After a year that resulted in 72% of practices experiencing decreases in revenue, patient volumes are normalizing. MGMA recently reported that 49% of healthcare leaders say their doctors are working more hours on average in 2021 versus 2020. While full schedules are a return to “normal,” this increased workload can also produce significant strain on physicians who were already facing potential burnout prior to the pandemic. Given many practices are now working with limited staff – a result of layoffs and an unexpected surge of physician retirements in 2020 – these organizations need to identify ways to work more efficiently and cut out the repetitive tasks that eat away at their most precious resource: time.
Business responses to the pandemic exposed the need for digital solutions and automated processes in every industry – especially healthcare. Practices struggling to maximize their workforce should consider how Intelligent Automation (IA) can assist in eliminating many of the labor-intensive revenue cycle tasks that would free up employee capacity. This blog will explore what a successful IA model looks like and examine three use cases that demonstrate how IA can help practices and physician groups achieve measurable value and alleviate workforce management strains.
With so many automation solutions on the market that are designed to enhance and transform the revenue cycle, it can be overwhelming for a practice to decide which is the best fit. Many organizations may be tempted to select the lowest barrier to entry: an “off-the-shelf” solution that appears to be cost effective and promises quick returns and almost-instant productivity improvement. In R1’s experience, automation is most effective when an ecosystem of automation technologies is applied to maximize scale, impact, and customization.
R1 has developed an Intelligent Automation Ecosystem that includes robotic process automation (RPA), machine learning, natural language processing (NLP) & optical character recognition (OCR), workflow orchestration, and predictive analytics. While the various technical components may sound intimidating, the IA ecosystem is structured in a way that gives practices access to various technology solutions to be successful in automating large-scale revenue cycle processes. In this model, digital workers are responsible for executing IA activities and seamless handoffs between digital and human workers are generated through an integrated workflow platform. Since there are always exceptions and instances where human intervention is needed, the workflow platform determines which tasks should be automated and which tasks need to be handled by humans. This overall structure ensures the right skill sets and expertise are always being considered and standardizes revenue cycle work in a way that creates capacity for organizations all while cutting down on their operational expenses.
To learn more about R1's Intelligent Automation (IA) Ecosystem's scope and scale, read our Intelligent Automation in Healthcare whitepaper.
Given that IA can accelerate revenue cycle tasks completion, how should the average practice or physician think about applying IA when their focus has been on maintaining financial stability after a tumultuous year? Even if your organization's operational processes have managed to absorb the disruption of 2020 and 2021, there is a significant amount of work that will eventually (if it has not yet already) frustrate staff members. Let us evaluate how an IA ecosystem can help physicians work through three common revenue cycle issues:
Issue #1: High-volume nature of professional services and processional billing
As patient volumes pick back up, there is a direct relationship to the increase in administrative burden in claims processing. Highly transactional patient financial services such as claim status follow-up, transaction posting, document preparation & mailing (just to name a few) can quickly cause backlogs and declining revenue cycle performance. While these tasks are not complex, they require manual work and time – preventing existing staff from performing high-value activities, such as providing quality care and customer service to patients.
IA enables providers to multiply their returns. Instead of traditional RPA, or a human, doing a claim status check, an IA ecosystem can conduct a claim status check from a set of accounts that predictive analytics identified as high-risk claims, leverage expert rules (a component of machine learning) to prepare the claim status response for the next action, acquire a medical record to substantiate denial appeal, and submit the appeal packet to the payer portal – all without human intervention.
Digital workers can steadily complete this highly transactional and tightly orchestrated work effort for effectively 24 hours a day without taking incorrect actions. All this leads to lower operating costs, reduced cycle times, and, eventually, faster payments from patients. Most importantly, it enables your existing staff to reprioritize their time on patient-centric, value-generating activities.
Issue #2: Extensive documentation need to prior authorization and medical records
A long-standing burden for physicians has been collecting and submitting required documentation. This is especially true for prior authorizations which are becoming even more difficult in 2021. MGMA found that that 81% of medical groups said payer prior authorization requirements have increased since 2020 and 69% of healthcare leaders report their organization’s denials have increased in 2021 – 11.6% stating this is because of authorization/pre-certification issues. For physician groups, IA enables automated workflow, helping providers achieve authorization much faster and at a lower cost. IA notifies providers of missing documentation, creates authorizations on behalf of the practice management staff, and follows up on the status of authorization requests to confirm financial clearance is achieved prior to service. To learn more about this process, read our blog Automating Prior Authorizations Enhances the Patient Experience.
Where applicable, IA can also be utilized to proactively notify providers when required documentation is missing from a medical record. Digital workers can analyze patient data exports, create an inbound worklist that states what is missing from each record, and reference a physician roster to determine who to reach out to for more information. The IA ecosystem works to create one secure email to the appropriate physician that lists out all the missing medical record information needed for each patient to facilitate timely coding and billing.
Issue #3: Disparate technologies and EMR systems
A common point of frustration is the time spent retrieving medical records from various EMR systems for large provider networks. IA can extract medical record details from a wide array of EMR systems, organize the content (i.e., “bookmark” key components of the record), and store the record or prepare it for further processing. A benefit of IA is its customization and flexibility; in addition to gathering details, digital workers also perform different tasks within EMRs, including posting transactions, updating insurance information, confirming eligibility, and extracting data.
As many practices steadily start to return to pre-pandemic volumes, leaders should take this opportunity to evaluate where a technology-enabled strategy can be applied within the revenue cycle to maximize employee value. With the right Intelligent Automation Ecosystem and strategic partner– one that combines both IA and RCM expertise – practices will be well-positioned to capitalize as volumes rebound.
Ron has served as a Director in R1’s Digital Transformation Office since 2019 where he oversees the automation product portfolio. Prior to joining R1, Ron spent nearly ten years in management consulting, first with Navigant (now Guidehouse) and then with Deloitte Consulting. While in consulting, he advised the nation’s largest acute, specialty, and outpatient provider networks on operational transformations and business strategy.