Case Study: MyNurse.ai

Case Study: Scaling AI-Powered Remote Patient Monitoring

The Challenge

24/7 remote patient monitoring created overwhelming workloads for primary care physicians. The need was for intelligent automation that could maintain clinical quality while reducing provider burden and improving patient outcomes. As Chief Of Staff, it was my job to ensure cross functional executive leadership alignment and day-to-day operations.

The Approach

Intelligent Automation & AI Design

• Designed ML algorithms to analyze multimodal patient data (vitals, symptoms, behaviors) for risk stratification.

• Integrated wearable devices and patient-reported outcomes directly into clinical workflows.

Clinical Decision Support

• Built automated alert system triggering interventions based on established clinical protocols.

• Created a provider dashboard to reduce cognitive load, prioritizing patients based on risk level.

Iterative Optimization

• Implemented continuous feedback loops enabling clinicians to refine ML model performance and accuracy.

The Solution

An AI-powered RPM platform that automated chronic disease management. ML algorithms monitored patients 24/7, flagged high-risk cases, and enabled proactive interventions—reducing PCP workload while improving patient outcomes.

Key Outcomes

• 41% average blood pressure decrease in hypertensive patients

• 2 hours per day saved per medical office through automation

• $25K additional annual revenue per practice via CCM billing codes

• 95% patient satisfaction with remote monitoring experience

• Platform validated as proof point for Series A fundraising

Bottom Line Impact

Demonstrated how AI-enabled automation could scale care delivery, maintain quality, and deliver measurable ROI for both practices and investors.

Skills Applied

ML Algorithm Design | Remote Patient Monitoring | Clinical Workflows | Chief of Staff Leadership | Wearable Device Integration | CCM Program Design