Researchers propose method for counting health IT errors

A pair of Texas informatics researchers has come up with a classification scheme for categorizing—and ultimately counting—errors involving health information technology.

Their proposal is outlined in a four-page article, "Defining Health Information Technology-Related Errors: New Developments Since 'To Err is Human'" that appears in the July 15 issue of the Archives of Internal Medicine. It references the totemic 1999 study on medical errors, "To Err is Human," by the Institute of Medicine.

The authors of the article are Dean Sittig, a professor in biomedical informatics at the University of Texas Health Science Center, Houston; and Dr. Hardeep Singh, an assistant professor of medicine at the Michael E. DeBakey Veterans Affairs Medical Center, Houston, and Baylor College of Medicine. Sittig is an adjunct associate professor at the Baylor College of Medicine and the founding editor of The Informatics Review, the electronic journal of the Association of Medical Directors of Information Systems. Singh also serves as the chief of the health policy and quality program at the Houston VA's Health Services Research and Development Center of Excellence.

Sittig, in a telephone interview, said the idea for the classification scheme came after he testified about IT-related errors before an IOM panel in December. A fellow witness also spoke about medication errors. Afterward, Sittig said he was approached by an IOM person who asked whether a definition existed for EHR errors. "We decided there really wasn't any," Sittig said, which led to their research and this report.

"There are a lot of people who don't seem to understand when their EHR is responsible for something that happens," he said. "This is what we think constitutes an electronic health-record system error. By creating this definition, people can start to realize we have a problem. We're trying to heighten people's awareness of these things so they come to mind when it happens."

First, Sittig and Singh report as established fact that health IT-linked errors are real, citing 19 types of actual errors—many gleaned from their literature search and others from the authors' own knowledge. The error types are listed in a table that also presents a "sociotechnical model" for health IT evaluation and use that provides "an origin-specific typology for HIT errors."

The table lists eight specific error-producing categories, ranging from simple hardware or software "bugs" to errors stemming from the inability of a system or organization to accurately measure and monitor such functions as “system availability, use, effectiveness and the unintended consequences of system use."

Each category in the table has two or three examples of errors drawn from the list of 19, and each category also has its own examples of one or two preventive measures to reduce the likelihood that these errors will recur.

Sittig said he likes the recommendation introduced by the National Research Council last week that the U.S. Food and Drug Administration should make it easier for laypeople to report problems with their home medical devices and then use the data collected to help determine root causes of any adverse events linked to use of the devices.

Sittig said the eight categories of errors he and Singh have come up with could be used as a framework for setting up a similar error reporting program for provider HIT systems. However it's done, end users need access to the information any such system might gather, he said.

"A problem with a lot of these reporting systems (is) it's not clear who gets the data and who gets to use it," Sittig said. "People need to log on and read about these errors reports."