Change Management In Healthcare

Organisational change is happening at a scale & pace we've rarely seen previously in the health & care sector. It is stirring up profound anxiety within teams. For leaders, understanding the powerful psychological undercurrents at play in driving group behaviour in times of change is as least as critical as managing the operational aspects of transition.  How do we do lead this change process with our teams in evidence-informed ways?  Heidi Pickett suggests following a process based on Bion’s group dynamic theory. Bion sets out 3 typical behaviours—dependency, fight-flight, & pairing – that block teams from moving forward. "Dependency" means over-reliance on leadership for answers, leaving team members passive & hesitant to act. "Fight-flight" manifests in blaming, conflict, or withdrawal from the challenge at hand. "Pairing" leads to an expectation that a “saviour” or magical solution will emerge to solve the group’s problems, neglecting participation & collaboration in the team.  Bion’s insights can help us move beyond managing tasks to working with meaning & emotion. This can significantly reduce group anxiety during organisational change. Here’s what leaders might do, based on Bion’s framework: •Don’t suppress anxiety but recognise the undercurrents of the group •Openly discuss the dynamics of the team & facilitate dialogue •Set clear goals, expectations & boundaries, reducing uncertainty fuelled anxiety •Build trust by communicating transparently •Encourage participation & ownership, encouraging people to take initiative •Engage the wider group in problem-solving & decision-making •Model emotional stability & help “hold” the team’s anxiety •Encourage group reflection & diverse perspectives & discourage “groupthink” An overview of Bion’s theory: https://lnkd.in/eiipZfxD By Psychology fanatic. Another superb graphic from Heidi Pickett.

I've watched 3 "revolutionary" healthcare technologies fail spectacularly. Each time, the technology was perfect. The implementation was disastrous. Google Health (shut down twice). Microsoft HealthVault (lasted 12 years, then folded). IBM Watson for Oncology (massively overpromised). Billions invested. Solid technology. Total failure. Not because the vision was wrong, but because healthcare adoption follows different rules than consumer tech. Here's what I learned building healthcare tech for 15 years: 1/ Healthcare moves at the speed of trust, not innovation ↳ Lives are at stake, so skepticism is protective ↳ Regulatory approval takes years usually for good reason ↳ Doctors need extensive validation before adoption ↳ Patients want proven solutions, not beta testing 2/ Integration trumps innovation every time ↳ The best tool that no one uses is worthless ↳ Workflow integration matters more than features ↳ EMR compatibility determines adoption rates ↳ Training time is always underestimated 3/ The "cool factor" doesn't predict success ↳ Flashy demos rarely translate to daily use ↳ Simple solutions often outperform complex ones ↳ User interface design beats artificial intelligence ↳ Reliability matters more than cutting-edge features 4/ Reimbursement determines everything ↳ No CPT code = no sustainable business model ↳ Insurance coverage drives provider adoption ↳ Value-based care is changing this slowly ↳ Free trials don't create lasting change 5/ Clinical champions make or break technology ↳ One enthusiastic doctor can drive adoption ↳ Early adopters must see immediate benefits ↳ Word-of-mouth beats marketing every time ↳ Resistance from key stakeholders kills innovations The pattern I've seen: companies build technology for the healthcare system they wish existed, not the one that actually exists. They optimize for TechCrunch headlines instead of clinic workflows. They design for Silicon Valley investors instead of 65-year-old physicians. A successful healthcare technology I've implemented? A simple visit summarization app that saved me time and let me focus on the patient. No fancy interface, very lightweight, integrated into my clinical workflow, effortless to use. Just solved an problem that users had. Healthcare doesn't need more revolutionary technology. It needs evolutionary technology that works within existing systems. ⁉️ What's the simplest technology that's made the biggest difference in your healthcare experience? Sometimes basic beats brilliant. ♻️ Repost if you believe implementation beats innovation in healthcare 👉 Follow me (Reza Hosseini Ghomi, MD, MSE) for realistic perspectives on healthcare technology

