Save the dateLab Data Summit 2027April 8 · PhiladelphiaRequest your invitation

Case study · AI patient finding · Gaucher disease

Find the pattern behind
a potential diagnosis.

A global biopharmaceutical company used lab results and medical history to flag potential Gaucher patients and support weekly physician follow-up.

Find the pattern across the history

Patient profile
Example lab signalLow beta-glucosidase
Profile review
ClientGlobal biopharmaceutical company
IndicationGaucher disease
Use caseUndiagnosed patient finding
1 per weekPatient with low beta-glucosidase, on average, in the reported program periods

Weekly signals
for physician follow-up.

Recurring alerts supported field-team outreach to relevant physicians. Lab-profile matches identified potential patients for further evaluation.

The challenge

The clinical signal
can span years of lab history.

Patients may move through years of abnormal lab results before receiving a diagnosis. The pattern can be present across the data while remaining difficult for any single physician to see in full.

The company needed an approach that could review disease-specific signals across laboratory results and medical history, then connect those findings with a physician-outreach workflow.

The approach

How Prognos AI solved it

The program moved from a review of disease-specific laboratory signatures to clinical profiling and recurring physician alerts.

Scan.

Review laboratory results for disease-specific signatures, including low beta-glucosidase.

Profile.

Use laboratory results and medical history to define clinical profiles and flag potential Gaucher patients.

Alert.

Deliver weekly alerts to support field-team outreach to relevant physicians.

The reported outcome

A recurring patient-finding
signal for the field team.

The source reports an average of one patient per week with low beta-glucosidase during the cited 2025 program periods.

Weekly alerts supported physician follow-up, giving the field team a recurring way to review relevant lab-profile findings.

A profile match is a starting point for further evaluation. It does not confirm a Gaucher diagnosis.

Weekly alert workflow

Recurring delivery to support physician outreach

MTWTFS↗
1Patient per week, on average,
with low beta-glucosidase

Where else this approach may apply

When a lab pattern can
precede a diagnosis.

The source identifies these as potential applications of the approach, rather than additional reported client outcomes.

Late-onset PompeFabryPKU

What the case illustrates

Make a dispersed pattern
available for follow-up.

The program used longitudinal clinical information to support patient finding and recurring HCP outreach.

Look across the history.

Evaluate lab results with medical history so patient-finding profiles can reflect more than one isolated signal.

Connect to a workflow.

Bring potential patient findings into a recurring alert process that supports follow-up with relevant physicians.

Explore patient finding

What could the lab history
help your team see?

Talk with Prognos about your rare disease program, relevant laboratory signals, and physician engagement priorities.

Find the signal.
Support the next conversation.

Explore lab-informed patient finding and recurring HCP alerts with Prognos.

Talk with Prognos
Cookie Preferences