Reducing Harm from
Diagnostic Error

  • 1 in 10 diagnoses are incorrect.
  • Diagnostic error accounts for 40,000-80,000 US deaths annually—somewhere between breast cancer and diabetes.
  • Chances are, we will all experience diagnostic error in our lifetime.

(US Institute of Medicine 2015, BMJ Quality & Safety 25-Year Summary of US Malpractice Claims, 2013.)

If these numbers surprise you, then come join us in generating solutions that turn the numbers around.

 

 

       
 

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SIDM's Bi-Monthly Newsletter

SIDM Journal Club

The Journal Club focuses on a recent publication of interest in the diagnostic error field and is an opportunity to engage in research-related interactive discussions.

Next Journal Club -

February 13th, 2018, 3:00PM ET

Dr. Ziad Obermeyer: will discuss “Early death after discharge from emergency departments: Analysis of national US insurance claims data” and a work in progress: "How good is clinical judgment? An analysis of testing decisions for pulmonary embolism 5 using electronic records and national Medicare claims."

Ziad Obermeyer is an Assistant Professor at Harvard Medical School and an emergency physician at the Brigham and Women's Hospital.

His lab applies machine learning to solve clinical problems. As patients age and medical technology advances, the complexity of health data strains the capabilities of the human mind. Using a combination of machine learning and traditional methods, his work seeks to find hidden signal in health data, help doctors make better decisions, and drive innovations in clinical research.

He is a recipient of multiple research awards from NIH (including the Office of the Director and the National Institute on Aging) and private foundations, and a faculty affiliate at ideas42, Ariadne Labs, and the Harvard Institute for Quantitative Social Science. He holds an A.B. (magna cum laude) from Harvard and an M.Phil. from Cambridge, and worked as a consultant at McKinsey & Co. in Geneva, New Jersey, and Tokyo, before returning to Harvard for his M.D (magna cum laude).