Healthcare IT projects are complex. They involve multiple stakeholders, strict regulations, and critical outcomes. When these projects fail, the consequences are severe. Wasted resources. Delayed patient care. Frustrated clinicians. I have seen it all. I know how to fix it. This post outlines effective IT project rescue steps tailored for healthcare organizations. It’s a no-nonsense guide to turning failing projects into success stories. Recognizing the Need for IT Project Rescue Steps Failing projects rarely fail overnight. They show warning signs. Ignoring these signs costs time and money. Here’s what to watch for: Missed deadlines and milestones Budget overruns without clear justification Poor communication among teams Unclear or shifting project goals Low user adoption or resistance Technical issues that stall progress When these issues pile up, it’s time to act. Early intervention saves projects. It saves organizations from costly failures. Example: A hospital’s EHR upgrade stalled after six months. The team missed deadlines and the budget doubled. Clinicians complained about usability. The project was on the brink of cancellation. Recognizing these signs early allowed the leadership to call in experts and apply rescue steps before it was too late. Key IT Project Rescue Steps for Healthcare Rescuing a healthcare IT project requires a structured approach. Here are the critical steps I follow: Assess the Situation Thoroughly Conduct a deep dive into the project status. Review documentation, budgets, timelines, and stakeholder feedback. Identify root causes of failure. Avoid assumptions. Use data. Re-establish Clear Goals and Scope Align all stakeholders on what success looks like. Define realistic, measurable objectives. Freeze scope to prevent scope creep. Rebuild the Project Team Evaluate team skills and dynamics. Replace or supplement weak links. Assign clear roles and responsibilities. Improve Communication and Governance Set up regular status meetings. Use transparent reporting tools. Empower decision-makers to act quickly. Implement Agile and Iterative Delivery Break the project into smaller, manageable phases. Deliver value incrementally. Adapt based on feedback. Focus on User Engagement and Training Involve clinicians and end-users early. Provide hands-on training. Address concerns proactively. Monitor Progress with Metrics Track key performance indicators (KPIs). Adjust plans based on real-time data. Example: In a recent project, we applied these steps to a failing telehealth platform rollout. After reassessing goals and rebuilding the team, we switched to agile sprints. User feedback shaped each iteration. The project recovered and launched successfully within six months. What does a healthcare IT project manager do? A healthcare IT project manager is the linchpin in project rescue. Their role is multifaceted and critical: Diagnose Problems: Identify technical, operational, and organizational issues. Coordinate Teams: Align IT staff, clinicians, vendors, and executives. Manage Risks: Anticipate and mitigate potential setbacks. Drive Communication: Ensure transparency and stakeholder buy-in. Enforce Discipline: Keep the project on schedule and within budget. Champion Change Management: Facilitate adoption and training. Without strong project management, even the best rescue plans fail. The project manager must be decisive, knowledgeable, and resilient. Example: A project manager stepped into a stalled clinical decision support system upgrade. They restructured the team, clarified priorities, and enforced weekly progress reviews. Their leadership turned the project around in four months. Practical Recommendations for Sustainable Project Recovery Rescue is not just about fixing immediate problems. It’s about building a foundation for long-term success. Here’s what I recommend: Document Everything: Keep detailed records of decisions, changes, and lessons learned. Engage Stakeholders Continuously: Regularly update executives, clinicians, and IT staff. Invest in Training: Equip users with skills to maximize system benefits. Prioritize Data Integrity: Ensure data quality and security at every stage. Leverage Technology Wisely: Use tools that fit your organization’s needs, not the latest trends. Plan for Scalability: Design systems that grow with your organization. These steps prevent future failures and improve patient care outcomes. Why Healthcare IT Project Rescue Matters Healthcare IT projects impact lives. Failed projects waste millions and erode trust. Successful projects improve care quality, clinician satisfaction, and operational efficiency. That’s why I emphasize healthcare it project rescue as a strategic priority. When you rescue a project, you rescue your organization’s future. You rescue patient safety. You rescue clinician well-being. You rescue your investment. Every healthcare organization deserves a trusted partner to guide them through troubled IT projects. That partner must act fast, act smart, and act decisively. Taking the First Step Toward Project Recovery If your healthcare IT project is struggling, don’t wait. Start with a clear assessment. Bring in experienced leadership. Apply proven IT project rescue steps. Focus on communication, user engagement, and agile delivery. Rescue is possible. Recovery is achievable. Success is within reach. Your next move could save millions and transform care delivery. Make it count.

