From the August 2017 issue of HealthCare Business News magazine
What would success look like, a.k.a. what are the system requirements? A successful system would need to be simple, robust and as inclusive as possible with rapid feedback. Ease of use is critical. A system would be considered a failure if the system was cumbersome, required extensive clicking or lacked specificity. To determine success, we created the following metrics: radiologist determining concordance or lack of concordance (called learning opportunities), or not applicable; and we monitored the percentage of cases marked as either concordant, learning opportunity or not applicable.
Our solution leveraged our infromatics partners, where we innovated an automatic feedback loop between radiology reports and pathology reports. In order to accomplish this feedback loop, we matched the potential pathology specimens with a correlative radiology body part. So, for example, a surgical lung specimen would correlate to a CT or MRI of the chest, but not a cardiac CT. We chose to only include cross-sectional studies to limit the number of correlations. Feedback was accomplished in two ways: via an email alert for all cases with an imaging report and a corresponding pathology result; and via a module that shows these matching results in a tabular format. In both of these formats, a single click by the radiologist results in a concordance, learning opportunity or not applicable designation. This system met all requirements and was released to all radiologists at NYU.
During our post go-live assessment, we determined that the number of cases that were marked as concordant or learning opportunity by radiologists was low. Despite the fact that the system met all requirements, adoption was limited. After discussion of this system with users, one of the key limitations for adoption was the volume of emails and matches that were generated. Many pathology results have several addenda. In some cases, as many as five emails were generated on a single pathology report resulting in email fatigue. With this feedback in hand, we changed the system to only send one email with each potential match. This change resulted in a significant decrease in the number of email alerts and a significant increase in the percentage of cases marked by radiologists.
This system shows a classic example of a product life cycle. There is a clearly defined problem, with clearly demonstrable expectations of a successful solution. The system was designed to meet the needs of the end-user. After the system was deployed, repeated measures showed a lack of success marking the need to iterate. Direct feedback from the end-user directed the redesign and dramatically improved compliance. Continued assesment of metrics and redesign based on user feedback is essential in any new system.
Another issue is the lack of visibility of the radiologist. This is considered one of the top 10 threats to radiology as a specialty. Before the advent of PACS, physicians would come to the radiology department because we controlled all the images from studies. Now, with the distributive model of imaging, where every computer can display images, physicians are less likely to interact directly with the radiologist. However, during this transition to PACS, medical imaging went through a revolution. Clinicians became heavily reliant on imaging for many aspects of patient care and, as a result, the volume of imaging studies exploded.
