Developing the “science of error measurement”

I woke up this morning to the People’s Pharmacy on NPR and an interview with Peter Pronovost (of checklist fame in a previous post) and David Newman-Toker. These two M.D.s hope to inspire research into accurate error measurement as an essential to developing systems that avoid errors in medical diagnosis. The goal of their commentary, published in JAMA, is to bring attention to the field of diagnosis.

The essence of the problem is that diagnosis errors are prevalent and not only result in ineffective treatment for a condition that is not present, but that either the lack of treatment of the real condition or the side-effects of treatment of the incorrectly diagnosed condition result in actual harm. This harm is one of the things that we do not yet measure well.

Once we fully understand the errors, we can develop effective decision aids that function and are accepted in the medical context.

How to reduce diagnostic errors (from the article):

  • Develop Systems Solutions to Cognitive Problems
  • Create Actionable Categories of Errors Based on Context Rather Than Cause
  • Emphasize Misdiagnosis-Related Harm Rather Than Diagnostic Error
  • Build Workflow-Sensitive Solutions
  • Focus on Comparative and Cost-effectiveness

Here are my favorite excerpts from the article that point to potential human factors solutions.

  • Look to other areas for information

“Parallels in medication safety offer an alternative view. If the problem is illegible physician handwriting in medical prescriptions, the most efficient solution is probably not handwriting retraining but computer-based prescription writing. Likewise, if the problem is a cognitive bias such as a tendency to overestimate the probability of a rare diagnosis recently encountered, the most efficient solution might not be cognitive debiasing training for all physicians but computer-based decision support systems that provide accurate estimates of disease probability.”

  • Collect data

“Systematically recording key clinical inputs (symptoms/signs/tests) and outputs (morbidity/mortality/costs) would also offer a platform for continuous quality improvement through structured feedback…”

Link to the primary source:

Newman-Toker, D.E. & Provanost, P. (2009). Diagnostic errors: The next frontier for patient safety. JAMA, 301 (10), 1060-1062.

3 thoughts on “Developing the “science of error measurement””

  1. I think the Health Care industry has been far too slow to embrace Human Factors and Usability Engineering techniques. Problems can be solved quickly and effectively with a focus on the development of user interfaces applying Human Factors techniques. FDA has been trying to get medical device manufacturers to do more as shown in this video:

    Most hospitals have also been slow to embrace, but a like this are aware:

    The field of Human Factors Engineering has a Public Relations (PR) problem in my view…

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