There are lots of transactional systems collecting lots and lots of data, but it is often hard to mine the data to identify trends and opportunities. Executives and administrators in healthcare organizations are often frustrated when they know the data they need are "in there." but getting to them to support planning and decision making proves difficult and time-consuming. This situation has been getting steadily worse with the increasing adoption of advanced clinical information systems electronic health records. These systems are designed to provide fast response time, one patient at a time. They are not designed to easily support cross patient analysis and reporting. Organizations spend tens of millions of dollars acquiring and implementing these systems with a focus on clinical transformation--improving clinical efficiency and outcomes only to be stymied when trying to access the data to evaluate how EHR adoption is affecting the performance of the organization. The time has arrived for health care to seriously pursue methods to turn data into information.

Leveraging Data for Clinical Excellence
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The need for clinical, financial, and administrative analytics and reporting spans a broad spectrum of clinical and business requirements. Frequently, the focus is on quality outcome and patient safety initiatives. There is also the need to provide data for mandatory reporting to the Joint Commission on Accreditation of Healthcare Organizations (core measures) or the Centers for Medicare and Medicaid Services (quality indicators). New pay mechanisms, such as pay for performance and related initiatives, need aggregate data reported on clinical outcomes. In organizations that have a research focus, there is a growing need to combine genotype and phenotypic data with clinical operations data to understand how genetic profile, clinical process, and therapies affect outcomes. This analysis can, in turn, lead to discoveries of new and improved approaches to clinical care.

This problem of leveraging the vast store of clinical and other data to support improvements in clinical excellence is not solved by simply dumping all the data into a giant data warehouse and giving folks (appropriated access with a reporting tool. More than one CIO has referred to this process as creating a data "landfill." The data warehouse might contain everything--including the kitchen sink--but the data are not necessarily organized in a way that the warehouse can be used effectively. People trying to run reports to analyze a problem--why a particular contract is not as profitable as anticipated, for example have to sift through a lot of useless stuff before finding what they need to answer their question.

Bringing together disparate data from multiple sources across the organization requires a plan that is driven by how the data will be used. In other words, the data warehouse needs to be built on the foundation of the clinical and business issues the organization intends to address. The issue of building an analytics environment needs to be approached holistically, from the top down, keeping in mind the overall clinical and business objectives of the organization. This will result in an integrated, enterprise informatics plan that will carry the organization through a multiyear design, build, and execute process.

Often, organizations begin the process of building a data warehouse by focusing on the technology it will use. Choosing database tools becomes the focal point. But before a data model can even be started, many issues around governance structure and process about how to manage the data must be put in place. The organization needs to define a knowledge management process. Not all needs can be met simultaneously. New sources of data and new needs for information will continually arise. A process for discussing the inevitable conflicts and prioritizing needs must be in place before an organization can begin the more tactical process of creating an appropriate analytics environment.

Taking Inventory

Once a knowledge management process is put in place, the organization should inventory all the various data analytic tools and databases currently in place. Understanding what exists and how it is used adds additional information to the overall integrated informatics plan. These existing tools can also be leveraged in the building of the new enterprise analytics environment.

Through this planning stage, which typically takes an organization three to four months, a decision framework needs to be defined that not only guides prioritization and decisions now, but helps support decisions into the future. This decision framework should be composed of a set of "principles" that describe the needs and aspirations of the organization around analytics. It should reflect the clinical and business goals and objectives of the organization