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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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Human-Machine Collaborative Optimization via Apprenticeship Scheduling

  • February 1, 2017

This thesis project—Human-Machine Collaborative Optimization via Apprenticeship Scheduling—was co-funded by CRICO and submitted to the Department of Aeronautics and Astronautics at Massachusetts Institute of Technology (MIT).


Thesis author Matthew C. Gombolay develops a novel computational technique, Collaborative Optimization Via Apprenticeship Scheduling (COVAS) that enables robots to learn a policy to capture an expert’s knowledge by observing the expert solve scheduling problems. CRICO’s support for his project was through a grant awarded to Neel Shah, MD, of Beth Israel Deaconess Medical Center, and his collaborative work with MIT in finding ways to aid clinicians in making the best OB/Gyn delivery decisions.  

Citation for the Full-text Article

Gombolay MC. Human-machine collaborative optimization via apprenticeship scheduling [thesis]. Cambridge, MA: Department of Aeronautics and Astronautics, Massachusetts Institute of Technology; 2017.

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Money for Safety: CRICO Pushes Hard to Prevent Medical Harm.