Unleash the power of AI and analytics, transforming the RCM game. Explore how these technologies revolutionize Revenue Cycle Management for efficiency.
Technological advancements are driving seismic shifts across various industries, with the healthcare sector being no exception. The US healthcare Revenue Cycle Management (RCM) market, encompassing patient intake and revenue management from initial contact to final payment, has made significant strides in patient data analysis with the integration of Artificial Intelligence (AI) and analytics.
These technologies are revolutionizing diverse areas such as medical transcription, coding, claims processing, fraud detection, payment estimations, and denial management among others. This article will explore the significant role that AI and analytics play in the RCM market, examining their value for healthcare providers and payers, and looking ahead to future innovations. The common theme? Maximizing profits and processing efficiency without compromising patient care standards.
AI and analytics in RCM
In the US RCM market, Robotic Process Automation (RPA), a form of AI, is deployed to automate repetitive tasks, such as data entry and processing. More advanced AI can analyze and contextualize vast data volumes identifying trends and patterns that might be time-consuming for humans to identify. These trends include those associated with patient or payer behavior, payment patterns, and claim status.
Although both RPA and ML can identify patterns, their primary function is to enhance accuracy and speed, rather than suggesting future actions. For instance, a recent study by Change Healthcare suggests that two-thirds of healthcare facilities and health systems are using AI to assist their revenue cycle. Of those, 72% of respondents are using AI applications for eligibility and benefit verification and 64% for payment estimation.
The 2022 State of Revenue Integrity survey by the National Association of Healthcare Revenue Integrity (NAHRI) also highlights numerous other AI-driven areas in RCM, including charge description master (CDM) maintenance, charge capture, denial management, payer contract management, physician credentialing, and claim to audit.
Transforming transcription, coding, and predictive analysis
Some of the most significant uses of AI and analytics in RCM are in medical transcription, coding, claims processing, fraud detection, and predictive analysis. Using conversational AI and natural language processing (NLP) coupled with ML has freed up more than 15% of physicians and clinicians’ time previously spent dictating medical records to transcriptionists — which allows them to focus more on patient care.
AI algorithms analyze claims data to identify patterns and anomalies that suggest corrective actions to be taken upstream. This feature in a reimagined claim production process will evolve constantly and move towards a zero-touch claims payment process. The adoption of AI-powered Predictive Analytics applications has significantly improved payment collections and helped healthcare organizations.
Moreover, AI and analytics have significantly reduced the time and effort required to identify errors and flag claims needing further human review. This leads to decreased rejections and denials, improving both first-pass yield and cost to collect. For instance, AI algorithms analyze claims data to identify patterns and anomalies, suggesting corrective actions. This will evolve towards a zero-touch claim payment process.
The advancement of automation across various areas of RCM will continue, especially in medical transcription, medical coding, and clinical documentation improvement. However, human involvement will remain crucial to ensure the highest quality of care and compliant reimbursement documentation.
Advantages for patients, too
In the field of patient engagement, AI-powered chatbots help patients navigate the payment process by answering their questions and providing real-time assistance. Additionally, AI and analytics provide insights into patient data, identifying patient preferences to facilitate personalized patient care and raise patient satisfaction.
In terms of patient consumerism and patient loyalty, patient behavior analytics has been linked to the patient financial journey, which relates to RCM. Advances in both patient and payer propensity-to-pay analytics and modeling have led to the development of customized patient payment plans that better align with a patient’s insurance allowances and true financial situation. An easy-to-use digital mobile experience for the patient to help navigate patient obligations also boosts patient satisfaction and, therefore, the overall Net Promoter Score (NPS) for the provider.
Conclusion
In conclusion, the role of AI and analytics in the US RCM market is significant, offering healthcare providers advantages that include higher efficiency, reduced errors, increased cash, lower unmanageable debt, and better patient satisfaction and outcomes. As the healthcare industry continues to evolve, AI and analytics are likely to play an increasingly key role in managing patient revenue and patient satisfaction from initial contact to discharge and final payment.
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