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Last week, we shared our model for interrogating “human error” in CAPAs. Today, we have a conversation about why, despite meeting metrics, hitting closure dates, and getting clean reports, problems addressed through CAPAs often come back.
That gap between a system that’s technically compliant and one that actually drives improvement is where many QA teams quietly lose ground. A closed CAPA that fails again for the same reason was never a successful CAPA. The numbers say you’re fine while the repeat offenders say otherwise.
Our Nick Capman recently sat down with Dan Eagles, Vice President of Quality Assurance at NeuraSignal, to talk about why CAPA systems become a check-the-box exercise and what it takes to rebuild them into something that strengthens compliance, efficiency, and team ownership.
Dan has spent over 20 years in the medical device and pharmaceutical industries. He got into medical device work before he even graduated college, held roles in manufacturing and R&D, and has spent the past 23 years in quality assurance at increasing levels of responsibility. Before NeuraSignal, he held quality leadership roles at NuVasive, Dendreon, Endologix, and Edwards Lifesciences. He’s a CAPA and NCR mentor, an auditor, and he’s listed on 21 ophthalmic device patents. At NeuraSignal, he leads the quality program for a company developing robotic transcranial Doppler ultrasound technology for stroke detection.
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Dan's key insights and practical takeaways
If you're short on time, here are the most important lessons from the discussion.
CAPA failure comes down to two things: process and stewardship. And they’re entwined. The process can be sound, but if nobody owns it well, it breaks. The stewardship can be strong, but if the process is clunky, people work around it. Dan’s framing applies beyond CAPA to the rest of the quality management system, but it shows up in CAPA more visibly than anywhere else.
The clearest warning sign of a broken-but-compliant system is repeat offenders. Your metrics can be timely. People can be hitting their marks. But if you’re seeing the same failure recur after an executed CAPA, the CAPA didn’t work, no matter what the closure data says. Metrics matter, and the one that matters most isn’t whether you closed on time. It’s whether the problem stayed solved.
The process is simple but not easy, and most of the trouble starts at the very beginning. A CAPA is straightforward: you have an issue, you write a problem statement, you investigate, you build an implementation plan, and you build a verification of effectiveness plan. The failure point is usually the problem statement. A poorly worded problem description creates ambiguity that follows you through the entire investigation. Dan’s recommendation: build training into your CAPA program specifically to teach owners how to write a good problem statement. And apply it to NCRs too, since NCRs often feed into CAPA. Train your owners to write strong problem statements and you get a lot of value for the effort.
The most common root cause mistake is back-engineering the answer you already wanted. Humans jump to conclusions. We see an obvious gap, decide that’s the cause, and then quietly reverse-engineer the root cause analysis to justify the corrective action we’d already picked. Dan illustrated this with a Six Sigma training example from his time at J&J:
A warehouse was seeing forklift damage. Three tenured drivers, two new ones.
The investigation found the new drivers hadn’t completed a particular training. Obvious, right? Train them.
They ran the effectiveness check, and the damage rate was exactly the same. In some cases it went up. The real story was hiding in the metrics: the two newer drivers were moving far more pallets per day, so their damage rate per pallet may actually have been lower.
The lesson is to tease these things out during the investigation instead of grabbing the first gap you see.
Stop defaulting to “retrain the operator.” It’s the favorite CAPA for almost any finding, and it usually isn’t the answer. Dan’s approach is to handle training inside the NCR first and track it. If the same issue keeps recurring (he gave the example of cable crimping), then you’ve built up compelling data, and you can escalate to a CAR, which serves as a precursor to a CAPA. By that point you have evidence that something deeper is going on, rather than reflexively writing “retrained the operator” and moving on.
Trade retrospective waiting for proactive validation where you can. A lot of organizations have drifted toward a “sit on it for three months and watch for recurrence” model for verifying effectiveness. That’s retrospective by design. In many cases, you could instead run a process validation that proves capability directly. Demonstrate that the operators understand the training, that they can do the task correctly, and that the process holds. When you wrap up that validation, you’ve wrapped up your VOE with objective evidence that the CAPA actually worked, rather than just waiting to see if the problem reappears.
Know the difference between a correction and a corrective action. Human nature pushes us to fix the thing in front of us, and in medical device work the instinct to protect patient safety is strong. But a correction just fixes the immediate problem, like filling in the missing fields on a record. A corrective action prevents recurrence. When you assign a corrective action, make sure it’s actually going to stop the problem from happening again. And keep risk management in mind throughout, because CAPA and risk management work hand in hand. The goal is to keep your risks as low as possible.
Good CAPA programs have leadership buy-in across every functional group. Dan reframed the leadership question around what good looks like. Strong programs have leaders who understand that CAPA is built into medical device DNA, that it isn’t just a compliance exercise but a project that drives continuous improvement. Shift the mindset from “I need to check this box for an auditor” to “I can finally fix the problems that have been aggravating us,” and CAPA becomes better operationally, better for compliance, and better for keeping manufacturing lines running.
