table, sit the recovery group, a team of four to five people who
interact with participants on the platform. Everyone faces a computer screen.
The technologists work with the data that sensors are pulling
o; participants’ phones as well as from their interactions with
the recovery team, identifying patterns that signal a move in the
wrong direction. Twenty-four hours a day, seven days a week,
Triggr actively watches over everyone on the platform, with a
single member of the recovery team following 500 people at any
time. Each participant has a rating on a scale of 1 to 10 based
on the patterns Triggr’s algorithm is tracking. A 1 means things
are going very well. A 10 is an alert that the person is exhibiting a pattern of behavior that may be on the edge of relapse and
needs to be contacted.
Most sta; communication with clients takes place via text
or app messaging. Without the clues they might get in person
from eye contact and body language, or on the phone from
someone’s tone of voice, the team relies heavily on alerts from
Triggr’s machine-learning systems have made the platform
smarter over time by studying both those interactions with participants and the millions of data points collected from their
smartphones. The systems search for anomalies, breaks from a
client’s typical routine. As more people use the system and more
data is gathered and studied, the ability to see signs of a potential
relapse improves. Eighty-five percent accurate a year ago, Triggr
can now predict with 92 percent accuracy when a client is likely
to slip in the next three days. The early intervention such predictions make possible is significantly improving clients’ results,
the company says.
The messiness of the data is what convinced Triggr’s data
scientist, John Santerre, that machine learning could be e;ective against the problem. Some of the most important warning signs of an impending slip have nothing directly to do with
drugs or alcohol. Instead, they’re life events, like the death of a
family member or another user, an a;air, an issue with housing.
Just one deviation from a client’s normal routine—something