Skip to content

Optimizing Community Pharmacy Workflow with Real-time Anomaly Detection Using Artificial Intelligence (GRA-039)

Optimizing Community Pharmacy Workflow with Real-time Anomaly Detection Using Artificial Intelligence is a documented award listed in the DePaul Resource Map (starting 2025). Funder: DePaul ↔ RFUMS partnership. DePaul PIs Sina Ansari (BUS) and Vahid Alizadeh (CDM); RFUMS PI Ateequr Rahman; Award $47,343; real-time AI anomaly detection for community pharmacy workflow optimization.

Summary ~

  • Sponsor / funder: DePaul ↔ RFUMS partnership
  • Program: AI in Biomedical Discovery and Healthcare
  • Status: awarded
  • Period: 2025 – —
  • Lead unit: Driehaus College of Business / Jarvis CDM
  • Amount: $47,343

Access (availability): 🟢 Green — open or routine · 🟡 Yellow — appointment or affiliation · 🔴 Red — restricted · ⚪/⚫ White/Black — not yet classified

Verification: ✓ confirmed · ~ likely · ? needs review · ✕ archived

Award details

  • Topics: artificial intelligence, healthcare, pharmacy
  • Award type: research grant

DePaul PIs Sina Ansari (BUS) and Vahid Alizadeh (CDM); RFUMS PI Ateequr Rahman; Award $47,343; real-time AI anomaly detection for community pharmacy workflow optimization.

People

  • Sina Ansari (PI)
  • Vahid Alizadeh (Co-PI)
  • Ateequr Rahman (Collaborator)

None documented.

No related outputs documented yet.

No related publications documented yet.

Verification

  • Status: ~ Likely current
  • Notes: 2025 RFUMS collaborative award; parent program GRA-029. No DePaul facility named in source.

Sources

Notes

Award metadata compiled from published DePaul research reporting. Listing here records documented activity; it is not an evaluation of the award, the people, or the unit.