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March 1, 2022 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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March 1, 2022 Improving Patient Handoffs Helps Reduce Malpractice Claims

Healthcare Risk Management reports on a large study conducted by Boston Children’s Hospital in which researchers reviewed 498 medical malpractice claims provided by Candello, CRICO’s national medical malpractice collaborative. The work revealed a direct relationship between the quality of patient handoffs and claims.

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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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Development and Validation of a Deep Learning Model for Detection of Allergic Reactions Using Safety Event Reports Across Hospitals

  • November 16, 2020

This study, funded by CRICO grants, utilized a deep learning algorithm to identify instances of allergic reactions in the free-text electronic narratives of hospital safety reports.


By applying a deep learning algorithm to over 299,028 safety reports filed at Brigham and Women’s Hospital and Massachusetts General Hospital, researchers were able to identify patient allergic reactions in electronic medical records with greater frequency and accuracy than a keyword search approach. The algorithm was able to identify 24.2% more cases of confirmed allergic reactions and reduced the need for manual review by 63.8%.

The findings suggest that similar deep learning models may improve the accuracy and efficiency in identifying allergic reactions in hospitals, which could greatly benefit surveillance and guidance for medical errors and system improvement.

 

Citation for the Full-text Article

Yang J, Wang L, Phadke NA, et al. Development and validation of a deep learning model for detection of allergic reactions using safety event reports across hospitals. JAMA Network Open. 2020;3(11):e2022836. DOI:10.1001/jamanetworkopen.2020.22836