AI Healthcare Reinvention
AI promises to reinvent many of the processes within healthcare. Revenue cycle management, in particular, has many areas in which we see fascinating advantages, ultimately improving the overall patient experience.
In a revenue cycle context, we’re looking at benefits that rest heavily on machine learning, deep learning, analytics, and natural language processing (NLP) allowing healthcare providers and revenue cycle servicers to leverage structured and unstructured data (such as paper and PDF EOBs/EOPs, coding, and clinical documentation). By doing so, these documents become strategic assets in building a straight-through revenue cycle, reducing costs, increasing efficiency, and delivering a patient payment experience without errors and with faster billing.
OrboGraph recently highlighted a Becker’s Healthcare Article in our OrboNation Blog on 5 Thoughts on AI Transforming RCM. Here are a few of the applications worth paying attention to:
Denials Prediction and Analysis
Getting ahead of the denial curve takes a proactive approach and AI is ideally positioned to support your efforts. The use of business intelligence is a powerful tool to visualize historic denied claim trends. However, machine learning, a subset of AI, can enable hospitals and health systems to more quickly identify root causes and minimize claim denials earlier in the revenue cycle process. This results in lower denial rates and increased revenue.
Denied Claim Documentation
Leveraging AI for image processing will automate the processing of correspondence letters. Hospitals lose over $260 billion every year from insurance denials. Part of this is because of shifting payer guidelines, but a large part is also because of human error. With the average cost of reworking a claim coming in at $25 (according to HFMA) and as many as 65 percent of denials never being reworked (according to MGMA), there is huge potential for cost savings here. Imagine receiving a denial letter and having it flow automatically into your denial management system with all data extracted and queued up for collections.
Cutting Collection Costs
Billing and collections benefit from AI. As Crowe Horwath predicts, healthcare organizations can expect to see a decrease of between 25% - 50% in their cost to collect by implementing AI and automation in their revenue cycle processes and can anticipate more accurate billing statements.
Improving Coding and Clinical Documentation
AI also holds serious potential for improving revenue cycle inputs. Deep learning and NLP allow for the processing and analysis of unstructured data, including free-text physician notes in an EHR. This, in turn, feeds higher quality and more accurate data into your revenue cycle, reducing the need for claim re-work and additional manual input in the process.