I think that one of the most important emerging areas in pathology and lab informatics is analytics which I have discussed in previous notes (see: Lab Analytics Emerges as Hot Area for Software Development; Leveraging Lab Analytic Data to Include Actionable Details to Improve Quality). Stripped to its essentials, analytics involves the analysis and interpretation of the various types of data generated in the labs, often using a dashboard. A major impetus for the pursuit of lab analytics is the need to produce test results faster, cheaper, and better as a result of healthcare reform. Although I have been focusing on lab analytics, there is also lots of activity in radiology analytics. Here's an article on this topic (see: Data Mining and Analytics in Radiology).
I like to divide lab analytics into two components that I refer to as clinical analytics and operational analytics. The former is patient-centric and addresses questions about testing such as the who, why, when. Operational analytics, as the name implies, addresses operational issues such as workflow, distributed testing, and cost-per-test. The interest in operational analytics is currently greater than clinical analytics with the task usually assigned to supervisors and lab managers rather than pathologists.
I had occasion last week to speak with Dr. Ben Strong who is CMO of vRad, a teleradiology company I mentioned in a recent post (see: XIFIN and vRad Partner to Pursue an Integrated Diagnostics Business Model). I learned during this call that the company is actively pursuing both clinical and operational analytics in its "nighthawk" radiology niche. This interest is emphasized by its analytics microsite. Operational analytics is obviously, and by necessity, a core competency of the company. Its radiologists are based in various locations in the U.S. and the imaging studies generated in its client hospitals scattered across the country and at night need to be rapidly accessed by them. Moreover, certain studies such as neuroradiology require subspeciality expertise. One might assume that clinical analytics would be of lesser importance for the company. The vRad radiologists interpret the cases as they appear in their work queues. Of what concern for them are the who, why, and when of a case?
It turns out that the company understands that these who, why, and when questions are vitally important for the future of the company. They are very relevant as the basis for ordering radiology procedures which is to say that they are critical to an understanding of test utilization. All of this begs the question of why a teleradiology company would want to address utilization issues. As I state above, one could assume that the vRad radiologists merely read the cases that appear in their work queues. However, developing upstream radiology ordering algorithms will enable the company to not only interpret the performed tests for its client hospitals but also offer services in the future such as optimizing imaging orders. I am sure that most hospitals would be willing to pay for such services.