Expert’s Opinion

Beyond compliance: Why medical device labeling should be treated as an engineering control system

Aalap Patel details how investigation science, human factors, and real-world performance data can transform labeling from a regulatory requirement into a patient-safety asset.

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By: Steve Katz

Associate Editor

Patel

By Aalap Patel

For decades, medical device labeling has largely been viewed through a compliance lens. Organizations invest significant effort ensuring labels, instructions for use (IFUs), symbols, packaging inserts, and warnings meet regulatory requirements. Reviews focus on accuracy, readability, translation, and conformity to standards, and success is often measured by whether required information is present, approved, and maintained within document control systems.

While compliance remains essential, this perspective overlooks a critical reality: labeling is one of the most widely deployed risk-control mechanisms in the medical device industry. Every day, labeling influences clinical decisions, guides procedural execution, communicates hazards, supports troubleshooting, and shapes user behavior. In many cases, it serves as the final barrier between product complexity and human error.

Yet organizations rarely evaluate labeling with the same rigor applied to design controls, process validation, reliability engineering, or risk management. As medical devices become increasingly sophisticated, the industry must begin viewing labeling not as documentation, but as an engineered control system.

The Hidden Cost of Treating Labeling as Documentation

Most quality systems evaluate labeling using a relatively simple question: Is the information correct? A more important question is often overlooked: Does the information successfully influence user behavior under real-world conditions? These are not the same thing.

A warning can be technically accurate yet operationally ineffective. Instructions can be complete yet difficult to interpret during a time-sensitive procedure. A symbol can comply with international standards while remaining poorly understood. Even critical information can be ignored if it appears at the wrong point in the user journey.

Many organizations only discover these gaps after complaints, investigations, or field observations reveal a disconnect between intended use and actual use. Labeling failures rarely present themselves as labeling failures—they surface instead as use errors, procedure deviations, training issues, product misuse, or customer complaints. The communication breakdown is often hidden behind the outcome.

What Investigations Reveal About Labeling Risk

One of the most valuable sources of insight into labeling effectiveness comes from technical investigations. When quality teams evaluate complaints, adverse events, service reports, and field observations, they frequently uncover patterns that extend beyond product performance.

Recurring findings often include overlooked instructions, misinterpreted warnings, ambiguous symbols, confusing packaging configurations, and information sequencing that does not align with real-world workflows. While these observations may appear isolated, together they often point to systemic communication risk.

The takeaway is clear: if users consistently interact with a product in unintended ways, the issue may not be user behavior alone—it may be communication design.

A Real-World Example of Hidden Communication Risk

Consider a manufacturer receiving recurring complaints related to incorrect product selection in a clinical environment. Investigations identify no product defect, no packaging damage, no manufacturing nonconformance, and no labeling inaccuracies.

At first glance, the complaints appear unrelated. However, a broader review reveals that two product variants share nearly identical packaging layouts, typography, and visual hierarchy. In time-sensitive conditions, users frequently select the wrong configuration despite technically correct information.

The issue was not a product failure—it was a communication failure. By redesigning the package layout, improving product differentiation, and restructuring information hierarchy, the organization significantly reduced selection errors without changing the product itself.

The lesson is simple: the effectiveness of communication matters just as much as its accuracy.

Packaging Is a Human Factors Interface

Packaging is often discussed in terms of sterility, protection, environmental performance, and compliance. However, it also functions as a critical human factors interface—the first point of interaction between the user and the product.

It communicates product identity, intended use, configuration, orientation, storage requirements, expiration, warnings, and procedural considerations. In high-pressure environments, users frequently make decisions based on information encountered during package handling.

This makes packaging design far more than a branding exercise. Visual hierarchy, typography, color differentiation, symbol selection, and information architecture all influence how quickly and accurately users interpret information. Poor differentiation can increase selection errors, visual clutter can obscure warnings, and poorly timed information can be ignored entirely.

Organizations routinely invest in engineering controls to reduce risk within the device itself. The same discipline should be applied to labeling and packaging systems surrounding the device.

About the author: Aalap Patel, MS, Biomedical Engineering, is an engineering and quality leader with more than 15 years of experience spanning product development, design quality, supplier quality, manufacturing quality, and post-market quality within highly regulated medical device environments. Throughout his career, he has led cross-functional investigations, risk management programs, quality transformations, and product performance initiatives involving complex Class II and Class III medical technologies. His professional interests include investigation science, human factors engineering, labeling effectiveness, reliability improvement, and systemic risk identification.

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