Sushmita Narayana
Founder
J
JeriMed
JeriMed.ai provides an AI‑driven platform that analyzes medication lists for older adults, scores the safety and cost impact of each regimen, and flags high‑risk drug combinations. It offers evidence‑based deprescribing recommendations, therapeutic alternatives, and cost‑impact projections, with dashboards that track risk reduction and financial outcomes for clinicians and care administrators in post‑acute and long‑term care settings.
- Artificial Intelligence
- Data & Analytics
- Healthcare Technology
Updated 6 months ago
Funding
Funding rounds are not available yet.
Founders
Product
Problem
Healthcare providers often struggle with managing complex medication regimens for older adults, leading to high rates of polypharmacy, adverse drug events, and related hospitalizations. Identifying high‑risk drug combinations and finding safe, cost‑effective alternatives requires extensive data analysis that exceeds the capacity of standard drug‑interaction tools.
Solution
JeriMed.ai delivers an AI‑driven medication optimization platform that ingests patient medication data and generates risk scores for each drug regimen. The system highlights high‑risk combinations—such as those increasing fall risk—and ranks them by ease of intervention and expected impact. Clinicians receive evidence‑based suggestions, including deprescribing protocols, therapeutic alternatives, and monitoring parameters, all tailored to patient comorbidities and facility capabilities. The platform also estimates cost savings from reduced adverse events and hospital readmissions, providing both individual‑patient and population‑level analytics. Real‑time dashboards track risk reduction over time, enabling care teams to monitor progress and demonstrate financial impact.
Target Audience
Primary users are clinicians, pharmacists, and care administrators in post‑acute and long‑term care settings who manage polypharmacy for older adults.
Features
- AI‑powered risk scoring that quantifies the safety and cost impact of each medication in a regimen
- Hub analysis that identifies root‑cause drug clusters contributing to adverse outcomes such as falls
- Evidence‑based therapeutic alternatives and deprescribing pathways customized to patient comorbidities and care setting
- Cost‑impact modeling that projects savings from avoided hospitalizations and readmissions
- Temporal analytics to track risk changes and intervention outcomes at both patient and cohort levels
- Integrated alerts designed to reduce alert fatigue while prioritizing actionable, high‑impact interventions