INTELLIGENCE IN ACTION

The Art of a Data Detective

Claire Leduc is a data detective. “It’s like solving a mystery,” she told me. “Why does this data look the way it does?” As Senior Network Access Analyst at Surescripts, Leduc builds processes that strengthen network integrity—and these will only get smarter and more efficient over time.

Born and raised in Arlington, Virginia, just outside Washington, D.C., Leduc recalls an early fondness for math. “As a kid,” Leduc says, “you’re taught that one plus one equals two.” There’s an answer to a math problem, in other words, and Leduc’s quantitative bent has served her well in her role as data detective, where she found math to be more complicated—and interesting—in the real world than it was at school.

If “data detective” sounds cheesy or overblown, consider that Leduc’s role is to help triage anomalies on the Surescripts network, which carried 20.4 billion transactions in 2021 alone. Anomalies are outliers that can be found in just about any large data set, but given the sensitive nature of health information, they must be taken seriously in order to continuously protect and improve the access, security and performance of the Surescripts network and the data it carries.

To do this, Leduc applies computer logic, machine learning and good, old-fashioned human ingenuity.

“My role in healthcare is to keep patient data secure, to make sure the data is used how it was intended to be used.”

– Claire Leduc, Senior Network Access Analyst, Surescripts

When Leduc joined Surescripts in July 2021, following a five-year stint as a data scientist, she quickly got to work on conceiving and developing a method for prioritizing anomalous outliers.

The result—Leduc’s “severity score logic”—uses rules and a machine learning algorithm to automate triage, so Surescripts network access investigators like Eric Engelken can hunt anomalies from highest to lowest priority in search of answers: Was it data misuse? Was it a misconfiguration? Was it nothing?

As the name suggests, severity score logic applies a score to each anomaly, to help our investigators focus their efforts.

The Network Access Team at Work

Leduc and her colleagues continually assess and refine rules for machine-learning algorithms across the Surescripts suite of solutions, such as Real-Time Prescription Benefit, and adapt to industry trends.

With continued growth in prescriber utilization of Real-Time Prescription Benefit, it’s critical that Leduc’s team stays focused on continuous improvement. See why more than half of all U.S. prescribers are using this solution to improve medication affordability and adherence for their patients.

As satisfying as math was for Leduc in grade school, with its clear-cut answers, she later found a quantitative “artform” at Columbia University in New York City, where she earned a master’s degree in statistics. Her study included advanced data analysis, which she puts to use at Surescripts.

Once Engelken and the other investigators complete their latest round of inquiries, and there is enough data to conduct a meaningful statistical analysis, Leduc takes several measurements:

  • Did the result of the investigation point to data misuse?
  • What was the source of the anomaly referral, whether human or automated?
  • How long did it take to close the investigation or move it to remediation, if necessary?
  • How many investigations were conducted in the given timeframe?
  • Were any “repeat offenders” a cause for concern?

These efforts result in a continuous, positive feedback loop, which Leduc and the Network Access team use to find the proverbial needle in the haystack of more than 20 billion transactions.

We work to continuously protect and improve access, security and performance for the network and the data it carries. Read more about our commitment to network integrity.

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