Billing & Reimbursement Essentials for CV Professionals-Tracking KPIs & Driving Performance in the CV Revenue Cycle

Video Transcription

Video Summary

In this module on Tracking Key Performance Indicators (KPIs) for Cardiovascular Revenue Cycle, Nicole Knight emphasizes the importance of KPIs in linking billing processes to financial health and overall organizational strategy. She highlights barriers such as fragmented workflows, evolving payer rules, limited resources, and low patient financial literacy. Effective revenue cycle management requires governance, leadership, standardization, teamwork, and data engagement.<br /><br />A paradigm shift from back-end cleanup to front-end accuracy—covering scheduling, authorizations, eligibility, and documentation—reduces denials and lost revenue. Key KPIs include charge capture lag, denial rates, net collection rate, days in accounts receivable (AR), case mix index, and cost-to-charge ratios, with targets like denial rates under 10% and net collection rates above 95%.<br /><br />Operationally, success depends on clear ownership, benchmarking, transparent communication tailored to audiences, and ongoing audits and education. Workflow recommendations promote specialized cardiovascular coding, revenue integrity staff, and clinical documentation improvement roles. Outsourcing, while offering scalability and tech advantages, poses challenges like loss of specialty knowledge and accountability.<br /><br />AI and automation enhance efficiency by reducing denials, improving claim processing, and boosting staff productivity, but require careful implementation. Strategic pillars include revenue integrity, operational excellence, financial growth, and innovation. Sustainable growth involves integrating professional and facility functions into a unified, high-performing cardiovascular service line, engaging all stakeholders, and leveraging KPIs for continuous improvement.

Keywords

Cardiovascular Revenue Cycle

Key Performance Indicators

Revenue Cycle Management

Denial Rates

Charge Capture Lag

AI and Automation in Healthcare

Clinical Documentation Improvement