by Tim Kuruvilla
Overcoming Size-specific Barriers to Implementation
Healthcare organizations today are both blessed and cursed by data: to get the insights necessary for impacting patient outcomes and improving operational efficiencies, health systems need to unlock the data that is spread across multiple siloed and dispersed IT platforms. Enterprise data warehouses (EDWs), which pull together data from across an organization or health system, used to be the only way that health systems could aggregate, normalize, and store the data needed to drive their analytics programs.
Yet organizations that tried to build EDWs found their IT departments spending hours struggling with “dirty data”—data that was in the wrong shape for visualization and analytics. To make matters worse, each time the data changed or a new information source needed to be analyzed they had to redo their work. This ultimately led to an ugly cycle of resources being spent on data-plumbing rather than on what health systems really needed to be doing: analyzing, visualizing, and operationalizing the data.
Health systems have since learned that there is little value in spending IT resources on the data-plumbing problem, and they have started to look for new approaches to implement their analytics programs. Some are shifting to a more user-centric approach, focusing on tailored analytics solutions that support specific initiatives instead of a one-size-fits-all, IT-driven EDW. Especially for mid-sized hospitals and health systems with limited IT resources, fully managed analytics solutions are the more easily implemented and operated option that still yields the insights required for the mandates of improving care and enhancing efficiencies.