System conversions are complex, high-stakes processes. Many organizations embarking on conversions understandably focus first on keeping their heads above water in the short term, putting off optimization until they feel comfortable with the new system and associated workflow—a learning curve that typically takes several months.
With organizations improving every part of the electronic health record (EHR) conversion from pre-live training and communication to post-live revenue capture and billing throughput, moving the post-live optimization phase earlier in the process can give health systems a distinct competitive edge.
Today, a growing number of health systems are getting back to baseline in important metrics in a matter of 90 days after implementing a new EHR system. After that, however, most organizations spend a few months observing how things play out before launching serious optimization efforts. The top performers, on the other hand, plan for optimization before they implement, giving them an opportunity to move seamlessly from stabilization to optimization.
One of these top performers is Michigan Medicine (known as University of Michigan Health System until early 2017), an academic medical center and multisite health system that handles more than 2.4 million outpatient and emergency visits, 48,000 hospital stays, 54,000 surgeries, and 4,400 births through its hospitals and outpatient facilities in southeast Michigan. Michigan Medicine implemented its EHR system in February 2012 and later that year performed a revenue cycle assessment and post-live optimization. The comprehensive revenue cycle assessment uncovered risks in areas such as self-pay collections, call abandonment rates, underpayment recovery, and denial management. Thus, along with the discharged-not-final-billed metric (widely acknowledged as one of the most pressing points of focus after a conversion), Michigan targeted specific metrics related to those areas and went after them immediately. Pushing forward when many health systems would choose to “wait and see,” Michigan achieved immediate and continuous improvement.
Leading Indicators Versus the Big Picture
Health systems know that getting back to baseline after a conversion is a major milestone. Typically, the organizations that reach that milestone quickly are the ones that think more holistically about their practice—the ones that pay attention not only to charging and billing metrics like claims and gross revenue, but also to the factors that dictate net revenue (such as denials, write-offs, and yields). After getting back to baseline performance, the top performers know which metrics will help them enhance their net revenue and cash collections—the point of investing in new technology in the first place.
One health system that took the “big picture” approach to an EHR conversion is St. Luke’s University Health Network (SLUHN), a regional health network that operates seven hospitals and over 200 clinics and other healthcare sites across Pennsylvania and New Jersey. After deciding in 2014 to convert the EHR at six of its hospitals, SLUHN devised a plan for implementation and optimization that focused both on key indicators and on the next steps after those goals were met. The plan established specific revenue cycle performance goals that would define success within 60 days of conversion (including gross revenue capture, unbilled claims, and cash collections) and assigned individual committees to provide training, reporting, and support for the clinical and operational end users who would directly impact these metrics. These committees were supported by an extensive and highly involved leadership governance structure that could address and resolve challenges as they arose. Ultimately, SLUHN achieved 104 percent of preconversion baseline gross revenue within four weeks of implementation—and shortly afterward reached 106 percent over baseline, a percentage the organization has maintained since.
The next steps for SLUHN included leveraging new functionality to reduce claim denials and increase patient point-of-service collections—two net revenue improvement goals that helped offset some of the investment costs associated with the implementation. These initiatives were spearheaded by a multidisciplinary team, led by the revenue cycle department, which designed and implemented improvements addressing the top denial reasons and trends.
Overall, the team achieved a significant reduction in bad-debt write-offs (approximately 0.5 percent of annualized net patient revenue). Patient collections at the time of service also increased by approximately $2.1 million during a six-month period, supporting revenue cycle goals to increase preservice financial clearance and enhance the patient experience.
Key Benchmarks: A Snapshot
The exhibit above lists the key benchmarks that hospitals and health systems should use to guide their revenue cycle structure alignment in the pre- and post-live phases of an EHR implementation. With every cautionary tale, the collective wisdom of such organizations undergoing conversions to new EHR systems has grown. Those that plan for early optimization can find themselves in a better position for success.