The Sample That Nobody Wants to Include

There’s a dangerous reflex in healthcare market research: when you see data that doesn’t fit the pattern, your first instinct is often to flag it as fraud. The respondent must be a bot. The prescribing patterns must be fabricated. The outlier must be noise to eliminate.

But what if those outliers aren’t problems to solve?

Advanced machine learning algorithms analyze large volumes of healthcare data to uncover patterns and anomalies indicative of fraud, with neural networks recognizing complex, non-linear relationships and anomaly detection techniques focusing on identifying unusual patterns or outliers.

These systems are powerful. But they’re also blunt instruments when applied to healthcare market research with physician panels.

Think about what this means in practice. Your fraud detection system flags a physician respondent because:

• They completed your survey faster than your benchmark
• Their prescribing patterns are statistical outliers
• Their treatment approach doesn’t follow the expected protocol
• Their patient population differs significantly from peers

But what if they completed it faster because they’re a specialist who deals with these exact clinical decisions daily? What if their prescribing patterns are outliers because they’ve pioneered a more effective treatment approach? What if they don’t follow standard protocols because they serve a unique patient population that requires adapted care? What if their differences reflect rural versus urban practice, academic versus community settings, or specialized versus general patient populations? Consider these scenarios that frequently appear as “suspicious” outliers: The Academic Physician: A physician at a teaching hospital might have very different prescribing patterns than community practitioners because they see more complex, refractory cases. The Early Adopter: A clinician who attends multiple conferences and stays current with emerging literature might adopt new treatment approaches before they become standard. Their data looks anomalous today but might represent tomorrow’s best practice. The Specialized Population: A physician serving primarily elderly, immunocompromised, or other specialized populations will have treatment patterns that differ from those serving general populations. The variation is warranted, not fraudulent.

What is the solution here?

Data quality is a significant concern, as the effectiveness of fraud detection tools depends on the accuracy and completeness of the data being analyzed, and issues such as outdated information, data inconsistencies, and integration difficulties with existing systems can hinder successful application.

Does the outlier make sense given the physician’s profile? Is there a contextual explanation for the unusual pattern? Does the respondent demonstrate genuine engagement elsewhere? Are multiple outliers clustering around a specific characteristic? .

Additionally, based on the Insights Association Benchmarking Report, there is a fascinating paradox in healthcare market research data quality. While healthcare shows relatively lower overall fraud rates compared to B2B and B2C research, it registers notably higher rates in two specific areas: within-survey removals and post-analysis exclusions. This pattern suggests something important: healthcare panel data may look cleaner on the surface, but researchers are aggressively filtering responses during and after data collection. They’re outliers representing genuine differences in practice patterns that trigger automated quality checks.

At AOPs, we recognize that data quality decisions are not always black and white. When removals appear questionable or potentially impactful, we are committed to partnering with our clients to re-evaluate those responses. We will not charge the client for this review, even if the respondent is determined to be legitimate. And of course, we will not charge the client for recruiting the respondent if fraud is detected. Our goal is to ensure that our clients can move forward with confidence, supported by data that is both accurate and responsibly vetted.