Healthcarercm

  Clinical documentation improvement (CDI) is a method for healthcare providers to collect, track, and analyze patient data while providing valuable feedback to physicians. Even though electronic health record (EHR) systems have been around for many decades, they have only partially succeeded in dealing with complex data sets. There is a growing demand for improved CDI technology, and AI (Artificial Intelligence) tools are the solution.  CDI programs backed by AI tools  have the potential to transform healthcare organizations dramatically. Significance of Accurate CDI With the growing need for better data quality, accurate reimbursement, and improved patient outcomes, the significance of a robust clinical documentation improvement (CDI) has grown. Healthcare systems require accurate CDI to bridge the gaps in coding and maintain quality care. CDIs must align to the level of care required for each patient, to the patient’s medical history and population health. Quality rati...

As discussed in “ Unlocking Missed Revenue Potential with Effective Contract Management ,” underpayments represent a significant and often silent challenge to realizing healthcare revenue. The difference between the amount billed and the amount paid (expected vs. allowed) can lead to significant revenue leakage. These leakages also highlight broader operational inefficiencies including capacity constraints, skill gaps, contract misunderstandings, and ineffective collaboration between revenue cycle operations and managed care teams. Underpayment Recovery Through Flexibility Given the complexities in the healthcare revenue cycle, a customized approach to underpayment recovery is most effective. Each health system has its unique challenges, operational framework, and financial goals, requiring tailored  underpayment recovery services  to effectively address and resolve their specific underpayment issues. Healthcare organizations should look for a partner with a spectrum of soluti...

  The evolution of  computer-assisted coding  (CAC),  computer-assisted professional coding  (CAPC), and artificial intelligence (AI) in revenue cycle management (RCM) and medical coding is impacting how healthcare information is processed, coded, and managed. Currently,  coding automation  methodologies leverage technology to scan clinical documentation and suggest relevant diagnoses, procedures, and treatments. That automation helps improve efficiency, accuracy, and consistency, allowing coders to focus their attention on more complex cases while maintaining compliance and helping to facilitate smoother reimbursement processes. In contrast, AI in medical coding automatically assigns codes without human assistance, analyzing vast amounts of data to identify patterns and guidelines. It is most commonly being deployed today in areas with high volumes of data and repetitive codes, including emergency medical settings and outpatient medical imaging, and s...