Business Challenge
Medical reports are notoriously difficult for patients to understand. Salomatic needed a way to translate complex doctor notes and lab results into clear, actionable advice.
Multi-Stage Extraction Pipeline
They used DSPy to create a rigorous pipeline that extracts, validates, and interprets medical data.
The Pipeline
Python
class MedicalReportPipeline(dspy.Module):
def __init__(self):
self.extract_labs = ChainOfThought("notes, results -> panels")
self.extract_diagnoses = ChainOfThought("notes, history -> diagnoses")
self.generate_consultation = ChainOfThought(
"profile, labs, diagnoses -> consultation"
)
def forward(self, notes, results, patient):
panels = self.extract_labs(notes, results)
diagnoses = self.extract_diagnoses(notes, patient)
return self.generate_consultation(patient, panels, diagnoses)Observability with Langtrace
Integration with Langtrace provided deep visibility into where data extraction might be failing, allowing them to pinpoint issues and improve accuracy rapidly.
Results
- 87.5% Reduction in manual corrections
- 90% Faster report generation
- 50x Capacity Increase