As key contributors to the data underlying RWE, radiologists need a seat at the table from the beginning to guide how this powerful and potentially contentious source of outcomes information is used in their practices and facilities.
The recent availability of de-identified medical image datasets is a tremendous step in the right direction, vastly simplifying the process of incorporating this type of data into RWE. While there is a lot of work to be done, the way forward seems pretty clear.
The primacy of clinical workflow Algorithms sometimes seem to work well — until they leave the testbed and come up against human beings in the real world, in this case caregivers and patients. Many ML models have shown a lot of promise while in the hands of data analysts, only to fall short in the trenches of clinical settings, and thus fail to see any uptake.
Clinicians tend to be uncomfortable with black-box models that lack transparency and interpretability. They want to see and understand the reasoning used to reach the presented clinical conclusions. And they are right to be suspicious since such models can reach flawed conclusions.
But they can also reach unintuitive conclusions that are nevertheless clinically meaningful, and should have an impact on treatment and outcomes.
Every effort must be made to show the connection between the results and clinical practice. Radiologists can contribute by insisting on clear demonstrations of how the model reached its conclusions, and aiding in testing the results in practice.
Radiologists likely make more clinical decisions per day than any other medical specialty, analyzing up to 100 scans per day in 10- to 12-hour shifts. RWE, along with other technologies such as workflow orchestration, should assist in prioritization and decision making. It is particularly important that radiologists be attentive to the decisions being made, and how they affect radiology workflow.
The need for technological agnosticism The third hurdle may seem relatively minor, merely a matter of procurement, but its effects on RWE uptake will be quite significant over time. There is a lot of potential profit in creating useful RWE and providing it to users in an effective form, so the field has attracted many innovative competitors.
Anyone who works in healthcare knows of the perils of being locked into a proprietary system with specific formats, interfaces and required behaviors. At first, each system promises a great step forward in increasing information, lowering cost and improving patient care. Over time, unfortunately as those solutions gain market share and consolidate into only a few vendors, a lack of flexibility appears, new features become less frequent and expenses increase. This cycle has been repeated many times in medical technology, with clinicians realizing that initial exciting short-term savings often turn into long-term expense coupled with reduced innovation.
