3 key elements of any effective analytics strategy

#5in5_Blog_BannerAn effective analytics strategy can help provider organizations control costs and identify risks and opportunities – key techniques to succeeding in a world of payments based on outcomes rather than volume.

Any effective analytical strategy needs three elements.

1. Highly accurate data

Analytics tools that provide the most usable insights generally draw data from a large sample size that includes diverse sources.

For health care, that can mean claims data, electronic medical records (EMRs), socioeconomic data and care management information – anything from lab work to pharmaceutical reports, inpatient and outpatient clinical data across all sites of care.

The data must also be prepared for use. Raw data must be analyzed and updated to account for human error, redundancies, undefined terms and other issues.

2. The ability to predict at-risk patients

To treat patients proactively, medical groups need to know which patients are in need of extra help.

For example, using data to discover what sequence of symptoms landed previous patients in a hospital may help providers identify patients currently on the same path.

Reaching out to these patients before their condition worsens could help keep them from being admitted – protecting their health and reducing preventable costs.

Understanding where providers can directly influence outcomes is also an important part of developing an analytics strategy, according to Dr. Carl Johnson, senior physician director for Optum Analytics.

“Let’s say a model finds patients who are likely to get congestive heart failure. That may not necessarily be useful because providers typically can’t prevent heart failure,” says Johnson. “But, predicting hospitalization due to congestive heart can be helpful. Since CHF admissions have well-proven approaches for prevention.”

3. The ability to track and compare performance

Carl Couch, MD, president of Baylor Quality Alliance, a clinically integrated provider organization, says you can’t manage what you don’t measure.

Dr. Couch said. “When we look at Dr. A and Dr. B and Dr. C, we need to know why one of them has far better clinical performance or one of them has far worse financial performance than the others. That leads to discussions on what we need to modify.”

Using analytics, an organization can track the success of certain interventions and compare outcomes to evidence-based guidelines in hopes of finding new ways to improve care and reduce costs.

With all of these elements in place, value-based organizations have the basis for an effective population health management program.

Learn about the connection between analytics and population health management from Allen Kamer, chief commercial officer for Optum Analytics. He answers five questions in #5in5 Coordinating care with population health tools: Targeting resources to maximize outcomes.

You can also explore the value of data quality in the Optum white paper: Getting from Big Data to Good Data: Creating a Foundation for Actionable Analytics.

About the author

Karen Thomas-SmithKaren Thomas-Smith is vice president of Provider Marketing & Reference Management at Optum. She brings to the role more than 15 years of global experience in the software industry. Karen has shared her unique ideas on corporate culture and leadership in a number of television appearances on Oprah, 60 Minutes and Canada Public Television. Prior to her position at Optum, Karen spent time at Allscripts and SAS.

She holds a bachelor of science degree in business administration from North Carolina Wesleyan College, graduating Magna Cum Laude.

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