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July 1, 2022 Development of a Taxonomy for Characterising Medical Oncology-related Patient Safety and Quality Incidents: A Novel Approach

Using funding from CRICO, this study describes the development of a comprehensive, validated taxonomy for medical oncology-related incidents.

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June 18, 2022 Trial and Error: Learning From Malpractice Claims in Childhood Surgery

Using Candello Solutions by CRICO, this article analyzed the malpractice claims involving patients ≤ 18 years old that occurred from 2008 to 2017.

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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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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.