This paper delves into the potential of digital transformation in reshaping the delivery of efficient, high-quality, and secure #Healthcare. The authors highlight the immense promise digital transformation holds for the development and deployment of new care models. By integrating information, computing, communication, and connectivity technologies, digital transformation can revolutionize clinical care processes. The paper also emphasizes the potential disruptions traditional medicine might face with the entry of digital health care companies. However, it underscores the significant opportunities that arise from innovative partnerships between traditional and digital providers. 1️⃣ Digital transformation's role in healthcare: The paper emphasizes how digital transformation can significantly enhance organizational efficiencies. By leveraging technology, healthcare institutions can transform patient care models, emphasizing patient empowerment and active participation in their health journey. 2️⃣ Potential disruption in traditional #Medicine: With the rise of digital healthcare companies, traditional medical practices are at a crossroads. These digital entities are reshaping consumer expectations and putting pressure on conventional healthcare models to innovate. 3️⃣ Emerging technologies in digital healthcare: Companies in the digital healthcare space are harnessing the power of #ArtificialIntelligence, telemedicine, and blockchain electronic health records. These technologies streamline workflows, reduce errors, and ultimately lead to improved patient outcomes. 4️⃣ The promise of collaborative models: The paper suggests that there's immense potential in collaborative models between traditional and digital healthcare providers. These collaborations can span across various clinical care value-chain activities, offering a more holistic approach to patient care. 5️⃣ Use cases - Diabetes and IBD: The authors present diabetes and Inflammatory Bowel Disease (IBD) as practical examples to demonstrate the potential of digital-traditional collaborations. For instance, in diabetes care, digital tools can provide continuous feedback, medication tracking, and provider recommendations, while traditional practices offer diagnostics and routine screenings. The paper offers a comprehensive insight into the transformative potential of digital healthcare. It not only highlights the challenges faced by traditional medical practices but also presents actionable solutions through collaborative models. For anyone keen on understanding the future trajectory of healthcare, this paper provides a roadmap for harnessing the power of digital transformation. 🌐⇢ https://lnkd.in/epr_q3YS ✍🏻 Jon O. Ebbert, MD, Rita G. Khan, MBA, Bradley C. Leibovich, MD. Mayo Clinic Proceedings: Digital Health. Published:March 25, 2023. DOI: 10.1016/j.mcpdig.2023.02.006

Hospitals are drowning in data. But most are still thirsty for real insight. This is the new healthcare paradox. The digital age promised transformation. But too many leaders still face the same old problem: lots of numbers, not enough meaning. Here’s what’s changing — and what must change — for hospitals to finally turn data into action: 1. Clean, structured data is the backbone. ↳ You can’t build AI on a shaky foundation. ↳ Messy, unstructured data leads to bad predictions and wasted effort. ↳ Invest in data hygiene first. Every insight depends on it. 2. Governance and accountability are now strategic. ↳ Data quality is not a side project. ↳ It’s a core capability, like clinical care or finance. ↳ Build clear rules, assign owners, and use the right tools to keep data trustworthy. 3. Clinical usability is the pressure point. ↳ Doctors and nurses need answers, not more paperwork. ↳ Narrative-heavy notes and disconnected systems slow them down. ↳ Streamline documentation. Make data work for caregivers, not the other way around. 4. Security and innovation must move together. ↳ Progress can’t come at the cost of trust. ↳ Protect patient data as you build new tools. ↳ Security is not a brake — it’s the guardrail that keeps innovation on track. 5. AI-powered relief is here. ↳ Smart automation can cut admin work. ↳ Connected care pathways let caregivers spend more time with patients, not screens. ↳ This is how you give time back to the people who need it most. This is not just digital transformation. It’s a full rethink of how care is delivered, how teams work, and how patients move through the system. Connected Care is the goal — where data, people, and intelligence come together to create a more responsive, compassionate system. Leaders must drive this shift with discipline, clarity, and a relentless focus on value for patients and communities. The future of healthcare is not just more data. It’s better care, powered by data that finally works for us, which means Connected Care!

Pt safety for AI safety... A recent conversation gave me one of those "why aren't we already doing this?" moments. We're spending enormous energy figuring out how to make #AI safe in healthcare. And we should be. The risks are real and likely to get more acute over time as the usage of #AI in Healthcare grows. What I've been wondering about is why we are treating this risk as something unusual...we already have a well-established, well-tested infrastructure for managing risk to patients sitting in every health system in the country. It's our existing #patientsafety experts and systems. When an AI model or algorithm produces an erroneous clinical result, we should treat it with the same rigor and scrutiny we'd apply to any patient-facing technology or process failure. What does that mean? → Report it through your existing event reporting systems → Execute comprehensive root cause and common cause analyses → Discuss findings in M&M conferences and risk management committees → Apply the hierarchy of controls to eliminate or mitigate the risk going forward We don't need to build something new from scratch. We need to redeploy what we've already built — the structures, the processes, the culture of safety — and extend them to cover AI-related risks. The discipline of #PatientSafety has spent decades researching and deploying best practices for how to interrogate system failures without blame and facilitating the redesign of systems and processes to prevent recurrence. That's exactly the muscle we need right now. The tools are already in your organization. Let's use them. My patient safety colleagues...what am I missing? How do we need to adapt our safety infrastructure to meet the AI moment? #HealthcareAI #QualityImprovement #PatientSafety #AI

