Healthcarercm

  Deciding to use  offshore support  to help manage your revenue cycle and contain revenue is an important decision that shouldn’t be made lightly. Using offshore talent isn’t new. Those working in IT have used offshore talent for decades. But is working with offshore talent for revenue cycle management (RCM) right for your organization? Increasing rules, regulations, and annual updates to medical codes add pressure to medical coders who struggle to keep up. Add  labor shortages  to that list of challenges, and it becomes clear how offshore talent can be beneficial. Know more about  What is revenue cycle management in healthcare But not all offshore RCM vendors are created equal. Our new  eBook shares how AGS Health stands apart from the crowd  and how our customer-centric approach enables leading healthcare organizations to improve their revenue cycle accuracy, productivity, and efficiency, leading to reduced denials, more reliable cash flow, and...

  Automation and artificial intelligence (AI) in revenue cycle management (RCM) have numerous real-world applications as healthcare organizations seek to implement advanced technology and tools. This first article in our three-part RCM automation series explores the automation continuum and the various forms of automation that are reshaping the healthcare industry. Discovering the Automation Continuum The automation continuum can be categorized across four progressive stages of increasing complexity and capability: basic automation, advanced automation, intelligent automation, and generative AI. Basic Automation  emphasizes rules-based processes designed for repetitive tasks, which operate under predefined instructions. This foundational level of automation performs simple, routine operations, reducing manual workload and minimizing errors. An illustrative example in healthcare is the automated claim status query process, where a standard transaction prompts a predetermined r...

  Clinical denials   are a growing challenge for healthcare organizations, leading to lost revenue and increased administrative burden. With the rise of payer automation and stricter policies, hospitals are facing mounting pressure to defend their care decisions. However,   artificial intelligence   (AI) is emerging as a powerful tool to transform clinical denial management from a reactive to a proactive approach. Below are five ways AI can make a difference in successfully  fighting clinical denials : Automated Medical Necessity Reviews AI-driven systems can flag documentation gaps and predict medical necessity denials before they occur. By identifying high-risk cases early, healthcare organizations can take proactive measures to strengthen documentation and prevent denials. 2.  Predictive Modeling for Risk Stratification Advanced analytics powered by AI enable predictive modeling to assess the likelihood of denials based on historical data (payer, diagnos...

  Denial management and prevention  remains a critical area of focus in healthcare revenue cycle management (RCM) for healthcare organizations and payers alike. Denial management is expensive, and if it is not avoided initially, it involves costly rework. As technology advances and payer behaviors shift, healthcare organizations must adapt to new challenges to ensure efficient claims processing and revenue protection. The Rise of AI and Automation in Denial Management In addition to robotic process automation (RPA), a trend in denial management is the increasing use of artificial intelligence (AI). Payers have embraced these technologies to streamline claims adjudication, reduce human intervention, and handle larger claim volumes. Leveraging AI can help healthcare organizations automate denial remediation processes to save time and reduce costs. AI-powered tools can now identify missing information in patient accounting systems, such as authorization numbers, and automatically...

  As healthcare organizations face increasing operating costs, policy changes, and more aggressive payer tactics, leaders need strategies to ensure financial stability. AGS Health recently sponsored a presentation to provide insights impacting health systems and revenue cycle management (RCM). Madeline Cree of The Health Management Academy outlined how the One Big Beautiful Bill Act (OBBBA) reforms are impacting Medicaid and Medicare, as well as reshaping supplemental payment structures and Affordable Care Act (ACA) subsidy design. Emily Paxman of KLAS Research shared insights into how healthcare organizations are responding to uncertainty, including investing in technology, restructuring their workforce, and exploring value-based care strategies. Amid these ongoing policy shifts and insights, I also shared three key revenue cycle management strategies for healthcare organizations to thrive: Every dollar matters, especially when revenue leakage and underperformance threaten finan...

Healthcare revenue cycle management (RCM) faces growing challenges, including revenue loss from claim denials, rising costs, and administrative complexity. With evolving payer regulations and unclear denial logic, healthcare organizations often struggle to maintain efficiency using traditional tools. To succeed, providers must adopt  predictive analytics and artificial intelligence (AI)  to move from reactive decision-making to proactive, data-driven strategies. Why Predictive Analytics Matters in Healthcare RCM Predictive analytics in healthcare revenue cycle management helps organizations: Forecast claim denials and prevent revenue loss Optimize accounts receivable (A/R) workflows Improve cash flow predictability Reduce administrative burdens and rework By analyzing historical data and applying machine learning models, predictive analytics enables smarter, faster data-driven decisions that directly improve financial outcomes. Analytics Maturity Model for RCM Healthcare organ...