Part 2: AI Revolutionizing Healthcare's Revenue Cycle Management (RCM)

The revenue cycle in healthcare encompasses all financial processes related to patient care, from scheduling and insurance verification to billing and collections. Historically, these processes have been labor-intensive and error-prone, leading to increased costs and delayed reimbursements. Artificial Intelligence (AI) presents an opportunity to streamline these operations, reduce administrative burdens, and enhance financial outcomes for healthcare providers.

Barrier 1: Manual and Inefficient Prior Authorization Processes

Challenge: Prior authorization requires providers to obtain approval from payers before delivering certain services. Traditional methods involve time-consuming phone calls, faxes, or even emails that can and often do lead to delays in patient care.

AI Solution: AI-driven tools can automate the submission and tracking of authorization requests, predict approval likelihood, and flag potential issues, significantly reducing turnaround times.

Real-World Example: Allegheny Health Network partnered with Humata Health to implement an AI-powered touchless prior authorization system, achieving a first-pass approval rate of 96% across over 200,000 authorizations annually. Business Wire

Barrier 2: Paper-Based Documentation and Data Entry Errors

Challenge: Healthcare organizations handle vast amounts of paperwork, leading to inefficiencies and errors in data entry.

AI Solution: AI-powered Optical Character Recognition (OCR) technology converts various documents into editable and searchable data, facilitating quicker data entry and enhancing accuracy.

Real-World Example: Google Cloud's OCR solutions enable the extraction of text and data from images and documents, turning unstructured content into structured data for better insights. Goo

gle Cloud

Barrier 3: High Rates of Claim Denials

Challenge: Claim denials are a significant pain point, often resulting from errors or missing information, leading to revenue loss and administrative burdens.

AI Solution: AI technologies can analyze patterns in denied claims, identify root causes, and predict which claims are at risk of denial, allowing proactive measures to improve acceptance rates.

Real-World Example: Waystar's AI applications in denial management have demonstrated effectiveness in decreasing manual workloads and expediting approvals, thereby improving provider efficiency and patient satisfaction. Targeted Oncolog

y

Barrier 4: Utilization Management Complexities

Challenge: Ensuring that healthcare services are used appropriately and efficiently is critical, but traditional utilization management processes can be cumbersome and slow.

AI Solution: AI enhances utilization management by analyzing patient data to support clinical decision-making, ensuring treatments align with best practices and are necessary.

Real-World Example: Xsolis' AI-driven utilization management solutions have delivered up to 83% time savings, enabling real-time, predictive analytics to tackle revenue and staffing challenges. Xsolis

Barrier 5: Time-Consuming Credentialing Processes

Challenge: Credentialing, the process of verifying healthcare providers' qualifications, is traditionally time-consuming and prone to delays.

AI Solution: Automated credentialing systems use AI to verify credentials against databases, reducing onboarding time and ensuring compliance with regulatory standards.

Real-World Example: Verisys offers automated credentialing solutions that streamline the verification process, enhancing efficiency and accuracy in maintaining provider credentials. Verisys

Information Blocking and Regulatory Challenges

There has been a visible increase in lawsuits with EMR providers around the blocking of API’s, lack of interoperability, robotic process automation (RPA), etc. There are also lawsuits against some of the biggest insurers thanks to AI aided denial management software. We’re seeing these AI tools fight with other AI

assisted insurance algorithms. The Guardian

AI holds promise for streamlining revenue cycle operations but it's crucial to acknowledge the systemic challenges that persist, particularly those stemming from payor practices. These practices are often designed to control payor costs by adding burdens to providers via additional administrative tasks which can both delay patient care and add financial strain with missed items that now produce a denial.

Regulatory initiatives like the TEFCA aim to foster better data sharing between payors and providers, potentially reducing administrative burdens. However that could end up being a potential double-edge sword. As data sharing becomes more seamless, there's a concern that payors will leverage this increased access to data to develop more stringent policies, possibly leading to higher denial rates.

Conclusion

The integration of AI into these backend processes is not just theoretical anymore. Healthcare organizations are implementing these technologies and report significant improvements. Childrens Cincinnati is already reporting a 98% clean claim rate with the help of Waystar. Waystar

While the cost and potential ROI from AI is certainly a very attractive selling point, AI integration will require careful planning, staff training, system interoperability, and strict adherence to all data privacy regulations.

It’s not a silver bullet by itself, and healthcare leaders must navigate these challenges thoughtfully to fully realize AI's benefits.

HI Resolution Consulting stands ready to help.