Healthcare Platform Reduced ER Admissions by 18% with Infoslab’s Clinical AI Talent

Project Overview

Our client is a US-based digital health platform serving over 2 million patients with chronic illnesses. Their mission is to shift from reactive, episodic care to proactive interventions. But their clinical data — mostly unstructured physician notes and distributed EHRs — held critical insights locked in textual form.

Industry

Health care

Challenge

Unstructured data chaos: Over 100 million notes with risk indicators embedded in free-text.
Poor stratification: Traditional scoring missed emerging risks in comorbid cases.
Lack of in-house clinical NLP: Hard to recruit talent with healthcare + AI experience.
Compliance overhead: HIPAA/GDPR risks from working with patient-sensitive data.

Infoslab’s Talent-Led Solution

Infoslab onboarded a specialized Clinical AI Pod consisting of:

NLP Scientist: Used BioBERT and ClinicalBERT to extract diagnoses, medications, and lifestyle indicators from notes.

ML Engineer: Developed explainable ensemble models to stratify risk (e.g., 30-day readmission).

Clinical Analyst: Mapped notes to SNOMED, ICD-10, and LOINC codes.

Compliance Architect: Built end-to-end audit logs, encryption layers, and de-ID pipelines.

Business Impact & Cost Savings

KPI Before Infoslab After Infoslab Impact Prediction Accuracy 74% 92% +18% ER Visits (per 10k patients) 1900 1560 –18% Model Interpretability None SHAP/LIME Clinical trust ↑ Deployment Time 14 weeks 7 weeks 50% faster Build Cost (One-time) $800K $600K –25% ($200K saved) Total Estimated Savings — — $850K+ annually

Why Infoslab?

Niche AI Talent: Fast access to rare clinical NLP specialists.

Security-Centric Build: Data anonymization and governance from Day 1.

End-to-End Delivery: From ETL to dashboard, all within a single pod.

Get in Touch with Infoslab

Technical Highlights

Combined structured labs, vitals, and progress notes for holistic view.
Used SHAP and LIME to explain risk scores to physicians.
Enabled EHR integrations with HL7 FHIR interfaces.
Anonymized patient identifiers to comply with HIPAA, GDPR, and local data laws.

Delivery Highlights

Early interventions led to measurable drop in ER utilization.
Clinicians felt empowered, not replaced, by explainable models.
Attracted 3 new partnerships from large hospital groups.