How a Healthcare Network Accelerated Clinical Trial Recruitment with Dedicated AI Pods

Learn how a US-based healthcare provider partnered with Netsmartz to deploy an AI Pod that streamlined patient identification for oncology trials, improving enrollment rates and physician efficiency.

Industry Healthcare, Oncology & Clinical Research
Location United States
Medical team clinical trials

Deliverables

Clinical NLP Model Development
EHR Integration
Compliance-First MLOps
Physician Dashboard
AI Pod Support

Client Overview

The client is a regional healthcare network running multiple oncology clinical trials. Their research teams manually screened thousands of electronic health records (EHRs) to identify eligible patients, an error-prone, slow process causing low enrollment and high administrative burden.

Medical technology analysis

Business Challenges

The client needed to scale trial recruitment but faced three critical bottlenecks:

1

Manual Screening Delays

Clinical staff spent 15–20 hours per week reviewing EHRs for a single trial, leading to missed patient matches and slow enrollment.

2

Complex Data Fragmentation

Eligibility criteria involved unstructured clinical notes, lab results, and medication histories scattered across incompatible systems.

3

Regulatory Compliance Risks

Any automated solution required strict HIPAA compliance, audit trails, and physician oversight—barriers that stalled prior tech initiatives.

Our AI Pod-Led Solutions

We at Netsmartz deployed a specialized healthcare AI Pod with clinical data scientists, NLP engineers, and compliance experts to build a secure, physician-assisted matching system.

Clinical NLP for Eligibility Automation

The Pod built a BERT-based NLP model to extract and interpret eligibility criteria from trial protocols and match them with structured and unstructured EHR data.

HIPAA-Compliant MLOps Pipeline

We implemented an on-premise MLOps environment with encrypted data pipelines, role-based access, and full audit logging, ensuring PHI security & regulatory adherence.

Physician-in-the-Loop Dashboard

Designed a dashboard that presented AI-matched patients with explainable reasoning, allowing oncologists to review and approve matches before outreach.

Results & Achievements

90%
accuracy in patient-trial matching
60%
reduction in manual screening time per trial
40%
increased monthly patient enrollment across active trials
100%
adherence to HIPAA regulatory standards

Tech Stack Used

NLP & Modeling

BERT (via Hugging Face) spaCy Scikit-learn

Data & Security

FHIR APIs PostgreSQL with column-level encryption HashiCorp Vault

MLOps

Kubernetes (on-prem) MLflow Docker

Frontend

React-based dashboard with role-based UI

Key Takeaway

By combining clinical expertise with compliant AI engineering, the Netsmartz AI Pod transformed a manual, tedious screening process into a scalable, accurate, and trustworthy system. This ultimately accelerated trial timelines without compromising physician oversight or regulatory standards. Ready to bring your healthcare AI ideas to life within just 90 days?

Find your ideal AI Pod here!

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