Lost in Transcription
Charge reconciliation—the practice of identifying procedures that have slipped through the cracks at some point in the charge process and billing for them to optimize revenue—is important for any medical group, but particularly critical for imaging. Radiology is one of the most high-volume specialty areas in terms of both codes and procedures billed; radiologists can work through hundreds of interpretations per day, making manual charge capture a Sisyphean undertaking. Automating this process can help radiology practices reclaim a significant chunk of lost revenue; based on audits of hospital-based practices, the average error rate for charge capture ranges from 4% to 6%.
Put simply, the process of charge reconciliation consists of ensuring that a practice’s business office is billing for everything that the radiologists have interpreted. Leakage occurs when interpretations are lost in transcription—in other words, when patient data or the corresponding CPT® codes include some kind of error that prohibit them from being billed. Once, this inevitable result of occasional human error had to be addressed manually, by keeping a log of radiologists’ every interpretation and having a staff member go through the list to make sure that every charge made it through. The Automated Approach Automation not only makes this process faster and more accurate than the manual approach, but also reduces staff time spent confirming each charge. Focusing only on errors and exceptions makes this a more cost-effective technique. It starts with the export of a list of procedures from hospital information system (HIS) or RIS, an electronic log detailing all the patients who underwent radiology procedures in a given day. After a reasonable waiting period (30 days is generally sufficient), that log is scrubbed against what was entered into the billing system. An exception report is produced, identifying any lost reports. There are a number of reasons that reports could be lost in transcription. A misspelled patient name could be the culprit, as could a mistyped CPT code; when a patient undergoes multiple exams in a single day, and these exams are coded separately, only one might make it through the billing process when charges are sorted based on patient name and exam date. For interventional radiology, in particular, there are generally multiple CPT codes involved, and the higher charge volume corresponds with higher dollar amounts to be billed. A large portion of revenue could be left unaccounted for by missing an interventional patient or missing some of that patient’s procedures. Automating charge reconciliation pivots around the accuracy of the log produced by the RIS or HIS. The hospital’s IT department can be helpful in identifying which data source is likely to be more accurate (in terms of which procedures were performed that day, on which patients). A common mistake is beginning with a log of procedures scheduled for a given day, instead of a log of procedures performed; this can leave staff members scratching their heads as they search for exams that never existed. Another critical component of the process is the skill set of the person working through the list of exceptions. A coding background is required for parsing situations such as that in which an exam is ordered as a CT study with contrast, but is billed as a CT study with and without it; it takes a person with industry knowledge to understand that this situation constitutes not a missing report, but rather, a report that was changed. Eliminating these false positives from the exception report enables staff members to spend more time focusing on finding missing exams, coding them, and billing for them. Analysis, Reporting, and Benchmarking Automated charge reconciliation offers another critical advantage: It permits analysis, reporting, and benchmarking. Taking the next step (beyond mere identification of errors) enables practices and hospitals alike to identify problem areas where charges are slipping through the cracks. In some cases, a certain modality will reveal itself as a problem area. Repeated missing MRI charges, for instance, represent a larger proportion of overall revenue than missing chest radiographs or ultrasound exams; a 2% or 3% error rate can have a big impact on a practice’s bottom line. Knowing that MRI is an area of vulnerability allows practices to seek out the issue and address it, whether it’s a new code or a change in the ordering process. Changes to the HIS or RIS can also affect charge capture, as can any number of other issues, which might range from having new, inexperienced coders on the job to having radiologists who never finish their dictated reports because they call the referring physicians directly to notify them of life-threatening issues (and then fail to complete their reports). By knowing that its typical error rate is, for instance, 1% or 2%, a practice can take any spike in this rate as a signal that something has gone awry, and can work to identify and address the problem. Automated charge reconciliation also enables radiology practices to make more accurate revenue projections. Before, a practice might have estimated revenue based on the number of radiology exams performed and might then have been surprised at the discrepancy, but with charge reconciliation, practices can better estimate the lag between identifying missing procedures and billing for them, leading to better projections. Although automating the charge-reconciliation process requires due diligence in its own right, from locating the best source for computer-generated procedure logs to cross-referencing those logs against billing records to employing the right staff to interpret the information, automation also offers numerous advantages. By helping practices to focus on errors and exceptions (and benchmark their own performance), automated charge reconciliation not only boosts the bottom line by recapturing revenue that would otherwise be lost, but also enables savvy practices to identify the sources of problems, gradually reducing their risks of lost revenue from charges lost in transcription. Andrew Casselberry, MBA, serves as a vice president of operations for the West region of Medical Management Professionals Inc and is based in Tulsa, Oklahoma.
"By helping practices to focus on errors and exceptions (and benchmark their own performance), automated charge reconciliation not only boosts the bottom line by recapturing revenue that would otherwise be lost, but also enables savvy practices to identify the sources of problems, gradually reducing their risks of lost revenue from charges lost in transcription."
— Andrew Casselberry