You need both kinds of clinicians – the ones doing the more RVU-friendly work and the ones reading the less “glamorous” studies – to make a practice work. It’s not just about running a holistic practice; it’s also about running a successful one where clinicians are happy. There isn’t a lot of happiness or job satisfaction to be found in spending hours per day reading studies that are vital to the patient, but that the RVU formula dictates as simply not as important.

To make workloads more equitable, take people out of the equation
Workload distribution should take RVU into account. But it shouldn’t be the sole factor determining which cases get assigned to which doctors. Complex problems require compromise solutions – in this case, a hybrid approach that minimizes, and maybe even removes, people from the study allocation process.
Why exclude people? We’re all human beings, we all have biases, and we all get angry when we perceive bias as positively aiding one person and negatively affecting another. The friction around RVUs and workload distribution is already grounded in the perception that some get preferential treatment based on the value or volume of cases assigned.

One way forward is to consider workflow orchestration technology that distributes cases not solely based on RVU but on multiple factors, like age, diagnosis, urgency, the clinician’s specialty, the number of studies already assigned to them, and so on.

Cases are matched to the clinicians with the most relevant skills and expertise rather than cherry-picked based on personal preference.

Radiologists share more even levels of caseloads, reducing both the risk of burnout and the perception of bias.

Clinicians also receive more equitable compensation, so no one is seen as benefiting more than others.

Patients’ exams are read and prioritized by order of urgency rather than more arbitrary RVU-driven reasons.

This is just one idea, but it’s one I believe has the potential to assign workloads more fairly to radiologists. Fairness, along with the reduced perception of human bias that goes with it, can go a long way toward making physicians feel happier, more fulfilled, and less burned out.

You don’t necessarily need a fully automated, machine-driven approach to this problem. I know one practice that created a committee with outside consultants that set up a new RVU review process to assign cases. But if the idea of bias is creating tension and feelings of unfairness among clinicians – which in turn exacerbates our industrywide burnout problem – then automating workload distribution can go a long way in eliminating this perception of bias. As I often heard at RSNA, “I want a machine to do this so there’s nobody to blame.”

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