The Healthcare Revenue Cycle: How to Optimize Performance
Patient care tends to get top billing in the healthcare industry, but many behind-the-scenes activities make each encounter, procedure, and life saved possible. Healthcare revenue cycle management, for example, is a critical function within an organization that keeps the business operating and servings its community. The revenue cycle encompasses processes from creating a patient account to collecting payment for healthcare services and each step in between. As such, health systems rely on effective revenue cycle management to follow the patient journey, navigate claims, and ensure the organization collects payment for its services.
Revenue cycle management today is about much more than billing and collecting payment. As the healthcare industry grows, traditional, time-consuming (often manual) approaches to managing the revenue cycle require too many resources and leave unacceptably broad margins for error. Meanwhile, out-of-the-box revenue cycle solutions don’t allow customization or integrations with payer and EMR data.
The contemporary healthcare landscape requires a comprehensive, standardized, and data-driven revenue cycle process. Additionally, lost volume due to COVID-19 has furthered the need to transform revenue cycle management, as organizations can’t afford to miss an opportunity for payment.
Three Ways Data Improves Healthcare Revenue Cycle Management
Health systems that leverage data to support comprehensive and accurate revenue cycle management improve their financial outcomes in three significant ways:
Within the revenue cycle process, claim denials cost each healthcare provider an average of $5 million every year. Organizations can recover some of this loss by using an analytics solution to integrate data from multiple sources to understand the denials’ causes. With such insight, health systems can implement denial prevention plans and procedures for recovering the denials.
Billings Clinic, for example, found opportunities to reduce its denials when it pinpointed the sources of the denials. The organization leveraged the Health Catalyst Data Operating System (DOS™) platform and a robust suite of analytics applications to implement a revenue cycle analytics application. The resulting insights informed data-driven improvement efforts that have yielded positive outcomes, including a $4.5 million reduction in denials in just 12 months, resulting from an 8 percent relative reduction in overall denial dollars.
Increase Collections with Propensity-to-Pay Insight
With patients responsible for an increasing amount of their healthcare costs, self-pay accounts are now the top contributor to bad debt for hospitals and health systems. Bad debt accounts for more than $55 billion in healthcare revenue loss annually. Health systems need strategy-driven processes for patient collections—in other words, a reliable propensity-to-pay predictive model—to get ahead of bad debt.
Allina Health leveraged DOS to navigate its bad-debt challenges by creating a predictive model to support a propensity-to-pay strategy. The resulting propensity-to-pay machine learning model uses artificial intelligence to predict the probability that the patient will pay their bill during the month in question. Propensity-to-pay predictions have helped Allina increase overall collections by $2 million in just one year, including collecting more than $660,000 in additional patient payments during the strategy’s first two months.
Improve Discharged-Not-Final-Billed Efforts
Managing discharged not final billed (DNFB) cases, where bills remain incomplete due to coding or documentation gaps, is one important way hospitals can improve revenue cycle performance. However, without analytics to support efforts, meeting a target for DNFB improvement remains a serious challenge.
Thibodaux Regional Medical Center invested in analytics and resources targeted at improving its DNFB rates. The health system deployed a DNFB analytics application on top of the analytics platform (DOS) to leverage the clinical, financial, operation, claims, and other data aggregated in the analytics platform. The analytics applications enabled quick and easy access to the information Thibodaux Regional needed to effectively manage its DNFB efforts and create a more efficient workflow process for coders and physicians. Two years after the organization launched its initial DNFB improvement effort, it has achieved $1 million in additional annual reimbursement and a 66.7 percent relative reduction in DNFB dollars, significantly improving cash flow.
Data and Analytics Reveal Critical Revenue Cycle Management Opportunities
Revenue cycle management today must navigate mounting organizational complexity while maintaining financial health through emergency situations, such as COVID-19. Health systems can meet these demands with robust analytics that help them identify opportunities to improve the revue stream and data-informed strategies to sustain improvements as organizations grow and challenges emerge.
Would you like to learn more about this topic? Here are some articles we suggest:
- Healthcare Revenue Cycle: Five Keys to Financial Sustainability
- Details and Dollars: Using Data and Analytics to Optimize Revenue Cycle Performance
- VitalCDM™ Ranked #1 in KLAS Revenue Cycle, Chargemaster Management Category for Third Year in a Row
- The 2021 Healthcare Financial Forecast: What to Expect, How to Prepare
- How to Optimize the Healthcare Revenue Cycle with Improved Patient Access