Five Steps Toward Maximizing Your Clinical and Business Intelligence

April 3, 2017
Effectively addressing clinical and business intelligence (C&BI) can drive more accountable and higher quality patient care and improve an organization’s financial performance. Kam Reams of Freed Associates shares five steps for addressing C&BI to help ensure successful implementation and operation of a robust analytics strategy.

Although management guru Peter Drucker famously said “What gets measured, gets managed,” measurement itself, while a great starting point, is no guarantee of management.

In health care, clinical and business intelligence (C&BI) involves analyzing and using healthcare data to directly inform—and presumably improve—clinical and business decision-making. Yet data analysis is easier said than done, as HIMSS board member Diane M. Carr recently acknowledged in an interview: “With the explosion of electronic health records recently, more and more health systems and individual providers are collecting data on patients than ever before. The challenge is to use those data to benefit the patients, and by that I mean to turn that data into useful information.”

Given the vast amounts, varied types and velocity in which data is being captured and shared, the health care industry has reached a critical juncture in health care analytics. When addressed effectively, C&BI has the potential to drive more accountable and higher quality patient care and improve an organization’s financial performance. However, done poorly—or worse yet, not at all—C&BI can waste precious resources and lead to misinformation and frustration.

Whether you’re seeking to turbo-charge your organization’s nascent C&BI efforts or simply get C&BI off the ground, the five steps below detail a path to ensure successful implementation and operation of a robust analytics strategy.

Kam Reams

Step 1: Define Your Business Needs and Questions

Diving into C&BI without first identifying your business needs and ensuring they match your organization’s strategy and prioritizing them is like embarking on a trip without a destination or a vehicle to get you there. Driven by both internal objectives and external requisites, your organization’s needs will likely be the basis on which data collection questions are formed. These questions can span from macro, big-picture questions, such as: What’s really driving my readmission rate? Do we have an effective care management model? Or more granular and specific questions, like: What’s our total cost of care for hip joint replacements? What are the greatest drivers to our holiday staffing costs?

The ultimate recipients of your data will also drive the nature of your questions. For example, generally speaking, senior leadership will be most interested in questions pertaining to the bottom line. Operational leaders will want answers to clinical and/or business questions pertaining to their specific departments or areas of responsibility. When formulating questions it is critical to include key stakeholders from all areas of the organization to ensure the appropriate questions with the right specificity are defined.

Step 2: Understand Your Data

When seeking to understand your data, engage analytical team members from both technical and business operational areas early in the process. The more team members understand what you’re seeking to glean from your data, the better they can counsel you on the most appropriate approach to data analysis.

Step 3: Set Benchmarks and Targets

Benchmarking and setting targets are important components of C&BI in that they support shared accountability and a common purpose toward meeting organizational objectives. It is a common practice to establish benchmarks and predefined targets, referencing industry standards.

Bear in mind that as your organization matures and gains greater familiarity with particular C&BI data sets, it is a best practice to periodically reevaluate targets to ensure they continue to align with organizational business needs. This may also include identifying a stretch target to encourage higher performance and more rapid process improvement.

Step 4: Analyze and Present Your Data

Early on in a particular data collection/analytics process, all parties involved—from data collection to recipients—should agree to an iterative approach. The first report generated by a particular group of data is typically not the definitive final word; more likely, it is part of a collaborative process that’s refined and re-refined as all parties grow more familiar with the data and its applications.

Your ability to gain organizational and/or department buy-in and support for a collaborative C&BI process will depend on the expectations you establish at the outset. Left to their own thoughts, end recipients of data often expect it to be a “magic pill” that somehow answers all questions. That could happen, and just as frequently data can be a cold splash of water that surprises and confounds its recipients. Strive to anticipate what sort of reaction the C&BI data and analysis will reveal, and anticipate the likely questions it will generate.

Step 5: Keep Data Definitions Clear and Consistent

Make sure your data analysis covers a broad enough scope of time or performance to ensure consistency and accuracy, and accounts for any aberrations. It’s important that data definitions are kept consistent over a long enough period of time to help all parties involved determine if results are as expected and to create shared expectations and understanding about what is being reported. Measurement consistency will also support the evaluation of any improvement activities.

Emphasize Continuous Improvement

For all organizations, regardless of size or resources, the C&BI process is a continuous journey, with constant opportunities for fine-tuning, re-calibration, and improvement. An effective C&BI process requires not only the right technical tools, but also an organizational leadership committed to building and sustaining the type of transparent, evidence-based culture that C&BI helps create.

Kam Reams joined Freed Associates, a California healthcare consulting firm, in 2012. Kam has significant experience in health care analytics, business intelligence, and statistical data analysis methodologies, including helping medical groups, health plans, and health care systems achieve their business and quality goals by interpreting and translating complex data and analytics into actionable specifications. Kam also has experience supporting health care organizations with planning and implementation of strategic priorities.

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