By Dan Schulte, MBA, CHFP, senior vice president, provider operations, HGS.
As outbreaks of COVID-19 continue to crop up around the country, the ongoing public health crisis is just one facet of the situation; economic disruption is another grim reality, including for the healthcare industry itself. The American Hospital Association estimates COVID-19 will result in losses of $202.6 billion for the country’s hospitals and health systems due to factors such as the cancellation of nonemergency procedures; the high cost of treating a patient with COVID-19; and the millions of Americans who could become suddenly uninsured due to the economic implications of the virus.
Providers must improve cash flow to remain stable, which will require new revenue cycle management strategies supported by technology. Artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA) together can provide an effective automation strategy that will help healthcare systems recover and retain more of their revenue — while boosting patient satisfaction — as they navigate this costly crisis.
Nine revenue cycle functions ripe for automation include:
- Prior authorizations: With manual prior authorizations requiring an average of 21 minutes and as much as 45 minutes per transaction, the opportunity to drive cost savings through automation is significant. Because of well-defined business rules in this area and structured data that systems exchange in conducting prior authorizations, RPA can significantly improve this process: Implementing a “bot” that can perform the same tasks repetitively and without variation can help reduce error rates, so patients can get the authorization they need quickly, and lower the likelihood of claim denials.
- Eligibility and benefit verification: While fully electronic transactions account for more than 84% of all eligibility and benefit verification transactions — a positive development — more can be done to reduce wasteful spending in this part of the revenue cycle. As the starting point for care delivery, this function represents a significant potential for improvement via intelligent automation. The focused manager will ensure that the EDI tools bring the right data across to the patient accounting system (timely, accurate and complete data), and will have the necessary add-ons to find the last 15% of data from screen scraping and outsourcing to a reliable service provider.
- Preparation of a patient presentment letter: AI and other automated tools can tap into external data from Experian, Equifax or insurance company databases to analyze whether the insurance is active relevant to the patient care being contemplated, as well as determine whether the patient can afford to pay for the care. When the provider is able to share all of a patient’s financial risks and obligations up front, with good solutions in hand (prompt payment discounts, down payment and payment arrangement strategies), the patient’s satisfaction rating improves dramatically, according to studies done by Optum, The Outsource Group, and others. In addition, the effort to collect balances at the Point of Service dramatically improves overall liquidation of patient receivablesoHoH.
- Coding assistance: Automation can help improve the timeliness and accuracy of computer-assisted coding (CAC). Smart software systems can help identify the correct code for a given presenting diagnosis, freeing up human resources from reading documents to determine diagnosis.
- Payer propensity to pay: Automation can determine how payers are performing over time by service, patient type and physician. AI can be used to analyze historical accounts from a particular environment to focus on exceptions, rather than the entire portfolio. This will free up resources to focus on the collectible accounts first, improving cash flow, accelerating the work process, and ensuring satisfactory and timely patient balance billing.
- Audits of charges: Automated tools that assist in preparing concurrent audits of the individual account also can check to ensure all associated charges are included, and no line items exist that shouldn’t be there. By filtering out this information via an automated system, time can be better spent focusing on exceptions instead of every claim. Concurrent audits can ensure that expensive carve-outs are always part of the package for specific diagnoses, including IACDs, pacemakers, and high-ticket biologicals.
- Credit balance adjustment: One of the most time-consuming and labor-intensive functions within the revenue cycle involves timely application of credit balances and adjustments to patient accounts. Automation can be deployed to help reduce compliance risk while simultaneously working to improve productivity and save significant staff time. Bots can be configured to identify the same payments from the same health plan and then process the same transaction, crediting the appropriate patient account each time.
- Claim status update: According to studies completed by HFMA (David Hammer’s excellent research), as well as research by Eide Bailley’s RCMS consultants and others in the business literature, payers deny almost 10% of insurance claims in settings where the patient accounting team is not laser-focused on managing denials. With the cost to recover these denials and underpayments approaching $120 per claim, a portion of the estimated $266 billion in annual waste can be reduced by thoughtful and well-designed application of intelligent automation. Combined with advanced analytics, technologies like AI and RPA offer effective ways to reduce denials and underpayments, and streamline the effort and resource requirements to follow up on exceptions. Paying attention here pays off: a well-run office will see initial denials drop to less than 5%, and overall loss from denials drop to less than 3%.
- Low-balance accounts: Collecting low-balance accounts (those under $2,000) through manual processes is labor intensive, with recoveries often below the cost of collecting the balance. Healthcare organizations instead can apply analytics to assess the likelihood of payment and the most effective use of staff resources to follow up on accounts that are most likely to pay sooner and at a higher rate. Additionally, automation combined with analytics can help predict when payers will satisfy a claim.
The healthcare revenue cycle is built around an abundance of tagged data in which values are codified to data points to indicate certain events, such as why a claim was denied or specific attributes of a patient’s diagnosis — making revenue cycle management an ideal application for automation. With a transformative AI strategy, healthcare organizations ensure that digital transformation will not only boost the bottom line, but also create value for their patients through faster, more accurate claims processing and reduced claims denials.
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