Many healthcare organizations have recently implemented a patient accounting system (PAS) conversion or are currently in the planning process for a conversion. These systems, which are sometimes implemented as distinct stand-alone packages (for example, “financials only”) and sometimes as components of an electronic medical record system, are popular solutions for driving efficiency and creating value. Despite the long-term benefits of these conversions, including the integration of clinical, operational, and financial departments and processes, there can be significant short-term and midterm financial and operational impacts. The effects of conversion on cash and revenue cycle key performance indicators (KPIs) can vary widely among hospitals. The large variance in performance highlights the need for hospitals to use reporting and analytics to enable root cause analysis to identify issues and improve performance.
The ability to quickly conduct an in-depth exploration into KPIs affected by a PAS conversion and understand exactly what’s driving poor performance can be crucial to a successful conversion. Providers also are realizing that this level of reporting and analysis can be critical in optimizing financial and revenue cycle performance, whether or not they are implementing a system conversion.
Analytics: Identifying Problems
When undergoing a system conversion, organizations may experience disruption to several areas of the revenue cycle, including discharged not final billed (DNFB) days, late charges, and denials. The Crowe Revenue Cycle Analytics benchmarking report, “Patient Accounting System Conversions: Challenges for Cash Collections and Revenue Cycle Performance,” studied these performance indicators using transactional-level data from the patient accounting systems of 580 hospitals. Eighty-three acute care hospital facilities in the analysis completed a PAS conversion during the previous three years, and 32 of these facilities generated sufficient data to yield statistically relevant results in an analysis of pre- and post-conversion cash flow and a range of KPIs. The hospitals had an average bed count of 225 with an average baseline monthly cash collection of $15.2 million.
Overall, the analysis revealed dramatic effects on most of the facilities’ revenue cycles occurring during and in the months immediately following the PAS conversion. Particular disruption was seen in billing and coding. For example, the analysis showed that DNFB days were up 86 percent during the month of conversion, to more than 14 days, and late charges rose by more than 600 percent to 38 percent of charges. In addition, cash collections during conversion were severely compromised. During the conversion month, collections dropped more than 5 percent below baseline, and collections continued to decline in the month post-conversion to nearly 21 percent below baseline.
These findings highlight the importance of staff being able to quickly identify issues that can surface during a PAS implementation. Doing so can help mitigate disruptions and reduce risk to cash collections and revenue cycle performance following a PAS conversion.
Providers are looking for benchmarking comparison and analysis to help them identify ways to optimize performance. The data collected from benchmarking highlights issues in the revenue cycle, provides insight into what is causing those issues, and helps identify opportunities for improvement. Healthcare organizations can expect to find value in this type of analysis regardless of whether they’ve been through or are planning a PAS conversion. Actionable, timely business intelligence can help stakeholders drive decision-making throughout their organization.
One issue of top concern for many healthcare organizations is the shifting of financial risk from managed care payers to the patient, often in the form of high-deductible health plans (HDHPs). This trend is becoming more prevalent in the healthcare industry and is expected to continue. According to a September 2015 Kaiser Family Foundation report on employer-sponsored health plans, in 2015, nearly one-quarter of workers were enrolled in HDHPs compared to in 2006, when only 4 percent of workers were in such plans.1 The rise in HDHPs can be seen in the individual marketplace, too: Nearly 90 percent of enrollees in the Affordable Care Act’s health insurance exchanges were enrolled in HDHPs in 2015, according to a February 2016 policy brief by “Health Affairs” and the Robert Wood Johnson Foundation.2
In order to respond to this fundamental shift in the healthcare industry, organizations need to be able to understand the issue from a variety of standpoints. Providers should be aware of how they compare with similar organizations on benchmarks related to this shift, including rates of self-pay after insurance collection and point-of-service collections as a percentage of self-pay after insurance responsibility. Providers should want to explore how they can improve rates of self-pay after insurance collection and also understand how this collection rate affects managed care realization down to the plan level.
A third-party benchmarking solution that uses PAS and 835 electronic remittance data, linked at the account level, can help organizations to analyze collections on self-pay after insurance co-pays and deductibles, and it may help them to understand how payments vary by patient type, payer, and other components. A root cause analysis can unveil opportunities for improvement if the organization isn’t performing up to the level of industry peer groups overall or in a specific area such as emergency services.
Having a process in place to access accurate data and perform root cause analysis on KPI trends within that data is critical to a healthcare organization’s success when undergoing a PAS conversion. In addition, evaluating peer benchmarking data that has been verified can help organizations find ways to optimize their performance – during a PAS conversion and beyond.
The Crowe Revenue Cycle Analytics (Crowe RCA) solution was invented by Derek Bang of Crowe. The Crowe RCA solution is covered by U.S. Patent number 8,301,519.
1 “2015 Employer Health Benefits Survey,” Kaiser Family Foundation and Health Research & Educational Trust, September 2015, http://kff.org/health-costs/report/2015-employer-health-benefits-survey/
2 “Health Policy Briefs: High-Deductible Health Plans,” Health rel="noopener noreferrer" Affairs, Feb. 4, 2016, http://www.healthaffairs.org/healthpolicybriefs/brief.php?brief_id=152