The system didn’t crash. The people did. A hospital launched a sleek new documentation system; 🔸 Training? Done 🔸 Interface? Clean 🔸 Leadership? Proud By the end of Week 1: 🔸 Nurses were scribbling on post-its 🔸 Doctors opened a shadow Google Doc 🔸 Admins retyped notes just to “make the system look used” No one raised alarms. No one filed complaints. They just.. worked around it. From the boardroom, it looked like success. On the ward, it felt like sabotage. This is not uncommon in big hospitals, small clinics, and everywhere in between. The tech might change, but the pattern doesn’t. And with AI tools, interoperability mandates, and shrinking workforces hitting all at once, getting this balance wrong costs more than ever. Because when transformation is treated as just an IT project, it wobbles. When it wobbles, staff feel it. And when staff feel it, patients feel it even more. Real transformation happens when people, systems, and technology move together. If one lags, the whole thing tilts. That alignment doesn’t happen because you held a training session. It happens because you manage the change, the beliefs, the behaviors, the trust. Change management isn’t “send an email and cross your fingers”. It’s: 🔹 Surfacing resistance before it hardens 🔹 Involving frontline voices, not just top-down decisions 🔹 Creating safety to fail, reflect, and iterate 🔹 Addressing the emotional toll of “yet another system” People don’t resist technology. They resist systems that make them feel unheard, unsupported, and replaceable. The bigger picture: 🔹 People need to believe in the change and see their role in it 🔹 Systems need to adapt so workflows make sense 🔹 Technology needs to fit into those workflows, not bulldoze them Miss one, and the transformation tips over (as I like to say: Transformation isn’t an upgrade, it’s an alignment). Let's go beyond noise💡 What’s a time you saw the tech work, but the transformation fails, and why? === 🍎 Missed my previous posts? Stay updated though my WhatsApp channel (no numbers shared) www.gobeyondnoise.com/#wa Like, repost if the content resonates with you. 🍎 This post is part of 'Rethinking Digital Health Innovation' (RDHI) #GoBeyondNoise

Digital Health Transformation? 90% is People, 10% is Tech Every hospital exec wants digital transformation. Until they realise it means changing people. Not just platforms. The reality? The software rollout isn’t the hard part. Getting a HCP to stop using their spreadsheet is. You can spend millions on the best tools. And still get crushed by resistance, silos, burnout, bureaucracy. Why? Because transformation doesn’t happen on a dashboard. In the break room. It happens on the ward. During a night shift when the system crashes. You can’t “deploy” adoption. You have to lead it. Fight for it. Sometimes, bleed for it. And that means retraining staff who don’t want to be retrained. Redesigning workflows that weren’t broken - until now. Making it harder before it gets easier. Taking the heat when clinicians revolt. Stop treating change management as the last checkbox. Make it the first investment. Bring HCP's into the design. Over communicate. Over-index on trust. Overtrain. The best HealthTech companies don’t just launch tech. They help rewire culture and implement change.

We don't have the luxury of time for experiments, bandwidth for endless pilots, or the comfort of incremental improvement anymore. The challenges we face (aging populations, workforce exodus, technological complexity, geopolitical instability, cybersecurity threats, and cost unsustainability) demand a fundamentally different approach. Yet we continue to treat innovation as a side project, pouring in pilots funded by temporary grants, championed by isolated enthusiasts, and measured by publications rather than impact. The "BOHICA" Reality on the Floor. We are bombarding our healthcare staff with an endless stream of new "solutions", often without taking the time to deeply understand how to actually offload their cognitive workload, and the assumption one could implement this 'on the side'. The result? A workforce that is numb, resistant to change, and leaving the profession in alarming numbers. When boards and governments introduce the next big fix -like pitching AI as the silver bullet, or an EMR that will help you massively- the reaction from the frontline is rarely enthusiasm. Instead, it is what Tilburg University Professor Mathieu Weggeman coined as BOHICA: "Bend Over, Here It Comes Again."

