Finding the right revenue cycle benchmarks

Successful practice executives know that “practice management requires the use of subjective and objective measurement, analysis, comparison and improvement … [T]o manage something, it’s necessary to know what it is (description), where it is (comparison) and how it got there (context). This can be accomplished through measurement and benchmarking.”

Unfortunately, managers often have trouble identifying the right metrics and comparisons. This is especially difficult when evaluating a practice revenue cycle, the administrative and clinical functions that contribute to the capture, management and collection of patient service revenue.

The 2017 MGMA DataDive Cost and Revenue provides the metrics to evaluate practice financial performance, including the revenue cycle, but users still need to identify which metrics to use and the type of comparison to apply. Especially troubling for the novice manager attempting benchmarking for the first time is that revenue cycle performance differs greatly by both types of practices and ownership.

Figure 1 displays some of the revenue cycle key performance indicators for multispecialty groups with primary and specialty care, comparing performance for physician and hospital-/IDS-owned practices. There are significant differences in revenue cycle performance. Median total accounts receivable (A/R) per full-time-equivalent (FTE) physician in hospital-owned practices are 52% less than the amount reported by physician-owned groups. This difference is attributed to hospital-owned groups having 85% lower billings, or median total adjusted fee-for-service (FFS) charges.

Looking at the revenue cycle metrics, we see that hospital practices have more days adjusted FFS charges in A/R, a lower percentage for adjusted FFS collection and more bad debt than physician-owned groups. While hospital-owned practices have metrics that reflect worse performance, this does not mean these practices are not doing what they can to optimize their revenue cycle.

Figure 2 offers a more detailed look at the tangible and intangible factors that may account for these differences. The payer mix for these practices shows how hospital-/IDS-owned practices have a very different combination of payers than physician-owned groups. Hospital-owned multispecialty groups with primary and specialty care have substantially more Medicaid than similar physician-owned practices. Since Medicaid is slower to process claims than other payers in most states, this lengthens the time to collect billed claims. Similarly, having more self-pay patients both increases billing times and often results in more bad debt, while the lower percentage of commercial insurer charges in hospital-owned practices is a reason for the lower collection percentage.

While looking at payer mix helps explain some of the difference in revenue cycle performance, nontangible factors also affect the metrics. Most hospital-/IDS-owned practices have billing functions integrated in the health system’s business office, which may use billing software that is optimized to collect inpatient and facility fee billings and not the professional services billings for medical group providers. In addition, a centralized business office will have a different relationship with a practice than the in-house billing function of a physician-led group where the physician-owners have a personal connection to the revenue cycle.

Benchmarking involves identifying the right metrics and comparisons. But more importantly, benchmarking is used to assess practice performance to understand how it compares to the target and — if there is less-than-optimal performance — to evaluate the cause of the variance, assess how to correct problems and implement solutions. Benchmarking is also a recurring activity. After implementing a solution, it is important to continue to collect data, compute the comparison metrics and repeat the benchmarking process to assess improvement.

Just as a practice’s revenue cycle comprises billing and collections, benchmarking has its own cycle of performance evaluation, correction and continued measurement.