CAMBRIDGE, MA--(Marketwired - Aug 13, 2013) - In order to implement population health management and accountable care, healthcare provider organizations (HCOs) will have to deploy dozens of analytical applications, including clinical, operational and financial applications. With demand for applications far outstripping the ability of the IT organization to develop these applications in-house, many of the required applications will be purchased -- frequently by end-users with little or no input from the IT department of a HCO.
"Short-term gains for end-users deploying single purpose applications, could cause long-term pain for IT if too many of these standalone applications get installed, all requiring different overlapping infrastructure technology, data, and support," says Chilmark Research analyst Rob Tholemeier, principal author of the report.
Chilmark Research's latest Insight Report, Building an Infrastructure to Drive Healthcare Analytics, examines these challenges facing HCOs and documents how to create a flexible infrastructure framework to support analytic applications across an institution.
Tholemeier continues: "For example, one application may use Microsoft's data integration, database, or end-user Business Intelligence (BI) software, another could use IBM's, even though IT has chosen a third vendor as the preferred standard for data integration, end-user tools, and database. Consequently, most HCOs will end up with a mishmash of analytical applications with different software components. That at some point will need to be rationalized."
One other challenge that we discuss in-depth within this report is the need to look at IT analytics projects as different from virtually any other IT project. Analytics projects have an exceptionally high rate of failure. What makes analytics hard and prone to failure is that the outcomes, particularly organizational impacts, are not known with certainty at the beginning of the project, and the economics are merely uncertain projections until the project moves towards production. The biggest challenge for most organizations developing a portfolio model is to manage and integrate each analytic project into the platform using the IT best practices we outline.
The bottom line is building an analytics infrastructure is a design and construction process pulling together and assembling a handful of mature and capable software components -- typically from different vendors. We also encourage users to use a portfolio management approach that will be flexible and anticipate potential failure, allowing for quick resource alignment once fate of a given analytics initiative is known.