Most hospitals think length of stay is a bed problem. It isn't. It's a decision problem. Hospitals lose an estimated 0.5–1.5 bed-days per patient to preventable decision delays. Not lack of capacity. Yet most interventions add beds, push discharge, or deploy AI. The bottleneck is upstream. The system is slow to decide. Over time, working across clinical care, population health, and health economics, I have found a simple framework: See. Align. Proceed. 1. See: Diagnose the system, not the symptom LOS is rarely driven by a single delay. It is a system-level outcome: diagnostics not prioritised for discharge, decisions made late in the day, fragmented ownership, planning that starts too late. From a public health perspective, this is a coordination failure, not an isolated inefficiency. Patients are often medically ready before the system is operationally ready. 2. Align: Fix incentives before scaling solutions This is where most initiatives fail. Clinicians optimise for safety. Operations optimise for throughput. Finance tracks cost, but does not control flow. No one owns end-to-end LOS. Until alignment is addressed: discharge will be delayed, variation will persist, and AI will underperform. Technology cannot compensate for misaligned incentives. 3. Proceed: Act where impact is highest and risk is controlled Only after alignment should we intervene. Start with high-leverage changes: discharge planning at admission, morning discharge rounds, prioritising diagnostics for discharge-ready patients. Then scale structurally: standardised pathways, real-time patient flow visibility, AI to predict discharge readiness and delays. The question is not "what works." It is what scales without introducing new risk. Do not reduce LOS by pushing patients out. Reduce LOS by improving how the system makes decisions. In healthcare, we do not lack solutions. We lack clarity on systems, discipline in alignment, and rigour in execution. That is where sustainable impact lies. This is part of a series on decision problems in healthcare. Most healthcare challenges are not constrained by resources. They are constrained by how decisions are structured and executed. I will be sharing practical frameworks across healthcare systems, AI, and capital. Connect if you are working on similar problems. #HealthSystems #AIinHealthcare #PatientFlow #ClinicalLeadership #HealthEconomics

"𝘞𝘩𝘦𝘯 𝘺𝘰𝘶 𝘱𝘶𝘴𝘩, 𝘸𝘦 𝘱𝘶𝘴𝘩 𝘣𝘢𝘤𝘬 𝘩𝘢𝘳𝘥𝘦𝘳." It’s an unspoken agreement in workplaces everywhere. Are you unknowingly igniting resistance instead of sparking change? 𝗧𝗵𝗲 𝗛𝗶𝗱𝗱𝗲𝗻 𝗖𝗼𝘀𝘁 𝗼𝗳 𝗣𝘂𝘀𝗵𝗶𝗻𝗴 𝗧𝗼𝗼 𝗛𝗮𝗿𝗱
 At City Hospital (a pseudonym used to protect confidentiality), the CEO, “Juliette Garnier” (also a pseudonym), believed decisive action would save the day. Faced with a funding crisis, she enforced a 10% budget cut across departments. Her intent? Keep the hospital afloat. The result? Chaos. Her leadership team froze in silence, employees raged in the corridors, and nurses threatened a strike over unsafe working conditions. Garnier had unknowingly stepped into what I call The 𝙋𝙪𝙨𝙝 𝘽𝙖𝙘𝙠 𝙋𝙖𝙩𝙩𝙚𝙧𝙣: * 𝗘𝘅𝗲𝗰𝘂𝘁𝗶𝘃𝗲𝘀 = 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗿𝘀 * 𝗘𝗺𝗽𝗹𝗼𝘆𝗲𝗲𝘀 = 𝗥𝗲𝘀𝗶𝘀𝘁𝗼𝗿𝘀 The harder you push, the harder people push back. 𝗪𝗵𝗮𝘁 𝗦𝗼𝗺𝗲 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 𝗠𝗶𝘀𝘀 𝗔𝗯𝗼𝘂𝘁 𝗥𝗲𝘀𝗶𝘀𝘁𝗮𝗻𝗰𝗲
 Resistance isn’t about rejecting change. It’s about rejecting the way change is imposed. When people feel ignored, undervalued, or strong-armed, their silence or anger signals mistrust and resentment. The more forceful the push, the stronger the resistance grows. 𝗕𝗿𝗲𝗮𝗸𝗶𝗻𝗴 𝘁𝗵𝗲 𝗣𝗮𝘁𝘁𝗲𝗿𝗻
 Garnier recognised the pattern and shifted her approach. Instead of enforcing change, she invited her team to co-create solutions. Within weeks, the same employees who had resisted her became her strongest allies, crafting a plan that cut costs without compromising care. The strike was called off, and trust was restored. 𝗧𝗵𝗲 𝗟𝗲𝘀𝘀𝗼𝗻 𝗳𝗼𝗿 𝗟𝗲𝗮𝗱𝗲𝗿𝘀 
Leaders who force change light fires that burn bridges. Those who nudge—inviting collaboration and listening deeply—build lasting trust and sustainable results. Are you lighting fires or building bridges? Would love to hear your views: What strategies have worked for you to overcome resistance and inspire collaboration? 📚 For a systemic lens to creating lasting change, explore the ideas in my book, 𝙏𝙝𝙚 𝙃𝙞𝙫𝙚 𝙈𝙞𝙣𝙙 𝙖𝙩 𝙒𝙤𝙧𝙠.