Rule-based System: We conceived and built a rule-based system that enabled automated assignment of exam reviews. These rules could be applied at different levels. For instance, a rule could be applied as broad as an entire practice or as narrowly as an individual radiologist. Specific categories also included sites and types of employees, for instance, new hires. The categories were dynamically linked to the companies database so that as options changed, for example, a new radiologist is hired, options automatically update. In addition to a selection of radiologists to which a rule applied an amount could be indicated, in either percentage or absolute numbers.
Automated Selection: The set rules were then cumulatively applied to exams in a rolling window. For instance, if 2% of exams were to be reviewed, 2% of the exams for the previous 30 days would be randomly selected for review. The process was set to repeat every day and the exams previously selected would already be accounted for.
This simple concept was applied throughout the system and on all rules using hierarchical layering. This helped minimize the number of exams selected while satisfying all rules that had been created.
Effective Sample Selection: Our process ensured that the exams that were selected for review were relatively recent and evenly distributed in time. The size of the window was adjustable so that stochasticity in the sample could be managed.
Resource Management: Finally, each exam was assigned to a radiologist that was credentialled to review the exam. This process included accounting for any specialities that might be applicable, i.e., MSK, Neurology, etc. Workloads were balanced among all radiologists using the Relative Value Unit (RVU) assigned to each procedure. As new exams were selected for review, it was balanced to perform across all viable peers. Thus, no member of the radiology team felt encumbered because they were assigned more complex exams to review.