In June 2020 at the weekly Protenus all-hands meeting, our CEO Nick Culbertson announced that Protenus was looking for actionable insights to address and eliminate racial and/or gender bias within the company and our products in light of the growing Black Lives Matter movement. In the collective brainstorm that followed this announcement, one suggestion was to evaluate Protenus’ Compliance Analytics Platform to determine if there are any underlying biases in our analytics. We would ask: do the potential compliance violations that we surface equitably represent all demographic segments among hospital employees and patients, or are they biased against some, particularly marginalized, groups?

Why do we choose the tools we do? If I'm asked to hang a picture in my son’s room, I grab a hammer and a nail or hook. If I’m asked to write a program, then I grab a programming language (compiler) and toolset. Equally easy, no? The challenge is in choosing the right tool—programming language—for the job. A framing hammer would not work to install trim, for instance; a dryer would ruin fine linen. And so it goes. 

Have you ever answered a CAPTCHA while trying to access a website? Well then it's likely that you are an integral part of training classifier algorithms. Not only do those CAPTCHAs prove you’re a human, they also serve as large repositories for labeled datasets. You are likely teaching the next generation of self-driving cars the difference between a pole and a pedestrian.

Subject matter experts are the lifeblood of product development at Protenus. Although these experts exist throughout the industry — as founders, external consultants, colleagues, or friends in the industry — our best information comes from our own customers. The conversations we have with them play a key role in influencing our quarterly product planning process.

At the heart of Protenus Compliance Analytics platform lies the Artificial Intelligence (AI) engine that monitors millions of accesses to patient data every day and determines how likely it is for each to warrant further investigation. This AI engine, or the “classifier" as it’s known in data science, is a relatively little nugget compared to all the different processes our clients’ data go through every day to produce cases which are narratives explaining why certain interactions have been flagged as suspicious. Just a fraction, less than 2%, of a day’s run is spent on classification, while the rest goes to pre-processing, data aggregation, feature calculation, and alert and case generation.