How AI Can Optimize Accounts Receivable Management

The following is a guest article by Connor Accurso, Data Science Senior Product Manager at R1

The pressures on healthcare providers – from labor shortages to rising costs to reimbursement delays – continue with little relief in sight. According to an AHA and Syntellis report, half of the 1,300 hospitals and health systems surveyed reported $100 million in Accounts Receivable (AR) for claims older than 6 months. Moreover, from January 2022 to June 2023, the median health system’s drop in cash reserves was 28% and the number of days cash on hand dropped from 173 to 124 days.

The constraints on both cash flow and cash reserves can have significant impacts. Case in point: The unforeseen Change Healthcare outage left many organizations struggling to cover expenses, even payroll, when payments were held up for weeks. Even without such a disruptive event, difficulties forecasting revenue and cash flow can harm an organization’s ability to provide high-quality patient care, plan for growth, and manage costs – which can ultimately erode patient trust and satisfaction.

Healthcare organizations need to hone every aspect of AR management to prevent delays and denials and improve financial stability. AI’s ability to pull critical insights from vast datasets, specifically unstructured text, can significantly improve efficiency across AR processes and help boost cash flow.

Using AI to Improve AR Services

Many delayed claims require multiple follow-ups to resolve, resulting in numerous notes about actions taken and next steps. To work a claim and determine the next step, service agents typically review the account’s history and research multiple payer documents (clinical and administrative policies, contracts, electronic/non-electronic correspondence, etc.).

AI can improve the account triaging process by analyzing the claim history and payer documents to present the agent with a concise, accurate summary of the status, prior steps taken, and strategy for resolution. AI can do in minutes what can take an agent hours, relieving them of time-consuming, burdensome work and allowing them to apply their expertise to more quickly resolve accounts.

Applying Best Practices for Implementing AI in AR Management

Healthcare providers have a unique opportunity to adopt AI within the revenue cycle to expand automation, increase efficiency, and facilitate better collaboration between healthcare organizations and payers. Provider organizations can leverage AI across the revenue cycle to reduce manual tasks, optimize resource allocation, and improve accuracy in billing and payment. To be successful, providers need a robust implementation process that makes it a priority to:

• Enlist Stakeholders Early: To begin any AI project, an organization should first engage experts in the target area as well as cross-functional leadership; tap their expertise to map out the process from beginning to end to identify key problem areas and drive clarity around the path forward
• Pilot with Frontline Users: Next, focus on developing a minimum viable product (MVP) to pilot directly with frontline users; continuously test the AI solution with users throughout development to encounter and resolve problems early on; by obtaining user feedback regularly, organizations can more quickly scale AI solutions that are useful and accurate
• Evaluate and Optimize: Build advanced analytics to assess model performance, identify areas to improve, and evaluate operational performance metrics; with a feedback loop in place, organizations can continuously improve their AI implementation and apply their learnings to future revenue cycle problems

Improving the Patient Experience 

AI can also help improve the patient experience, especially when it comes to payment responsibilities. In a recent survey, 90% of patients surveyed said they would like to know their payment responsibility upfront, while only 20% understand what they will owe after an appointment.

Information provided by payers, such as Explanations of Benefits (EOB), may seem vague or unclear to patients, resulting in 72% of consumers reporting being confused once they receive their medical bill. When patients call with questions, AI can quickly and accurately summarize the history and status of the patient’s claim so service agents can facilitate an informed discussion about any balance owed and options for payment. Increasing clarity and efficiency throughout the process can reduce stress and improve satisfaction for patients.

Realize the Potential for AI in the Revenue Cycle 

The role of AI will continue to evolve and further reshape AR processes, driving enhanced patient experiences, improved cash flow, and stronger financial performance. AI is already transforming the AR landscape in numerous ways, setting the foundation for a more stable future in healthcare financial management. Those providers who are quick to adopt this new technology are poised to improve their experience and that of the patients they serve.

About Connor Accurso

Connor Accurso is a Senior Product Manager for Data Science at R1 where he focuses on delivering AI, machine learning, and advanced analytics solutions across R1’s portfolio of services to drive toward a faster, simpler, and more seamless revenue cycle. Connor has nearly a decade of experience in healthcare revenue cycle management and began his career at Triage Consulting Group (now part of R1) where he helped launch the company’s hospital coding, charging, and pharmaceuticals optimization products.