4 Ways to Drill Down into Bad Debt

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1 4 Ways to Drill Down into Bad Debt By Craig Kappel and Brett McMillan Conducting this four-step analysis of your hospital s bad debt is the first step to controlling it. Revenue cycle scorecards typically track high-level bad debt metrics, such as bad debt writeoffs as percentage of gross patient service revenue. While these can help leaders understand recent changes in bad debt performance, they do little to pinpoint specific opportunities for improvement. To develop a sustained and robust understanding of the determinants of bad debt, healthcare providers need to drill down further by conducting four additional analyses. Analysis 1: Bad Debt Transfer Timing Every business office has a mechanism that identifies eligible bad debt transfers and executes a bad debt write-off transaction on these accounts. Some are automatically driven by rule engines built into the organization s IT infrastructure; others are manual. Determining how many days it takes to transfer accounts to bad debt can help an organization see if its processes are working as intended. Creating the analysis. Calculate the median age from service date for all accounts transferred to bad debt during a recent 12-month period. Separate the uninsured accounts from those that are self-pay after insurance. Plot the results on a simple time line. This metric should be tracked on a monthly basis because process improvements could improve results almost immediately. It can be useful to further break down the data by specialty. For example, the exhibit below shows that the timing of bad debt write-offs at one hypothetical hospital varies considerably among specialties, even though all specialties are governed by the same organizational policies. \ 1

2 Interpreting the results. In our experience, leading-practice organizations write off uninsured patients to bad debt approximately 120 days from the service date. This generally provides enough time for several statements to be sent, and is consistent with Medicare bad debt guidelines. For self-pay after insurance patients, the median write-off date should be 120 days plus the average insurance carrier days to pay. For instance, if accounts receivable (A/R) days are 34, the median bad debt write-off should be about 154 days. Many facilities fall outside of these leading-practice time frames for bad debt write-offs. Thus, most hospitals could improve bad debt management by analyzing their bad debt transfer timing. Analysis 2: Bad Debt Recovery Rates An analysis of bad debt recovery rates sometimes points to the need for a more rigorous collections process in patient access. Creating the analysis. To calculate bad debt recovery rates, first isolate all accounts transferred to bad debt for a six-month period ending six months prior to the date of the analysis. For example, if running this analysis in January 2014, choose all bad debt transfers for the period of Jan. 1, 2013 through June 30, Next, identify any bad debt recoveries collected on these accounts for the previous 12 months. Bad debt recoveries are A/Rs resolved through cash and contractual adjustments after the bad debt write-off date. The lag between the two sets of data creates a buffer of at least six months 2

3 (and up to 12 months) for recoveries to be made on these bad debts. While some recoveries may be made beyond 12 months, this time frame provides a representative analysis of the overall population. Finally, identify the original insurance for these accounts so that self-pay after insurance is segmented from true self-pay accounts. Interpreting the results. A low bad debt recovery rate from uninsured patients is not unexpected. However, significant recoveries from patients with managed care or commercial insurance is typically a sign that pre- and point-of-service collection protocols are not in place or are not functioning as intended. For instance, the exhibit below displays recovery rates for a hypothetical hospital. There is significant variation in recovery rates by payer. Recovery rates from uninsured patients are about 2 percent, while recovery rates from managed care payer no. 1 patients, at 20 percent, are 10 times higher. The weighted-average recovery rate is approximately 11 percent, but this is weighted down heavily by uninsured patients. Analysis 3: Common Bad Debt Write-Off Amounts Pinpointing the most common reasons for bad debt write-offs (e.g., copayments or Medicare deductibles) can help revenue cycle leaders identify targeted solutions for fixing problematic processes. Creating the analysis. Isolate all bad debt transfers for a recent 12-month period. Group the data by transaction amount. Select the top 20 most frequently occurring transactions and list them with corresponding account volume, total transaction amount, and type of charge (see exhibit below). 3

4 Interpreting the results. The results of this analysis typically quantify the potential benefit of strengthening pre-service clearance and point-of-service collections programs. In our experience, bad debt sourced from insured patients is growing at a faster rate than bad debt sourced from patients without insurance. From what we ve seen, the most frequently occurring bad debt transactions are common copayment amounts, such as for insured patients, such as $15 or $20 per office visit. The remaining amounts are typical charges from the emergency department (ED) or clinic areas that are used by some patients as a place to receive primary care. Common copayment amounts are obvious targets for up-front collections, and leading-practice organizations often ask uninsured patients for nominal self-pay deposits. Simply having this type of conversation with patients may help stop this type of preventable bad debt from occurring. 4

5 Analysis 4: Repeat Bad Debtors A high-repeat bad debtor rate indicates problems with front-end policies, financial counseling, or clearance procedures, or a combination of these factors. Creating the analysis. Isolate all bad debt transfers for a recent 12-month period. Next, group the data by guarantor (or by patient if guarantor is unavailable). Finally, calculate the percentage of accounts transferred to bad debt during the period that belong to a guarantor with more than one account transferred to bad debt during that period. For example, 20 percent of the patients might be repeaters but 50 percent of the dollars might be associated with those repeaters. Calculate this repeat bad debtor percentage for both the number of accounts and total amount of the bad debt transaction. A sample analysis is shown in the exhibit below. The analysis shows how many patients had one bad debt transaction during the measurement period, how many had two bad debt transactions, etc. For each category, the total dollar amount written off and the total number of accounts are shown. It is useful to calculate this metric for guarantors themselves, rather than just accounts. For example, if 100 individual guarantors had accounts sent to bad debt during the period, and of these, 36 guarantors had more than one account sent to bad debt, then the guarantor repeater rate is 36 percent. Interpreting the results. An organization s patient segmentation protocols may come into question based on this analysis. A high-repeat bad debtor rate may indicate that the organization needs to improve its ability to appropriately classify patients between bad debt and charity. In other words, a portion of the bad debts really should be charity care. 5

6 Another possibility is that insufficient financial clearance procedures and controls, or inadequate execution of these controls, are contributing to patients ability to cheat the system. Further iterations of this statistic by month, registration area, or day of the week may provide additional insight into gaps in the patient access process. From Analysis to Improvement By conducting analyses such as these, revenue cycle leaders can develop a better understanding of their organization s specific determinants of bad debt. Embedding proactive analyses like these into revenue cycle analytics can help an organization stay abreast of trends, bolster a continuous performance improvement culture, and develop a high-performing bad debt management program. Craig Kappel is a partner, advisory services, Ernst & Young LLP, Boston (Craig.Kappel@ey.com). Brett McMillan is a manager, advisory services, Ernst & Young LLP, Richmond, Va., and a member of HFMA s Virginia-Washington, D.C. Chapter (Brett.McMillan@ey.com). This article originally appeared in the September 2013 issue of HFMA s Revenue Cycle Strategist newsletter ( Used with permission. 6