Chain-of-command approach boosts accuracy

Verify numbers in your database

Although some input errors will inevitably creep into your performance measurement system, the situation isn’t hopeless, experts say. Building in some common-sense safeguards can help you minimize the likelihood of errors — and help you catch the errors that slip through.

Fay A. Rozovsky, JD, of the Rozovsky Group in Richmond, VA, says coding discrepancies can be reduced or eliminated through education, quality oversight, and correction, and having a "chain of command"-style communications process. Such a process helps ensure there’s a rapid-response mechanism in place that lets coders double-check anything they’re uncertain about before the data are submitted.

"Supplemental staff or outsiders doing the numbers can be controlled via education, monitoring, and correction, all through contract," she says. "Some places use coding-clinical teams to review the numbers before data is submitted."

Patrice Spath, ART, consultant in health care quality and resource management with Brown-Spath Associates in Forest Grove, OR, says for each performance measure, your rate is compared to the rate reported by other hospitals. A standard deviation is usually calculated for each indicator, and you are shown where your performance falls in relation to the standard deviation. But when a hospital identifies itself as falling 1.5 or 2 standard deviations from the mean for any performance measure, she suggests taking the following actions:

• Confirm the accuracy of the data submitted to the project coordinator by verifying the numbers in your internal database. Verify the numbers from your abstract sheets or other input documents against the values on the comparative report.

• Review the data definitions for the indicator, and verify that the data you collected complies with the approved definitions found in the project’s data dictionary. Discuss your data definitions with other hospitals in your peer grouping, call the project coordinator to verify your definitions, and discuss the definition with the people who are coding or abstracting the data in your hospital.

If you find data quality problems to be the cause of the variation, Spath says, stop your analysis at this point and resubmit corrected data to the project headquarters (or proceed as advised by the measurement project coordinator). (For further tips on dealing with process deviations, see Spath’s "The Quality-Co$t Connection" column in Hospital Peer Review, January 1999, p. 14.)