Eventing systems will be crucial to improving (currently low) EHR productivity.
An event is a change in state, in a patient (blood glucose exceeds threshold), a population (percent of population whose average blood glucose exceeds threshold for specified duration); or even change in logistic state (patient entering waiting room, average patient wait time exceeding a pre-specified limit, or perhaps even an external event such as an admission, a discharge, a transfer).
No. Not those kinds of events.
Maximizing EHR productivity (value of information state divided by cost of information state) requires managing how EHR events trigger EHR workflows. Events in the numerator (info value) include those just listed. Events drive workflows. Examples include ordering a test, changing policies controlling test workflow, notifying whoever is next in a patient-specific workflow that a patient is waiting for a test, and policies controlling workflow escalation. Events in the denominator (info cost) include a big-data ocean of time-stamped EHR-initiated, and sensor-detected, events. Methods for extracting patterns and building models from this data are evolving, but process mining is one increasingly popular tool.
The more consistent, yet also transparent and flexible, the EHR workflow, the greater the potential for systematically improving EHR productivity. The best candidates for automating this unique combination of consistency, transparency, and flexibility include modern business process management suites and their cousins, adaptive case management systems. The best candidate for understanding and improving these resulting workflows is to feed insights of processing mining back into workflow design. Doing so will discover and correct non-conforming workflows and throughput bottlenecks, both necessary steps to systematically improve EHR productivity.
And this virtuous cycle, of improving EHR productivity, will not be possible without incorporating eventing systems.