Regulatory Coordinator

Full course · Regulatory Operations Leadership and Career Progression

Regulatory operations metrics: what to measure, what to ignore, and what actually predicts compliance performance — Regulatory Operations Leadership and Career Progression

Regulatory operations metrics: what to measure, what to ignore, and what actually predicts compliance performance

Evaluate candidate metrics against three selection criteria, design a balanced metric set with leading, lagging, and process indicators, and recognize the perverse incentives produced by poorly chosen measures.

22 min

Regulatory operations metrics: what to measure, what to ignore, and what actually predicts compliance performance

There is a sentence I repeat to nearly every regulatory operations leader I work with, and I will repeat it to you now because the rest of this lesson depends on you taking it seriously. The metric you measure is the behavior you get. Not the behavior you intended. Not the behavior the policy memo described. The behavior the metric, by its existence, rewards.

A site that measures regulatory submission volume gets more submissions, faster, sometimes at the cost of quality. A site that measures the absence of protocol deviations gets the appearance of zero deviations, sometimes by suppressing the reporting of real ones. A site that measures training hours delivered gets seat time, sometimes detached from any verified competency. None of these outcomes were anyone's stated goal. All of them are direct, predictable consequences of how the metric was framed.

This is not a problem you can solve by working harder at choosing metrics. It is a problem you solve by working slower, applying explicit selection criteria, and being honest about what each candidate measure actually rewards. That is what this lesson is for.

What you will learn3 objectives

By the end of this lesson, you will be able to:

Evaluate candidate regulatory operations metrics against three criteria — actionability, predictive value, and proportionality — applying the proportionate quality management approach from ICH E6(R3) Annex 1, Section 3.10
Design a balanced metric set for a regulatory operations function that combines leading indicators (predicting future performance), lagging indicators (measuring past performance), and process indicators (measuring operational efficiency)
Analyze the perverse incentives that poorly chosen metrics create, identifying at least three patterns where a measure that appears to capture quality actually captures something else entirely

Progress saves itself — finish reading and this lesson completes

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Regulatory operations metrics: what to measure, what to ignore, and what actually predicts compliance performance