Dimensional Insight

George Dealy

A recent IDC Health Insights report shows that, analytics will continue to be one of the fastest-growing segments of provider IT budgets in 2016, as it has been for several years. Specific areas of new investment include—provider and care team performance analytics and data that examine referral patterns and other financial areas. However, while healthcare institutions clearly recognize the value of an analytics platform, many are still challenged with turning their data insights into actionable alterations of their care delivery tactics.

"A healthcare analytics program can impact outcomes, improve clinical performance, ensure the best patient care and increase overall operational efficiency"

Given the complexity of healthcare data and the pressures of regulations and oversight, it is easy to see why healthcare organizations feel as though they are drowning in data without a clear path to the finish line. The thought of launching a new platform can seem intimidating and often paralyzes healthcare organizations from even beginning the process. Before these organizations can even launch a platform, they need to take a step back and create an inventory of their data assets. The goal of this stage is to understand the capabilities of the available data.

Data governance is the next critical step in preparing for an analytics program. A concerted focus on governance early in the analytics lifecycle helps to ensure that efforts and resources are concentrated on solving the most critical problems. While the IT department may have a good handle on the data, understanding how it can be used to impact value is often less clear. Having an effective data governance plan will help to guide users to the data most appropriate for them. Additionally, knowing what your organization’s goals are, and determining how to best support these goals with information derived from your analytics platform, are among the most important aspects of governance. That’s why ironing out data governance and understanding IT, finance, service line and provider points of view is important, prior to launching any analytics project.

Intertwined with data governance is the issue of business rules, which refer to the transformations that are applied to the data between the original data source and the user presentation. Using length of stay as an example you may discover that within your organization there are many different ways to define this measure (i.e. acute patient population vs. all patients). Before trying to meaningfully analyze the data, organizations need to define and implement the rules, then close the loop through the data governance process to ensure that everyone understands what measurements they should be working with, how these are specifically defined and what they are intended to measure. When done well, business rules are the foundation of any successful analytics program and are essential to achieving organizational goals. Defining rules precisely up front helps to determine what data is actually needed to produce relevant and reliable quantitative measurements. Which is arguably the most important part of the whole analytics puzzle.

Once data governance is in place and initial business rules have been defined, the next step is to ensure that the organization possesses the ability to derive valuable insights from the available information. Increasingly, larger and complex data sets that integrate clinical data with claims and operational data, comes with both challenges and opportunities. Both the information and the ability to understand and leverage will become the new currency for communicating across both departmental and functional organizational boundaries. For example, a discussion around cost is only useful if an institution is able to consider the impact on quality, safety and patient experience. Access to information that is well-defined and understood enables financial and clinical staff to have more meaningful and ultimately productive interactions. This can start with an important observation that is only possible with the right supporting data, such as whether a particular protocol is having the intended effect on outcomes. This requires both the availability of data and the skills necessary to interpret it.

By both implementing and effectively using analytics solutions, healthcare organizations will be equipped to identify inefficient processes and, in turn, provide insight for improvements. After all the meaningful and valuable data is uncovered, the success or failure of any healthcare analytics program revolves around getting the information into the hands of the front line. Since changes to actual practice come largely from observations of front line managers, giving them better access to the data is critical. Whether using summary level dashboards or interactive analysis, front line staff and managers will have opportunities to build analytical competencies and draw their own conclusions– ultimately fostering a culture of data-driven decision making.

When done correctly, a healthcare analytics program can impact outcomes, improve clinical performance, ensure the best patient care and increase overall operational efficiency. As healthcare institutions begin, or continue to build on, their data analytics programs, the focus needs to be on providing the information that best supports evidence-based decisions and capitalizes on opportunities to build analytical competencies. As per IDC’s report, the future of data analytics is big. It is just a matter of making sure healthcare organizations have the ability to drive real, actionable value from their analytics platform to be able to impact outcomes.

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