strategic design & innovation, service design
strategic design & innovation, service design
PROBLEM statement
PROBLEM statement
Over 9.6 million Americans suffer from diabetic retinopathy, with 1.84 million facing vision-threatening stages, yet 60% of diabetic patients do not receive annual eye exams. Early stages are typically symptomless, leading to late diagnoses and preventable vision loss. Current diagnostic methods are manual, resource-intensive, and inaccessible to underserved populations. NYC public hospitals, already strained by high patient loads, lack scalable, efficient tools for early detection and intervention.
Traditional healthcare workflows are not equipped to handle the growing diabetic population efficiently, leading to poor patient outcomes, operational inefficiencies, and escalating costs.
Over 9.6 million Americans suffer from diabetic retinopathy, with 1.84 million facing vision-threatening stages, yet 60% of diabetic patients do not receive annual eye exams. Early stages are typically symptomless, leading to late diagnoses and preventable vision loss. Current diagnostic methods are manual, resource-intensive, and inaccessible to underserved populations. NYC public hospitals, already strained by high patient loads, lack scalable, efficient tools for early detection and intervention.
Traditional healthcare workflows are not equipped to handle the growing diabetic population efficiently, leading to poor patient outcomes, operational inefficiencies, and escalating costs.
solution
solution
This project proposes the deployment of ARDA (Algorithm for Retinal Disease Analysis)—an AI-based screening tool for early detection of diabetic retinopathy and glaucoma—within NYC public hospitals. By integrating ARDA into primary care workflows, hospitals can:
Expedite diagnosis with AI-driven, automated screening.
Increase access to eye exams across underserved populations.
Minimize resource wastage through predictive risk assessment.
Improve patient outcomes via timely intervention.
Reduce operational costs and carbon footprint by optimizing diagnostic resource usage.
AI integration positions NYC Health + Hospitals as a leader in accessible, efficient, and sustainable preventive healthcare.
in association with unsdg 3,4 & 9.
This project proposes the deployment of ARDA (Algorithm for Retinal Disease Analysis)—an AI-based screening tool for early detection of diabetic retinopathy and glaucoma—within NYC public hospitals. By integrating ARDA into primary care workflows, hospitals can:
Expedite diagnosis with AI-driven, automated screening.
Increase access to eye exams across underserved populations.
Minimize resource wastage through predictive risk assessment.
Improve patient outcomes via timely intervention.
Reduce operational costs and carbon footprint by optimizing diagnostic resource usage.
AI integration positions NYC Health + Hospitals as a leader in accessible, efficient, and sustainable preventive healthcare.
in association with unsdg 3,4 & 9.
