From the August 2019 issue of HealthCare Business News magazine
Specifically, this value resides in the massive amounts of data regularly collected by these two professionals, and in the insights to be derived from combining and analyzing them. “The use of predictive data and early warning scores is becoming the norm in clinical care,” Bauerlein points out, but has yet to become the norm in equipment management and optimization. Largely untapped, these data streams promise answers to perennial questions about parts replacement frequency and associated costs; the relative wisdom of switching manufacturers; and the concrete effects of changes to operational or clinical protocols. (While most of these questions pertain to the diagnostic side of medical radiation equipment, applications in oncology would also benefit from the answers.)
The most immediately useful knowledge to be gained from the medical physicist/biomedical engineer relationship comes from its dual perspectives on specific key performance indicators. The most basic data analysis would flag an increase in a KPI like the number of hours equipment is down, while a more sophisticated analysis could help track the increase back to its cause, such as a generator miscalibration or recurring mechanical issue. It is important to have both data sets — the operational data of the engineer and the compliance data of the physicist — in order to clarify the origin of the problem. Can the fault be traced back to the equipment, its parts, or its operators? Or is it the result of changing regulations or some other external factor?
From technical service to detective work
How would such value look in the real world? A recent experience at an eleven-hospital medical corporation in the American northeast provides one illustration.
This year, the organization’s medical physicist service noticed that the facilities’ x-ray tubes seemed to need replacement more frequently than in the past. Because the service had to confirm that the machines were functioning properly after each replacement, records indicated that the physicist’s hunch was right: the hospitals were going through CT tubes faster than usual.
In most cases, there are no “next steps” beyond this insight, particularly when the medical physicist is not an in-house employee. But by comparing notes with the biomedical engineer at the corporation, the physicist was able to corroborate the change and then trace it back to the organization’s decision to change manufacturers, a decision made without input from either service line. Digging deeper, this ad hoc team found out that daily quality control indicator drift was unusual, compared with units that utilized the new OEM tubes. These discoveries led to a change in practice: this KPI is now monitored by the facilities’ biomedical staff as a predictor of tube malfunction, whereas it had previously been considered a compliance activity alone. In other words, the medical physicist/biomedical engineer relationship shifted a reactive approach to tube replacement to a proactive one.
