We engaged Viston to reduce emergency department overload and claim denials using AI triage, clinical summarization, and claims automation.

Business challenge

Long triage wait times, rising LWBS (left-without-being-seen), and high claim denials.

Fragmented notes across EHR modules made clinician burden high and documentation inconsistent.

Our approach and solution

Ran a discovery and data-privacy assessment, then delivered an AI triage assistant and clinical note summarizer integrated with our EHR.

Automated first-pass claims coding with human-in-the-loop review to improve accuracy and speed.

AI applications and benefits delivered

Nurse-facing triage chatbot for symptom intake and acuity scoring; reduced triage times and improved consistency.

LLM-based clinical summarization that structures SOAP notes and ICD-10 suggestions.

Claims coding assistant that flags missing documentation and auto-populates CPT/ICD.

Cost of implementation

USD 580,00 for MVP and production hardening.

Time to implement

16 weeks MVP; 28 weeks to full rollout across 7 hospitals.

Tools and technologies used

Azure OpenAI Service, Presidio for PHI de-identification, FastAPI, FHIR interfaces, Epic APIs, Azure Kubernetes Service, Azure Cognitive Search, Redis, PostgreSQL, MLflow, Evidently AI, Power BI, Terraform.

Quantitative outcomes

28% reduction in triage wait time.

12% reduction in claim denials.

35% faster claim turnaround.

+18 NPS in patient digital experience.

Key performance indicators (KPIs) tracked

Triage cycle time, LWBS rate, claim denial rate, coder productivity, summarization accuracy, PHI leakage incidents.

Pre- and post-implementation metrics

Average triage time: 41 minutes → 29 minutes.

LWBS: 5.2% → 3.7%.

Denial rate: 9.4% → 8.3%.

Claim processing TAT: 9.2 days → 6.0 days.

Stakeholder quotes or testimonials

“The AI triage assistant gave us back clinician time while standardizing acuity scoring.” — VP, Clinical Operations.

“Our first-pass coding accuracy improved immediately, with fewer payer queries.” — Revenue Cycle Director.

Regulatory or compliance considerations

HIPAA, SOC 2, BAA in place with all AI vendors, PHI redaction at ingestion, prompt logging without PHI, audit trails, model output explainability for coding.