analyses to estimate reductions in acute care events - Assessed fairness across demographic subgroups - Performed qualitative chart reviews where the models differed Results: 💠 SARSA-guided CM reduced acute care events by 12% points compared to standard care management with a NNT 8.3 (95% CI 4.6-45.2) to prevent 1 acute event 💠 SARSA CM improved fairness across demographic groups, including gender (reduction 1.5%) and race and ethnicity (reduction 3.3%) 💠 Qualitatively, SARSA CM detected and recommended interventions for specific medical-social interactions (e.g. respiratory issues associated with poor housing quality, food insecurity for those with diabetes) Models like this have the potential to help care managers leap forward in becoming even more efficient in advocating for patients to receive needed services with better outcomes. #smarter #efficient #caremanagement #value #populationhealth #AI https://lnkd.in/g36B_Ztp

A few years ago, I worked with a hospital that was struggling with high turnover rates and low morale. People simply didn't feel valued or heard. Our strategy was aimed at reshaping organizational culture, and we believed the key to this transformation was leadership development. We coached leaders on conducting regular one-on-one check-ins with team members, which provide opportunities to discuss progress, address concerns, and invite feedback. We stressed the need for leaders to recognize people for their efforts and the pivotal role they play in the organization. We guided leaders on fostering psychological safety, ensuring an inclusive environment where everyone feels comfortable sharing ideas and asking questions. Over time, things started to change. People not only felt recognized, but they also began to communicate more openly, bring forward ideas, express concerns, and collaborate. Morale rose, turnover decreased, and quality improved. This transformation aligns with what neuroscience teaches us. Our brains naturally thrive in environments that foster trust, respect, and positivity. Leaders who tap into this understanding not only create better work environments but also elevate overall team performance. I encourage healthcare leaders to focus on the culture they are building. See the difference it makes in your teams and the care your patients receive. Strong teams and strong cultures lead to outstanding results, which means a healthier healthcare system for all. Have you experienced a similar transformation in your organization? What have you found effective in boosting culture? Share below! #Healthcare #Leadership #teamwork #Leadershipdevelopment

The solution to the nursing exodus isn’t resignation, it’s reinvention. Research confirms what many of us nurses know and have felt firsthand, burnout is driving thousands of nurses away from the bedside. Nearly one in five RNs plans to exit the workforce by 2027. This isn’t just a staffing issue, it’s a healthcare crisis. But instead of watching nurses walk away, we need to reimagine their roles and provide real opportunities for them to thrive. Nurses are highly skilled in patient education, chronic care management, and care coordination which are essential components of value-based care. They are qualified to manage programs like CCM, RPM, TCM, and BHI from CMS. These programs reward preventative care and proactive support, creating new pathways for nurses to apply their expertise beyond hospitals and acute care settings. Imagine nurses working as care managers and independent consultants, building businesses that prevent disease exacerbation and keep patients healthier, longer. By embracing nurse-led entrepreneurship, we allow nurses to step out of toxic environments, take control of their careers, and get paid fairly for their expertise without the middleman. Supporting nurses as entrepreneurs and innovators isn’t just the right thing to do it’s essential for the future of healthcare. When nurses thrive, patients thrive. Let’s invest in their potential, provide them with the tools to launch independent practices, get paid for their services and create an environment where nurses are empowered to lead change. It’s time to stop the burnout and start the revolution. Nurses don’t belong at the end of their rope, they belong at the forefront of healthcare innovation leading the change that is much needed in healthcare. #NurseEntrepreneurs #HealthcareInnovation #ValueBasedCare #BurnoutPrevention #CareManagement

Why hospital CEOs are failing to optimise and adapt at launch? Launching new initiatives whether it’s a digital health platform, a service line, or a patient care model should be a moment of strategic precision for hospital CEOs. Yet too often, these launches fall flat. Not because of a lack of funding. Not because of technology limitations. But because leadership is failing to adapt and optimise at the very moment it matters most. Here’s what’s going wrong: 1) The launch is treated as the Finish Line Many CEOs treat the launch as a ceremonial milestone rather than the beginning of an adaptive process. But healthcare is dynamic. What works in theory needs real time adjustment in practice. 2) Lack of frontline feedback loops The people closest to the patients—doctors, nurses and admin staff are often excluded from post-launch refinement. This leads to blind spots in execution, usability and impact. 3) Over reliance on consultants and external playbook While external expertise helps, templated solutions rarely address the complexity of real hospital ecosystems. Adaptation requires listening, iteration and context-specific leadership. 4) Slow decision making in fast moving environments Inflexible org structures and bureaucratic approval chains slow down essential course-correction. CEOs must empower cross-functional teams with decision rights to move faster. 5) Neglecting cultural readiness A successful launch is aligned with the ‘why’ behind the initiative, even the best ideas will face resistance. So what should hospital CEOs be doing instead? ✅ Build adaptive feedback systems ✅ Involve clinical and operational voices early ✅ Treat launch as the beginning of learning ✅ Act fast, adjust faster ✅ Communicate the long-term vision clearly and frequently Hospitals are evolving ecosystems. leaders who fail to optimise and adapt post launch risk wasting resources, staff morale, and most critically patient trust. The best hospital CEOs don’t just lead from the boardroom. They listen. They adapt. They evolve in real time. #Healthcare #Leadership #Hospital #Strategy

Recent advances in oncolytic virus therapy in oncology highlight a broader challenge across medicine: patient-specific biology does not conform well to population averages. In cancer, therapies succeed or fail based on tumor microenvironment, immune response, and viral behavior, factors that vary dramatically between individuals. Cardiology and cardiac surgery face an analogous problem. A digital twin is a computational, continuously updated model of an individual patient built from multimodal data; imaging, physiology, genomics, labs, and clinical history. It allows clinicians to simulate how interventions may affect that specific patient before treatment begins. In cardiac surgery, digital twins could fundamentally change decision-making:  • Simulating ventricular mechanics and hemodynamics before intervention  • Predicting response to valve repair or replacement  • Modeling outcomes of revascularization or device therapy  • Stress-testing treatment strategies before entering the operating room Just as oncolytic viruses behave differently in each tumor microenvironment, cardiac interventions behave differently in each cardiovascular system. AI allows us to model this complexity rather than average it away. These systems are not about replacing surgeons. They are about augmenting clinical judgment, reducing uncertainty, and improving patient-specific risk assessment. Oncology is already moving in this direction. Cardiac care will follow. The future of high-acuity medicine is not automation replacing clinicians instead it's clinicians empowered by patient-specific intelligence. How do you see digital twins changing procedural planning and outcomes in cardiac care? Follow for more AI + Healthcare