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Revenue Cycle Management Best Practices: Overseeing Billing, Coding, and Collections for Timely, Accurate Reimbursement
Effective revenue cycle management (RCM) is essential for healthcare organizations, as it plays a crucial role in achieving financial stability and operational efficiency. Overseeing the entire revenue cycle process involves careful attention to detail in billing, coding, and collections. Ensuring timely and accurate reimbursement is vital, as it directly impacts the financial health of healthcare providers. Implementing best practices in RCM can lead to improved cash flow, enhanced revenue integrity, and compliance. In this guide, we'll explore effective strategies for RCM oversight, focusing on medical billing accuracy, claims management, and denial management. Understanding the Revenue Cycle: Key Stages and Stakeholders…
Five Ways AI Will Transform Non-Clinical Healthcare Administration in 2026 — and What Hospital Leaders Should Do Now
Artificial Intelligence (AI) will fundamentally reshape non-clinical healthcare administration. While much of the attention around AI has focused on clinical decision support and diagnostics, the most immediate and scalable impact is occurring in the healthcare back office—where administrative complexity, workforce strain, and financial pressure continue to intensify. For hospital and health system leaders, AI is no longer a future consideration. It's becoming a core operational capability that will influence staffing models, financial performance, patient access, and regulatory compliance. Additionally, the organizations that prepare now will be better positioned to manage volatility, reduce burnout, and operate more predictably in an increasingly…
Why AI Matters in Nonclinical Healthcare Operations
Nonclinical functions form the backbone of hospital performance. Coding, documentation, billing, scheduling, care coordination, and data abstraction directly affect revenue, compliance, patient experience, and staff sustainability. These areas are also among the most labor-intensive and vulnerable to variability. AI helps address this by automating rules-based work and surfacing insights from large volumes of operational and clinical data. The result is not just efficiency, but greater predictability—in staffing, throughput, and financial outcomes. Where Health Systems Are Applying AI Today Health systems are moving beyond isolated experiments and deploying AI across multiple nonclinical domains: Coding and clinical documentation support Natural language processing…