Without that buy-in, CAPA becomes a hot potato. You get people who position themselves as CAPA experts mainly to argue that something isn’t a CAPA, though if you press them to define a CAPA, they often can’t give you a solid answer. The underlying attitude is “my department is already stretched, we don’t need the extra work.” But if you’ve ever been through a recall, you know that takes far more time and money. Dan describes CAPA as a bridge between your product before it leaves the dock and your product out in the field. Kept under your umbrella at headquarters, it’s a powerful tool for stopping discrepant product from ever reaching the market.
Rebuilding a CAPA system is a project, so treat it like one. Put a CAPA leader in charge, someone who genuinely understands CAPA, and pull in team members from every cross-functional group. Start with CAPA training: what it is, what the benefits are, what the downsides are, what it means to FDA and ISO. Look at warning letters and you’ll see the same heavy hitters every time, CAPA, training, and complaint management, so you know where regulators will focus. Then keep the process itself simple.
Standardize your forms so nobody is inventing anything on the fly. Whoever owns the CAPA should be working from the same templates. Build a problem statement template that captures who, what, why, and how. Provide a template for the five whys. Provide one for the fishbone diagram, since most people can’t recall all the categories from memory anyway (man, materials, process, environment, and the rest). If you can pull a released document from your QMS with the fishbone already laid out, you don’t have to remember it. That consistency drives quality across the whole process.
Build mentorship into every phase. Train experienced people to mentor CAPA owners at each step: reviewing the investigation, checking the implementation plan, pressure-testing the VOE plan, and tightening up anything that looks loose. Dan recalled a colleague who had a genuine knack for problem statements, whose CAPAs were brief, whose investigations weren’t long-winded, and whose implementation plans were succinct. As your CAPA owners grow, develop them into leaders, into technical experts, and into mentors who can pass that knowledge to their teams and delegate. That’s how you spread CAPA literacy across the entire company culture.
Track effectiveness, not just closure. Closure and on-time rates are useful high-level signals that tell you whether you’re resource-constrained or task-saturated. But the most important metric is whether your VOEs are actually effective. After a CAPA closes, keep tracking those specific errors to see whether they resurface, and stay objective about whether a new nonconformance is genuinely the same issue. Dan pointed to a medical device white paper involving a torque problem: the engineer assumed the fix was a torque standard on the floor and verified torque wrenches. Standards went in, wrenches were checked, parts were torqued correctly, and the problem persisted. The real answer was upstream in the manufacturing process, in finding a different way to capture the fastener rather than relying on torque alone.
Use AI as a content tool, not an oracle. Most teams are running CAPA in an electronic system, and Dan’s first piece of advice is to make that system easy to work in. If it’s clunky, change it, because that’s what your change management program is for. A clean electronic system helps the owner, helps the approver, and earns its keep at audit time, since you can print a CAPA that an auditor can follow easily. When an auditor pulls a few CAPAs and they all share the same look and feel, that signals your company takes CAPA seriously. Dan uses AI personally to help draft CAPAs and work through investigations, often feeding in his quality procedures to check what he might be missing. He reads the output and doesn’t take it as gospel. As he put it, if the output tells him to install a muffler bearing, he’s not going to abide by that. The value is in generating content quickly so he can pull the pieces he needs, since the typing itself eats up time even when you already know what you want to say. Down the line, AI could also analyze company data to assess whether a CAPA system is effective, which would add real value for continuous improvement.
Three moves to rebuild a CAPA system from scratch.
First, understand what your current system is actually doing. Gather your team and plot out the workflow the old-school way, noting where things get stuck. If your performance metrics show a pile-up in investigation or implementation, the workflow map will usually show you why.
Second, design your blue-sky system and keep it simple. There’s more material available on CAPA than ever, and FDA and ISO lay out exactly what you need, so use the regulations as your baseline and scale the system to your company, whether that’s 30 people or 3,000.
Third, put in an electronic system, sandbox it, and validate it. The activity is the same as it was on paper, so make sure the system genuinely works before you rely on it.
One thing to bring back to your team
Pull your last several closed CAPAs and ask one question: have any of these problems come back?
If they have, your closure metrics are telling you a story that isn’t true. Then look at where those CAPAs started:
Was the problem statement clear enough that someone outside the team could understand exactly what went wrong, or was it vague from the first sentence?
Did the corrective action actually prevent recurrence, or was it a correction dressed up as one, plus a “retrain the operator” line?
Did anyone back-engineer the root cause to match a fix they’d already decided on?
Did you verify effectiveness by waiting three months and hoping, or did you prove capability with a validation?
Dan Eagles is Vice President of Quality Assurance at NeuraSignal, where he leads the quality program for a company developing robotic transcranial Doppler ultrasound technology for stroke detection. He has over 20 years of experience in the medical device and pharmaceutical industries, with roles spanning manufacturing, R&D, and the past 23 years in quality assurance. Before NeuraSignal, he held quality leadership positions at NuVasive (as Site Quality Lead and Management Representative), Dendreon, Endologix (as Head of Quality Control), and Edwards Lifesciences. His expertise covers FDA Quality Management System regulations, ISO 13485, MDR implementation, quality system remediation, audit management, risk management, and CAPA/NCR processes. He holds an MBA from UC Irvine’s Paul Merage School of Business and a BS in Mechanical Engineering from Cal Poly Pomona, and is listed on 21 ophthalmic device patents.
Connect with Dan on LinkedIn here.
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