SBIR Phase II: NLP Driven Automation for Optimizing New Patient Referral Pathways

NSF Award Search · 01002526DB NSF RESEARCH & RELATED ACTIVIT · $1,250,000 · view on nsf.gov ↗

Abstract

The broader impact and commercial potential of this Small Business Innovation Research (SBIR) Phase II project lie in potentially improving the efficiency, accuracy, and equity of new patient referral triaging. Inefficient referral management leads to treatment delays, poor clinical utilization, and logistical bottlenecks— challenges that are problematic nationwide when transferring patient information across hospital systems. This project streamlines referral workflows, reducing administrative burdens and optimizing healthcare resource allocation. Beyond operational improvements, this innovation addresses incomplete work-ups, missing records, and inaccurately scheduled appointments. By analyzing referral inflows and outflows, the technology identifies inefficiencies, alerts coordinators, and helps ensure access to care. Commercially, this solution meets the growing demand for data-driven referral management in hospitals, clinics, and healthcare networks, helping institutions reduce costs when physicians are operating at the “top of their license” and improve patient outcomes. As healthcare systems shift toward value-based care, this project has the potential to become a scalable, industry-leading solution. By enhancing care coordination and accessibility in a highly fragmented healthcare system, this project advances both scientific and technological understanding while offering a commercially viable tool to reshape referral management nationwide. The proposed project add

Key facts

NSF award ID
2512998
Awardee
IIAM CORPORATION (CA)
SAM.gov UEI
ZK6GD2N1PQG1
PI
Max Jiam
Primary program
01002526DB NSF RESEARCH & RELATED ACTIVIT
All programs
Health Care Enterprise Systems
Estimated total
$1,250,000
Funds obligated
$1,250,000
Transaction type
Cooperative Agreement
Period
07/15/2025 → 06/30/2027