Accuracy and validity of data are persistent concerns for those who use it and are the subject of that data. And the concerns are well founded. Data error is a risk to patient safety.

Distrust of data can stop or delay action on a performance improvement agenda and it's very difficult to overcome that distrust. Tracing and correcting errors is costly and often imperfect. The amount of operational inefficiencies due to data quality issues, such as untangling an error in patient identification, is legend.

In short, problems in data accuracy and validity can impair the value of the information that health care is investing so much to digitize.

The Data Warehousing Institute estimates that poor data quality costs U.S. businesses $600 billion a year. There's no estimate of the cost of data quality problems in health care-but even the most conservative guesses for quality problems in the nation's largest industry would indicate there's real money at stake here.

The explosive growth of digital information-with weak information governance-has raised the stakes. Information integrity is a foundational building block of effective information management and it's arguably the most underdeveloped block.

A story in the April issue of Health Data Management by Linda L. Kloss, president at Kloss Strategic Advisors in Chicago, examines the processes--framing the challenge, scoping the issues and practical steps--of improving data quality.

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