WhiteSpace Health Blogs

Introducing Intelligence to the Revenue Cycle

Data’s Vast Untapped Opportunity

by Gautam Char

Healthcare and revenue cycle data has been collected in numerous systems for decades. As an industry, we have created vast data silos and burdens on clinicians with the intricacies of all this documentation. And for what? This data is often not combined or normalized into a useful dataset that creates a holistic picture of patient and financial care. With all the technology at our disposal and all the highly educated staff contributing to this body of knowledge, we are missing a tremendous opportunity to advance healthcare and the revenue cycle that supports it.

The challenge is using these resources to answer important questions, discover, and capitalize on important trends and address issues even before they are identified. The very data that is vexing us at present should be used to make informed decisions that allow us to react and improve healthcare and its revenue cycle.

Evolution of Data Governance

Historically, revenue cycle leaders built KPI dashboards. First, they were completed on paper forms. Gradually, they moved to Excel spreadsheets and then to data rendering tools that evangelized their “drill down” capabilities. Why were we so excited about hunting through data just to figure out what needs to be done? Even when individual issues were found through investigation, these homegrown reporting tools were incapable of prioritizing the issues that had the largest financial impact. They were also unable to identify patterns in the data and use those patterns to become more proactive. Some organizations had better luck than others with the drill down approach and I suspect it all comes down to people. This manual effort was only as good as the investigative skills of those who sifted through the data – and the quality of the data certainly was a key factor too. Regardless of ability, everybody was spending far too much time identifying areas of revenue leakage – and even more time prioritizing work and figuring what to do about each problem.

AI and Alerts

Now that artificial intelligence (AI for short) is advancing into the revenue cycle, we are poised to use our systems efficiently as they swiftly cull through vast amounts of data. While alerting functionality has been available for decades, it was not as intelligent as it needed to be. Numerous articles and clinical outcries related to alert fatigue can be found in any simple Google search. Through the use of AI in the revenue cycle, we now know which workstreams are under performing and the precise issues that need immediate attention. When modern alerts fire, the platform lets users know it has identified a financial opportunity. In addition to firing the alert, AI automatically populates work queues and prioritizes them according to impact on revenue. The very alerts that used to be so tiresome are now intelligently governed by AI and the thresholds each organization sets. Staff can now rely on AI-generated alerts with proposed solutions to resolve rather than be annoyed by them, transforming the entire tightly managed. Clinical outcomes must be aligned with patient outcomes, value-based reimbursements, and bonuses. Also, provider time must be optimally utilized.

The AI Imperative

Over time, the outcomes get smarter using machine learning (ML). In tracking performance, ML learns how to accurately predict future performance and likely opportunities based on history and deep learning. This gives us valuable context. For example, ML can help predict a future cash position and opportunities that can be implemented to improve. It can also help us identify and resolve revenue cycle challenges: AR global issues, coding errors, denials prior to adjudication by provider, location, specialty, etc. And, by incorporating scheduling information, we can address more vexing issues such as reducing patient no-shows, predicting which patients will cancel or re-schedule their appointments, and the lack of revenue that results with workflows that affect the revenue cycletightly managed. Clinical outcomes must be aligned with patient outcomes, value-based reimbursements, and bonuses. Also, provider time must be optimally utilized.

Incorporating RCM Expertise into AI

WhiteSpace Health is chock full of revenue cycle, HIM, healthcare IT, and data science experts. Our team has deep expertise from years spent running revenue cycle and HIM departments in a number of healthcare organizations ranging from community hospitals to complex academic medical centers with large physician practice affiliations. We have run billing companies and large RCM outsourcing organizations. We have used this combined experience (and the headaches we used to have when we walked in your shoes) to create a platform that uses deep analytics. We take in your data from its many disparate sources to create a world class normalized dataset. Each day our Revenue Intelligence Platform performs a “data sync” to ensure you always have fresh data for management and decision making. Our platform applies AI to this data and creates automated insights that identify which workstreams are healthy and needing immediate attention.

A New Level of Performance with ML

The Revenue Intelligence Platform also leverages ML to predict the best courses of action – those that have the highest probability of resolving your RCM issues. And ML continues to look for complex patterns that are far more sophisticated than humans can comprehend. The insight generated from the platform spawns intelligent action from your revenue cycle management (RCM) team. This intelligence informs both strategic and tactical efforts. From work queue prioritization of high dollar claims, the use of AL and ML on other areas of the revenue cycle is transformative. Together, the Revenue Intelligence Platform provides the best intelligence available so you and your team can work as efficiently as possible, improving and sustaining your cash performance.

RCM is Becoming Prospective

Historically, RCM management has been retrospective – and very reactionary – for all participants in the process. We have all reacted to DRG groupers, claims scrubbers, claims kicked back by clearinghouses, payer denials, and non-payment by patients. Each acts as a speed bump to getting paid and adds friction to the revenue cycle, slowing down or even eliminating cash collections in some cases. As datasets get better and as data science models become more sophisticated, we will benefit from more accurate forecasting of cash position, denials, and other key RCM workstreams.

The WhiteSpace Health platform is already able to identify potential denials by payers, providers, locations and more based on historical performance of payers – even before the patient enters your doors for treatment. This incredible new level of intelligence and predictive capabilities will allow patient access and other RCM related teams to discover issues earlier when they are most addressable. The goal is to find and resolve these issues as early as possible, and proactively manage the revenue cycle before care is rendered and the initial claim is dropped.

About Gautam Char