On Demand: Addressing the Pervasive Burden of Delayed Identification of Disease-Webinar Recording

Video Transcription

Video Summary

This webinar addresses the critical issue of delayed identification in valvular heart disease and showcases an innovative case study from Cedars-Sinai using Epic’s EHR system combined with Biome’s cloud-based analytics. The expanding patient funnel and increasing prevalence of valvular heart disease demand better operational efficiencies to improve timely care, reduce complications, and optimize financial margins.<br /><br />Biome integrates clinical and financial data by linking administrative billing data with national registry data to provide physicians and administrators a unified, risk-adjusted view of performance. Their platform, utilizing AI and natural language processing, automates echo report analysis to identify patients with severe disease earlier, facilitating timely intervention and reducing urgent cases. This approach has demonstrated over $1 million in added margin by converting urgent TAVR cases into elective procedures, improving outcomes and resource use.<br /><br />Cedars-Sinai enhanced the Epic Patient Tracking Tool with customized smart forms, scripting, checklist tasks, and dashboards to longitudinally track patients through their care journey—from referral to follow-up—replacing inefficient manual methods like sticky notes and spreadsheets. This system enables care teams to manage and schedule interventions efficiently, achieve documentation compliance, and support clinical trial enrollment.<br /><br />Clinically, early identification improves patient outcomes by preventing progression to symptomatic disease with high mortality, decreasing emergency visits, and reducing hospital length of stay. Operationally, it frees capacity, supports program growth, and alleviates clinician burnout.<br /><br />Key success factors include interdisciplinary collaboration, iterative development over 12–18 months, clinician champions, and a balance between technological solutions and preservation of the patient-clinician relationship. The model is adaptable to other conditions and healthcare systems beyond Epic, emphasizing that effective use of existing data and infrastructure can transform cardiac care delivery and financial performance.

Keywords

valvular heart disease

delayed identification

Epic EHR system

Biome cloud analytics

AI echo report analysis

urgent TAVR to elective conversion

patient tracking tool

clinical and financial data integration

early intervention outcomes

interdisciplinary collaboration