Revenue Cycle Management (RCM) is one of the drivers of the health care system, encompassing the handling of patient files from creation, to care received, to payment logged. With the ever-increasing complexity of medical billing and insurance requirements, the process can easily get bogged down. The age of Automation and AI is here with new tools to read, understand, and summarize documents, analyze trends for insurance approval and can even interface with patients. But is it secure? Are we ready to implement AI in the strictly controlled world of patient data? – The emerging answer is YES.
Is AI Safe for Patient Data?
Data security is a primary concern for any organization implementing AI tools. But with 98% of healthcare organizations planning to implement AI tools in the next 3 years, finding a way to maintain the strict standards of patient data privacy is a must.
There are two primary solutions to this problem, use private Large Language Models (LLMs) that run inside your network or work with systems that have advanced AI Trust layers like UiPath.
In our work with the US Department of Defense, our public sector team implemented an on-premises LLM in an airlocked system (no internet access) clearly locking down outside access and mitigating the data breaches that organizations fear. Some business clients of ours use a generative AI Chat with your Data solution which safely accesses outside data to augment their decisioning models. For them, we combine the data security layer that exists in automation to parse the information and redact sensitive data with an AI Trust layer before any query leaves the organization.
AI solutions for RCM can use either method or a combination of both to ensure patent data privacy while still enabling the organization with AI productivity gains without disrupting existing security and compliance mandates.
Most healthcare delivery organizations (HDOs) are attempting to manage their revenue cycle processes using legacy solutions that were purpose-built for another era. These legacy solutions hamper revenue efficiency and contribute to the technical debt CIOs must manage.
Gartner® Market Guide for Revenue Cycle Management Software, 29 January 2024
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Legacy Systems and AI Integration
Healthcare organizations, much like Banks and Insurance companies, are bound to legacy systems to ensure communication across the industry, often created in house.
Integration with legacy systems is a place where combining traditional RPA/Automation know-how with advanced AI comprehension tools shines.
Traditional RPA robots can read the user interface screens like people would and can even extract data from legacy systems that cannot be integrated through API’s. The addition of AI capabilities allows the automation to understand and interpret the information, enabling predictive analytics. Imagine a world where a legacy system can live on while being digitized; this can save costly upgrades and largescale implementation projects.
Where is Automation + AI creating the biggest gains in Revenue Cycle Management for healthcare?
Reduced cycle time, streamlined processes, and reduced manual data entry are the standard goal set of every automation project. Many organizations set out to decrease cost by outsourcing their RCM operations, but there are some areas where we are seeing real jumps in RCM with the use AI technology keeping the program in house.
Predictive Analytics for Claims Management – 90% of denials are preventable, reported Morgan Haines of Advisory Board in “An Ounce of Prevention Pays Off.” AI tools analyze historical claims data to predict the likelihood of claim denials. They use Gen AI to recommend proactive measures to prevent denials. These updates are handed off to Automation workflows that ensure timely claim submission and follow-up on unpaid claims or patient billing.
Patient Onboarding – The addition of AI readers and Document understanding automation allows handwritten intake forms, scanned insurance cards, and patient notes to be ingested and added to patient records automatically. One of our recent clients implemented automation to validate patient Insurance eligibility
AI Chat for Patient Financial Engagement – As younger generations are becoming responsible for their own and their elder’s care the preference for chatbots over traditional phone services is emerging. “Gen Z hates phone calls” claims Business Insider.
Secure gen AI chatbots connect patients directly to their account data, answering billing queries and facilitating patients to set up payment plans. These tasks currently take significant customer service agent time, representing significant savings to organizations.
How to get started with AI for RCM Automation
AI is accelerating automation improving customer experience and relieving overloaded medical staff of repetitive billing and onboarding tasks. The need is clear, but where to start and how to manage data governance is causing organizations to hesitate.
The WonderBotz team of AI Automation experts are here to help. Reach out to talk through your current needs and identify which parts of the RCM process can most benefit from implementing AI.