Medical billing systems are database files managed using programs that perform accounting functions. These are written to accommodate a daunting number of rules imposed by health-insurance companies and state/federal programs. They contain edit functions that screen all relevant acquired demographic and clinical information for completeness and adherence to claim-filing requirements.
While this screening prevents submission of claims with incorrect field data, it cannot anticipate rules pertaining to physician credentialing, exam preauthorizations, patient eligibility, and so on. The system is capable, however, of providing valuable insight by cataloging insurance rejections based on payors’ rule sets. Such analysis provides useful information when these rejections are organized in various ways. There may be a preponderance of a certain rejection by ordering physician, reading physician, imaging site, or type of exam, for example. This information can be acted on to reduce rejections in the future.
A system should provide reports that enable practices to judge short- and long-term progress in collecting patient-services income. The reports’ extent depends on file structure and data-mining capacity. Today’s systems can retain and catalog an unlimited amount of transaction detail, which can be rapidly compiled due to ever-increasing processing speeds. The degree of detail is only limited by the imagination of the practice administrators. If information is captured, it can be organized to suit the needs of the user.
The most advanced systems are providing access to receivables reports via Internet, where practice members can log in and either automatically print standardized reports or use some data-mining options that enable them to isolate information specific to a practice segment. Transaction detail is generally offloaded to a separate server, either daily or weekly, so that this data manipulation does not interfere with operational processing (such as posting debits and credits or generating claims/statements).
The Role of the RVU
Many practices compile work RVUs per physician. It is easy to populate a library file with work RVUs per CPT code, using this library to compute monthly and year-to-date statistics based on exam volumes. It is possible to show the data as units posted to the receivables system in the processing month or to reorganize the statistics by original month of service (MOS). The MOS data, however, will be slightly inaccurate because a practice is required to use the date that the patient had the exam, not the date that the dictation was performed. In about 80% of cases, the two will be the same, but exams performed after 3 PM might not be read until the following day.
Work RVUs can also be used to construct a time valuation, albeit an imperfect one. The work units are closely linked to the time that it takes to read and dictate a case. The entire fee schedule can be converted to time elements by dividing all work RVUs by the single-view chest RVU, then establishing an agreed-upon average time for handling a single-view chest exam, yielding a time conversion factor. Multiplying this conversion factor by all other work RVUs provides a library table of approximate reading times per exam. It then becomes possible to approximate the reading time per month, per physician.
Although these data are readily available, there is limited evidence that they are used as a basis for income distribution. There is universal concern about the impact of volume-driven incentive plans on group chemistry. An important element in group cohesion is the fair distribution of departmental coverage, where some time slots carry lower reading volumes. There is also the opposite issue, where some members, being concerned about volume-driven income, take on inappropriately large caseloads that trigger reading errors and general burnout.
Deriving the work RVU statistics per member is valuable as a way of benchmarking group size against regional/national criteria. Groups should share these data with the ACR, which occasionally surveys its members, so that all member practices have access to current information. The last work RVU survey included many practices that did not have the advantage of a full PACS environment, causing such large deviations that it made the statistics difficult to use as planning tools. It has been proven that PACS materially improves productivity. New, post-PACS survey data would be useful.