Algorithms show potential in measuring diagnostic errors using big data

Greg Slabodkin at Information Management: “While the problem of diagnostic errors is widespread in medicine, with an estimated 12 million Americans affected annually, a new approach to quantifying and monitoring these errors has the potential to prevent serious patient injuries, including disability or death.

“The single biggest impediment to making progress is the lack of operational measures of diagnostic errors,” says David Newman-Toker, MD, director of the Johns Hopkins Armstrong Institute Center for Diagnostic Excellence. “It’s very difficult to measure because we haven’t had the tools to look for it in a systematic way. And most of the methods that look for diagnostics errors involve training people to do labor-intensive chart reviews.”

However, a new method—called the Symptom-Disease Pair Analysis of Diagnostic Error (SPADE)—uncovers misdiagnosis-related harms using specific algorithms and big data. The automated approach could replace labor-intensive reviews of medical records by hospital staff, which researchers contend are limited by poor clinical documentation, low reliability and inherent bias.

According to Newman-Toker, SPADE utilizes statistical analyses to identify critical patterns that measure the rate of diagnostic error by analyzing large, existing clinical and claims datasets containing hundreds of thousands of patient visits. Specifically, algorithms are leveraged to look for common symptoms prompting a physician visit and then pairing them with one or more diseases that could be misdiagnosed in those clinical contexts….(More)”.