Part 4: Analytics for Finance Leadership
June 3, 2022
This is the fourth installment of a four-part series. View Part 1 (How to Calibrate Hospital Lab Financial Performance), Part 2 (Analytics for the RCM Team), or Part 3 (Analytics for the Lab Director).
Increased staffing costs and investment losses are impeding hospital profitability. Recent financial disclosures by large health systems including Kaiser Permanente, Providence Healthcare, and Mayo Clinic are examples that clearly demonstrate the impact of these issues. Meanwhile, a recent analysis of the Consumer Assessment of Healthcare Providers and Systems Hospital Survey (HCAHPS) shows the patient experience has declined. A flourishing laboratory outreach and outpatient program can contribute to top-line and bottom-line results as well as patient satisfaction levels, with the appropriate people, processes, and technologies in place.
As discussed previously, success factors for laboratory outreach include maximizing capacity, optimizing competitive pricing, automating cumbersome and complex revenue cycle management routines, and curating and reporting data based on unique stakeholder requirements.
The relevant stakeholders referred to above include the lab director, RCM team, and finance leadership. Each plays a different role in helping achieve the same overarching objective – a successful profit center. The RCM team is fundamentally tasked with driving accounts receivable to cash. The lab director, working in concert with customer-facing teams, is responsible for running the lab as a profit center. Finance leadership tracks salient financial trends, stewards budgeting issues, and handles forecasting.
At a high level, newly developed analytics help these cross-functional teams with:
For finance specifically, following is a sampling of analyses that create needed transparency to make more informed decisions around trends, budgeting, and forecasting.
End of Month Report
This report provides a clear snapshot of the current state relative to the preceding twelve months, immediately identifying trends needing attention in the areas of volume, charges, and payments. While some may opt to view this data in a graphical format, our experience shows most finance people are accustomed to and prefer numbers.
Cash Forecasting Report
Utilizing historical performance, this report provides critical insights for planning purposes. For example, the highlighted box below makes it clear that across all payor categories, approximately 28% of payments are collected within the first 30 days. Assuming the other variables (e.g., payor mix) hold relatively constant, this data provides a reliable basis for cash forecasting. In addition, this data helps the user quantify and visualize the impact of process optimizations that can accelerate payments.
Additional Key Performance Indicators
A host of KPIs provide further insights for multiple stakeholders, including finance. These metrics are classified as “internal” data, which should be compared to historical performance only, or “external” data, which can be benchmarked against industry data provided by your RCM vendor.
“Gross collection rate” demonstrates payments as a percentage of billed charges. A downward trend here should be flagged for investigation. Because this is a function of your unique chargemaster, payor fee schedule, test mix, and payor mix, it should be measured against historical performance only. Similarly useful internal data points include units per accession, billed charges per accession or unit, and collections per accession or unit.
“Net collection rate” and “Days in AR” are useful data points that can be benchmarked externally, because they do not reflect unique impacts such as payor contractual adjustments.
The following table illustrates the utility of some of these KPIs. In this example, the collections per accession fell noticeably in months 10 and 11 of 2021. Ostensibly, this is a red flag, possibly indicating an RCM issue. Upon further examination, however, we see that charges per accession for the preceding few months are significantly lower. By getting a more complete picture for root cause analysis, it becomes clear that the test mix has likely changed. Meaning, there may actually be a problem with referrals.
Recommended Resource:
XiFin RPM Advanced Analytics
Learn more about the operational and financial analytics available through Tableau-driven workbooks, specifically designed for C-level executives, finance, billing and revenue cycle leaders, and departmental managers.