Data management classically partitions transactional or operational data from analytics data. The very term “extract, transform, and load” (ETL) speaks to this bifurcation. In healthcare, the electronic health record (EHR) is typically the operational data system. this ETL process, but EHRs, as we’ve typically viewed them, now must incorporate and manage additional information. Data from a patient’s medical history, lab results, and imaging are vitally important. The patient’s insurance kuwait whatsapp number data data or data from other third-party providers must also be taken into account.
Then, OR-specific data must be considered – how long certain procedures typically last, what kind of differences there are between providers, and which instruments or robots are needed in a given room for a given surgery. Systems don’t work as well as they could if this wide assortment of data points is not integrated into a single system from which insights, predictions, and recommendations can be extracted.
Because the data sources live in different applications, seamless integration remains a significant challenge, hindering the application of modern tools. For example, today’s sophisticated analytics and AI solutions become somewhat like driving solely with a rear-view mirror due to the latencies involved. Yet, continuing with the driving metaphor, a high-performance surgery department (and, by extension, all hospital departments) requires information about the road immediately and further ahead. So, how do we reconcile the existing gap.