Developing highly efficient and effective revenue cycle management (RCM) processes is one of the fastest routes to financial improvement for laboratories. But, how do you know if your processes are effective for capturing the revenue you have earned? While there are several benchmarks that can provide value to your ongoing monitoring program, there are three key metrics your organization should monitor consistently for evaluating the effectiveness of your RCM processes.
The first three metrics must be monitored as a group, NOT individually.
Metric 1: Net Collection Rate (%)
Net Collection Rate indicates how much of what you are eligible to collect is actually being collected. As such, the higher the percentage, the better the Net Collections Rate.
Net Collection Rate is calculated by dividing payments ($) by allowed charges ($). Allowed charges include the portion of gross, or billed, charges that the payors contractually allow the entity (in this case a laboratory or other diagnostics provider) to attempt to collect. It is calculated as billed charges ($) less contractual adjustments ($). Contractual adjustments should not include any other write-offs, as these are more appropriately classified as bad debt.
Net Collection Rate is an external benchmark that can be compared across groups or segments and can be affected by demographic differences in the patient population.
Metric 2: Bad Debt Rate (%)
Bad Debt Rate indicates how much of what you are eligible to collect is being written off or turned to a collection agency.
Bad Debt Rate is calculated by dividing bad debt write-offs ($) by allowed charges ($). As with Net Collection Rate, allowed charges include the portion of gross, or billed, charges that the payors contractually allow the entity to attempt to collect. It is calculated as billed charges ($) less contractual adjustments ($).
As with Net Collections Rate, Bad Debt Rate is a benchmark that can be compared across groups or segments. However, it too can be affected by demographic differences in the patient population.
In clinical laboratories with billing performed by a hospital billing team, the Bad Debt Rate may by higher than expected due to de minimus write-off policies employed at many hospital and health systems to systematically adjust unpaid balances below a certain amount. This often impacts the ability to effectively collect on the high volume/low dollar services performed in a hospital outreach laboratory. This is usually done because the labor to collect on these claims outweighs the cash collected potential. However, hospital outreach labs that are managed as profit centers and cost reduction engines leverage RCM solutions specifically designed to process high volume and low dollar claims with intelligent automation. Smaller dollar value claims can be collected effectively, which means fewer write-offs and a lower bad debt rate.
Metric 3: Days in Accounts Receivable (AR) (#)
Days in AR indicates how many days it takes for a Billed Charge to be fully adjudicated to a zero balance. In other words, how long does it take for you to be fully paid for services rendered?
Days in AR is calculated by dividing ending AR ($) by average daily charge ($). The industry standard for calculating the average daily charge is to sum the prior three months’ billed charges and divide that total by 91 days. However, watch out for fluctuations in billed charges that could artificially influence this metric, and ensure the average daily charge being utilized appropriately reflects your business.
For fully adjudicated claims (claims with a zero balance), Net Collection Rate + Bad Debt Rate as described above, should equal 100% of allowed charges. Industry benchmarks indicate that high level, national, targets for these metrics would be 90% Net Collection Rate and 10% Bad Debt Rate. That said, the demographics of your market will influence these metrics. In a rural area with a high Medicaid and/or uninsured population, a Net Collection Rate of 80-85%, with a corresponding Bad Debt Rate of 15-20% may be acceptable. Conversely, in an area with very low unemployment and a smaller population of Medicaid and uninsured patients, a Net Collection Rate of 95% with a Bad Debt Rate of only 5% may be achievable. It’s important to understand the demographics of your market and the impact they can have on your metrics.
These three metrics should always be considered as a group. The risk is that individually the metrics could be inappropriately skewed. For example:
Inappropriately classifying a bad debt as a contractual adjustment would decrease your allowed charges, therefore artificially inflating the Net Collection Rate and conversely deflating the Bad Debt Rate. Ensure that your write-offs are appropriately categorized and applied consistently for the most effective evaluation of these metrics.
An RCM team incentivized to maintain a Days in AR less than a certain amount may be motivated to adjust aged AR balances without adequate follow-up and appeal. This can cause a lower, more favorable, Days in AR outcome, but can artificially inflate Net Collection Rate (if written off as contractual adjustments) or Bad Debt Rate (if written off as bad debt). Even more important is that this practice may negatively affect your overall cash flow. When total AR or Days in AR decrease drastically from one period to another, it is important to determine the cause of decrease: increased payments, increased contractual adjustments, or increased bad debt.
Monitoring as a group will help you better understand the effectiveness of your program in a way that looking at one or two metrics independently will not.
In addition to the key three metrics above, some other interesting measurements to consider include:
Gross Collection Rate (%)
Gross Collection Rate is calculated by dividing payments ($) by billed charges ($). Gross Collection Rate is an “internal benchmark” and should not be compared across entities. Instead, this metric is most useful when monitored for fluctuations over time, and can be used to help predict future cash flows. However, to truly understand this metric and related changes that occur, it is vital that you understand the key drivers that influence this metric:
- Changes in charge master/fee schedule
- Changes in CPT mix
- Changes in payor mix
Denial Rate (%)
Denial Rate is calculated by dividing charge dollars denied ($) by billed charges ($) or by dividing units denied (#) by billed units (#). A high denial rate or an increasing trend in denial rate may indicate there are issues that could be addressed and fixed on the front end of the RCM process to improve clean claim submission, thus reducing denial rate. This metric may vary for multiple reasons, but we recommend tracking it, as improving visibility into denials is the best way to effectively work toward avoiding denials and improving cash flow.
Percentage of unbilled (unclean) claims (%)
Percentage of unbilled claims is the percentage of claims received from hospital LIS/EMR with unclean data delaying the billing of the claims. Examples include, incorrect demographic information, missing or invalid DX codes, medical necessity edits, CCI edits, missing invalid ordering physician errors. This percentage is often high for health systems and more often than not these claims are written-off without being billed. It is imperative that the lab have a system that can manage these front-end edits efficiently and quickly to ensure turn around for billing. This information assists in training the staff entering the data as well.
Average and median price per accession ($)
The average charge per accession or test is important to understand and forecast costs. This price is used to calculate costs, profit, commission, etc.
Average and median reimbursement per accession and by test classification ($)
Average cash reimbursement per accession and per test is important to understanding the profitability of each laboratory patient and test compared to the average price per accession (charge) and how that correlates to cost. Tracking your CPA is essential in setting pricing.
Month-over-month revenue and profitability by client, sales rep, procedure and payor ($)
Month-over-month metrics that measure the profitability by client, test, and payor are incredibly important in identifying if your outreach lab is profitable and helping lab leaders identify where they are lacking. Who are the good clients? The profitable clients? What payors are we struggling with? What tests are we having more difficulty in getting paid due to medical necessity, coding changes, etc.? Without visibility into this, you cannot improve where necessary or understand what areas are in need.
Operating margin (%)
This is a measure of profitability. This metric identifies how much profit is made after all operating costs are accounted for and is calculated by dividing the labs profit by its net sales. This is key in understanding the lab’s financial stability.
We recommend that you discuss these key metrics with your team. Consider setting goals for each, knowing that an impact to one metric will likely have an impact on the others. Review how these metrics are being calculated and change the calculation if needed, so that you are accurately measuring your most important revenue cycle metrics. Your overall business metrics will benefit.
Having difficulty with revenue cycle management metrics? We can help.