I was going to pursue my CPHIMS certification this year. I changed my mind. After watching the AI narrative coming out of Healthcare Information and Management Systems Society, it stopped feeling like guidance… …and started sounding like marketing. Written By: Ross Wolfson, MHA PMP (Founder) I was going to pursue my CPHIMS certification this year, but I reconsidered after seeing the excessive, biased AI promotion. It's an embarrassment for an organization with such a strong reputation. They sound like Silicon Valley influencers, touting AI benefits without substance. And have you really asked yourself what real benefit are we talking about? Automated emails? Bots that route you to a human because the AI can't handle healthcare's unpredictability? Every patient, payer, and plan is unique. Fragmentation, unpredictability, and the human connection are vital, and data protection is paramount. HIMSS seems to have shifted from process improvement to product promotion. The "success stories" are often simulated outcomes or narrow pilots from academic centers, far from the reality of community hospitals. When AI hits an "edge case" (which is most days), it fails. The "benefit" disappears when humans must fix it. Healthcare is arguably the most chaotic industry. While an academic center with a dedicated data science team creating a predictive model for sepsis might be considered a "success story," in a 100-bed community hospital where nursing staff is already at 110% capacity, that same model is just another alert to ignore. We don't need more data generation; we need workflow integration that actually gives time back to the provider. The "success" they tout is about scaling, not solving. They scale data generation and emails, but don't solve the fact that humans remain responsible for the outcome. If I were in charge of the "governance" budget, I'd invest in solutions that truly improve patient care and data security, rather than "bots" that simply redirect to humans....rather than 'bots' that simply redirect to humans—tech that is unproven and far less successful than the hype suggests. Substance Over Scale: The Real Priorities Interoperability over Integration:  Interoperability over Integration: We don't just need systems that "talk"; we need them to speak the same language. Without strict standardization, you end up with the "Duplicate Identity" crisis—where the same provider exists under two different names in two different systems. If the data doesn't align at the source, AI won't "interpret" it; it will just scale the confusion. Data Integrity: We must clean the foundation before we automate the house. If your "advanced" directory lists a Cardiologist under Pediatrics, your AI isn't just wrong—it’s a clinical liability. You can’t build a high-tech future on a foundation of bad data. Functional User Experience: True innovation should reduce the "click-burden" and cognitive load on providers. We need workflow integration that actually gives time back to the clinician, not "bots" that add an extra layer of bureaucracy between the doctor and the patient. Two grand to get approval from these 'experts'? I’m good.

The tech world often prides itself on rapid innovation and bold solutions. Yet, when it comes to healthcare, this confidence sometimes crosses into dangerous territory. Recently, a wave of Silicon Valley "geniuses," many fresh from companies like Palantir, have been pushing a narrative that AI agents are the ultimate fix for healthcare’s complex problems. They suggest ditching existing vendors, hiring a few young AI engineers, and building everything in-house. This approach is not only naive but also risky for healthcare organizations. Healthcare is not a clean data playground. It is a fragmented, people-centered system where critical information often arrives as handwritten notes on faxed PDFs. The arrogance of assuming that AI agents alone can solve these deep-rooted issues ignores the realities on the ground. This post explores why this Silicon Valley disruption myth is harmful and what healthcare organizations should focus on instead. Why Healthcare Data Is Unlike Any Other Data Tech companies like Palantir thrive on clean, structured government data and surveillance logic. Their engineers are used to working with datasets that are well-organized and standardized. Healthcare data, by contrast, is messy and inconsistent: Patient records may be handwritten or faxed, making digitization difficult. Data is spread across multiple systems that often don’t communicate well. Privacy regulations like HIPAA strictly govern how data can be accessed and shared. Compliance with state laws such as New York State Article 28 and Joint Commission audits adds layers of complexity. This environment demands more than just technical skill. It requires deep domain knowledge and an understanding of healthcare workflows, regulations, and human factors. The False Promise of AI Agents in Healthcare The current hype suggests that AI agents can automate workflows and solve inefficiencies. But this promise overlooks critical questions: Who trains these AI systems? Engineers without healthcare experience may not understand what constitutes a HIPAA violation or how to handle sensitive patient data. Can AI fix broken workflows? If the underlying process is flawed, automation only spreads errors faster. Are these solutions sustainable? Startups often build quick prototypes to attract venture capital but lack long-term support plans. For example, a hospital that replaces its vendor with an in-house AI team might find that the AI misinterprets handwritten notes or fails to flag compliance issues. When audits occur, the organization faces penalties and operational chaos. Healthcare workstation cluttered with faxed documents and handwritten notes Healthcare data often arrives in unstructured formats like faxed documents and handwritten notes, complicating digital transformation. The Reality of Healthcare Workflows Healthcare workflows are people-centric and involve many stakeholders: doctors, nurses, administrators, patients, and regulators. These workflows are rarely linear or standardized. For instance: A nurse may need to verify patient information from multiple sources before administering medication. Billing departments must navigate complex insurance rules and coding standards. Compliance teams prepare for audits that require detailed documentation and traceability. Attempting to automate these workflows without first understanding and fixing their flaws leads to scaled inefficiency. AI agents can multiply errors, cause delays, and increase the risk of regulatory violations. What Healthcare Organizations Should Do Instead Rather than chasing shiny AI solutions, healthcare organizations need a grounded approach: Focus on execution, not hype. Develop clear strategies with practical roadmaps that address real problems. Invest in domain expertise. Hire or consult professionals who understand healthcare regulations and workflows. Improve data quality. Digitize and standardize data before applying automation. Pilot carefully. Test AI tools in controlled environments and measure their impact. Plan for compliance. Ensure all solutions meet HIPAA, state laws, and audit requirements. At Pack Leader Consulting Group, the emphasis is on delivering execution. Strategy without a roadmap is just noise. Healthcare organizations must build solutions that work in the real world, not just in theory. The Cost of Falling for the Disruption Myth When healthcare leaders buy into Silicon Valley’s oversimplified AI narrative, the consequences can be severe: Financial losses from failed projects and penalties. Operational disruptions that affect patient care. Damaged reputations due to compliance failures. Staff burnout from dealing with chaotic systems. These outcomes are avoidable with a realistic approach that respects healthcare’s unique challenges. Healthcare innovation requires humility and deep understanding. AI has potential, but it is not a magic wand. The real work lies in fixing broken workflows, improving data quality, and building solutions that comply with complex regulations. The arrogance of Silicon Valley’s disruption myth puts organizations at risk. Instead, healthcare leaders should demand execution and practical strategies that deliver lasting results. Takeaway: Don’t fall for the hype of quick AI fixes. Focus on building strong foundations and executing well-planned projects that respect healthcare’s realities.