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January 5, 2022 To Measure and Reduce Diagnostic Error, Start With the Data You Have

This article, published by the Michigan State Medical Society, provides insight into how CRICO's diagnostic process of care framework, using medical malpractice claims data, can be used to reduce diagnostic errors.

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January 4, 2022 Journal of Patient Safety Study Shows I-PASS Can Significantly Decrease Likelihood and Cost of Malpractice Claims
December 15, 2021 Frequency and Nature of Communication and Handoff Failures in Medical Malpractice Claims

Using Candello data, this study examines the characteristics of malpractice claims which miscommunications.

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Machine-Learning System Could Aid Critical Decisions in Sepsis Care

  • November 7, 2018

Funded in part by CRICO, this study enabled researchers from Massachusetts General Hospital (MGH) and Massachusetts Institute of Technology (MIT) to develop a predictive model to help guide clinicians in deciding when to give potentially life-saving sepsis treatment to patients being treated in the emergency room (ER).


Research shows that sepsis is one of the most frequent causes of hospital admission and one of the most common causes of death in the intensive care unit (ICU) with the ER most often the first point of  contact with the sepsis patient. This first-ever model, developed by the MGH/MIT team to specifically aid ER clinicians in sepsis care, aims to result in better outcomes for patients.

 

Citation for the Full-text Article

Matheson R. Machine-learning system could aid critical decisions in sepsis care. MIT News. November 7, 2018.