Healthcare
20% faster clinical decisions, 150+ patients onboarded in 12 weeks.
We enhanced PDC's existing remote patient monitoring platform with AI-powered vital sign analysis, predictive risk stratification, and automated clinical summaries - cutting clinical decision-making time by 20% and scaling to 80+ clinics within three months.
Client
PDC
Industry
Healthcare
Timeline
12 weeks
Team Size
6 engineers
Impact
Clinical decision time
HIPAA compliance
Patients onboarded
Clinic adoption
Clinical decision time: Reduction in clinical decision-making time through AI-powered analysis and automated recommendations.
HIPAA compliance: Full HIPAA compliance maintained through AWS Bedrock's compliant LLM hosting and data anonymization pipeline.
Patients onboarded: New patients onboarded to the AI-enhanced platform within the first 12 weeks.
Clinic adoption: Clinics actively using the AI-enhanced platform within three months of launch.
“The AI layer transformed our platform from a data display into a clinical decision tool. Providers tell us the automated risk scores and summaries save them real time every day.”
The Challenge
PDC had an established RPM platform supporting continuous glucose monitors and blood pressure monitors, but clinicians manually reviewed every patient reading - creating bottlenecks as the patient base grew and delaying identification of at-risk patients.
The competitive RPM market demanded differentiation. Without AI-driven analysis, PDC's platform offered the same commodity data display as every other RPM vendor, with no automated insight layer to attract new clinic partnerships.
Billing compliance tracking for CMS reimbursement codes was manual and error-prone, with providers missing revenue because end-of-month summaries required hours of chart review per patient.
What We Built
Integrated AWS Bedrock with Anthropic Claude 3 Sonnet for HIPAA-compliant AI analysis of patient vitals, balancing cost efficiency with clinical accuracy across thousands of daily readings from CGM and BPM devices.
Built an AI-driven abnormality detection engine that evaluates readings against historical patient trends and established medical norms, generating smart alerts with tailored recommendations for providers rather than raw threshold-based notifications.
Developed predictive risk stratification that groups patients by acuity level, enabling care teams to prioritize high-risk patients and allocate resources where clinical intervention has the greatest impact.
Created automated end-of-month AI summaries and billing compliance predictions, eliminating hours of manual chart review and reducing missed reimbursement opportunities.
Related Work
AI in RPM moves beyond simple threshold alerts by analyzing patient vitals against historical trends and medical norms. Instead of alerting on every high reading, AI identifies clinically meaningful patterns - sustained trends, abnormal combinations, and deviations from a patient's personal baseline. This reduces alert fatigue while catching genuine risks earlier.
Next Step
One call with a founder. No sales team, no follow-up sequence. If we can't help, we'll say so.