AI (Artificial Intelligence) is already prime time in clinical and administrative functions in the healthcare industry.
According to this NY Times article, doctors are even impacted by AI! Some on the clinical side may argue with this, but when it comes to the revenue cycle, AI is ideally suited to answer some of RCM’s biggest challenges, and it can be implemented right away.
Any healthcare decision-maker who is excited about AI will be smart to look for the simplest, lowest-risk, highest return use cases. For most healthcare organizations, revenue cycle is the perfect candidate. That’s because from EOBs to chargemasters, to patient payment histories, the revenue cycle is rich in historical transactions between providers, patients, payors, and banks — the perfect conditions for machines to learn from this history and improve processes to yield high results.
Even if clinical discussions of AI are partially hype, revenue cycle is the real deal. Let’s take a look at a few examples:
Insurance claims cost hospitals approximately $262 billion annually, and this total doesn’t include unnecessary processing costs to insurers and intermediaries. Since many of the root causes of denials are founded in humans making errors while managing large volumes of transactions, AI has massive potential to enhance denial intelligence, improve your cash position, and contribute to the long-term health of your organization.
AI can be leveraged pre-submission to identify reasons for denial and give recommendations, such as filling in missing fields or addressing authorization issues — enabling prediction, reducing denial rates, driving down receivables, and boosting reimbursement.
Next generation revenue cycle management will rely on business intelligence that goes beyond advanced analytics; it will leverage AI to empower staff efficiency for providers, payors, and intermediaries using Big Data.
AI can look at historical data around scheduling, registration, charge capture, billing, and collections to reduce cost to collect and improve work processes. This results in increased resource specialization, improved efficiency, and less effort in managing remote and offshore workforces. For example, Hitachi, a business outside healthcare, saw an 8% improvement in work after implementing an AI-driven work solution.
Leveraging AI in Image Processing and Data Extraction
Neural networks have been used for image recognition and extraction for years. There has also been many advancements with more advanced levels of machine learning, along with cognitive neural networks.
This is especially true now as RCM stakeholders strive for full electronification of payments. For example, although EDI 837 and 835’s are the desired payment and remittance vehicles to the industry, paper is still a major player in the process. A modernization approach to payment automation streamlines the use of EOB, check payments, correspondence letters, and reconciliation operations which enables complete payment and remittance electronification. The result: faster cash posting, fewer errors, efficient identification of actionable trends, and improved decision-making — all using existing infrastructure.
In the world of image processing, there’s real opportunity to dig deeper than a cursory view of AI and dive into deep learning. This shift will become essential to support organizational resilience and identifying new efficiencies in a revenue cycle ecosystem with increasingly complex payment mixes and patterns of data.
AI in revenue cycle has moved beyond inflated expectations. It’s at a level of productivity where it should be a norm for any organization that’s invested in modernizing its platforms to fit the challenges and opportunities of the modern revenue cycle. AI will be the foundation of marrying clinical data with administrative and payment data, and creating true organizational alignment and long-term health.
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Blog authored by OrboGraph Innovations Team.