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How Intelligent Automation is Reshaping Revenue Cycle Management

John Meier

Manager, Healthcare Business Consulting

Shreya Desai

Manager, Business Consulting
Blog
  • Life Sciences & Healthcare

A provider’s revenue cycle efficacy is a key determinant of the viability of an organization, and revenue cycle management (RCM) is an immensely important, yet particularly challenging area in the healthcare industry. Due to complex regulations and rules, the revenue cycle often contains inefficient and error-prone manual processes that can have a wide-ranging impact on the financial performance of an organization.

Many of these RCM processes are time consuming and repetitive, making them an excellent candidate for intelligent automation (IA). Most non-clinical KPIs are largely dependent on revenue cycle activities and are manually performed with little oversight. When executed properly, IA can enable organizations to streamline these tasks while increasing billing cycle efficiency, reducing costly mistakes resulting in denials and improving financial performance.

Benefits to Adopting Automation
The Front-End – Challenges & Opportunities

Often, front-end revenue cycle tasks (tasks that take place before care delivery) lay the groundwork for a clean claim. IA can streamline the front-end tasks of the revenue cycle, including registration, patient data collection, eligibility and authorization, and the calculation, communication and collection of the patient’s portion of the provider’s bill.

Registration

Claims with inaccurate patient, provider or payor data are a common, but potentially avoidable issue that continues to impact revenue cycle performance. Any discrepancy (incorrect name, DOB, provider information, policy number, etc.) between an 837 claim form and a payor’s database will be returned for correction. Claim rejection delays reimbursement and increases the amount of time spent in accounts receivable.

IA can alleviate data integrity concerns during front-end revenue cycle processes. For example, intelligent document processing (IDP) allows a patient to utilize a smart phone or scanner to upload a government issued ID and medical card directly into the patient portal to accurately and easily share information with a provider. Robotic process automation (RPA) bots can then extract the necessary information (demographics, policy number, insurance carrier, etc.) and populate data into the electronic medical record (EMR). This functionality enables health systems and provider groups to validate patient demographics and insurance through real-time eligibility, payor websites and address verification tools prior to patient arrival. Any inconsistencies can be flagged with a rules-based engine accompanied by machine learning and sent to a queue for follow-up before the patient receives care. This maximizes staff time by only identifying the discrepancies that require attention while improving data accuracy, leading to fewer rejections. Additionally, patients will have greater transparency into what medical services are not covered under insurance so they can be better prepared.

Prior Authorization

Prior authorization is a critical step to receiving payment and is required before care begins to determine if the prescribed procedure, service or medication is covered under the patient’s benefits and meets medical necessity. According to a survey completed by the American Medical Association (AMA), approximately 92% of care delays are associated with prior authorization issues. The AMA also reports that 64% of providers reported waiting at least one business day for a prior authorization request and 30% waited at least three business days. Moreover, 78% of providers reported that these delays led to patients abandoning their treatments.

To address these challenges that adversely impact patient experience and even health outcomes, RPA bots can leverage APIs to extract necessary data and initiate the prior authorization process when a request enters the work queue of an EMR. The bot starts by sourcing patient information from the EMR, and then retrieves all documents for procedural and care plan information. IDP (optical character recognition-based intelligent document processing) is then used to extract the relevant information from the document and forms. Once all data has been gathered, the bot submits the request through the appropriate channel established by the payor. When the insurer issues a response, the bot alerts a staff member of the results via an email or text alert through conversational agents (chatbots with speed recognition/generation technologies like Alexa or Google Assist). If the prior authorization request is approved, the bot moves on to registration and/or scheduling or is routed for additional follow-up. This low touch or no touch automated process will help remove the administrative burden of authorization and allow staff to focus on patient-centric activities. Furthermore, faster turnaround time improves patient adherence and increases the probability of payment, as an audit trail of approval from the payor has been established by the provider.

Cost Estimation

According to the Centers for Disease Control (CDC), from 2007 to 2017, the number of patients aged 18-64 with employer-sponsored coverage enrolled in an HDHP with an HSA increased from 4.2% to 18.9%. Patients without an HSA increased from 10.6% to 24.5%. An HDHP exposes the patient to greater financial risk, particularly those without the benefit of tax-free HSA funds. As healthcare organizations become more reliant on the patient as a source of revenue, they must adapt and develop new strategies for calculating, communicating and collecting the patient’s portion of the bill.

Providing patients with cost estimates and initiating face-to-face conversations regarding their financial responsibility can improve POS conversions. However, this process requires multiple sources of information, is time consuming and distracts from patient-focused engagement. Through IDP, bots can read and reference insurance contracts, as well as collate the data required to determine the financial liability for the patient’s care. When the bot is presented with the patient’s procedural information, it can cross reference the associated price, factor in the insurance plan and calculate a cost estimate. Key information related to health coverage, deductible and co-payment responsibilities can then be shared with the patient in two ways: by uploading the cost calculation directly to their patient portal or sending the information to the appropriate nurse or physician to discuss payment responsibilities with the patient. This proactive approach to patient financial services will help the patient better understand the incurred cost and reduce “surprise” billing issues. Additionally, this process saves the patient time conversing with the provider for a cost estimate.

Conclusion

The front-end of the revenue cycle contains a series of complicated processes that are crucial for collecting revenue. Many providers are experiencing the downstream impact caused by errors and inefficiencies in the front-end. By effectively integrating IA, organizations can create a proactive safeguard to reduce errors, improve efficiency and increase cash collection. Embracing this technology by overhauling the revenue cycle can have a significant financial impact for providers.

In the next part of our series, we will dive into the back-end of the revenue cycle, uncover existing pain points and the technology, tools and improved processes available to help address these issues.  

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