From digital check-ins to connected devices and telehealth programs, patients expect the benefits of a more digitized healthcare experience.
At the same time, they’re also demanding a more personalized approach from healthcare providers. This duality - the need to provide a more convenient experience with one that’s more tailored to the patient - is fueling a wave of technology modernization efforts and the replacement of monolithic legacy IT systems.
With limited re-use outside of the context they were built for and a reliance on nightly batch processing, legacy IT systems fail to deliver the services healthcare IT teams need or provide the experiences patients demand. Modernization should come with a move to microservices that can be used by multiple applications, agile teams that embrace domain driven design principles, and event busses like Kafka to deliver real-time data and functionality to users.
While this transformation is occurring, there’s an 800 lb. gorilla not being widely addressed: Analytics.
What the healthcare industry doesn’t want to talk about is how costly analytics has become; the people, the software, the infrastructure, and particularly how difficult it is to move data in and out of data lakes and warehouses. It's hindering the industry’s ability to deliver insights to patients and providers in a timely and efficient manner.
And yet, so many organizations are modernizing their analytics data warehouses and data lakes with an approach that simply updates the underlying technology. It’s a lift-and-shift effort of tremendous scale and cost, but one that is not addressing the underlying issues preventing the speedy delivery of meaningful insights.