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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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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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