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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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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.