News
Machine-Learning System Could Aid Critical Decisions in Sepsis Care
Nov 07, 2018
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.
Latest News from CRICO
Get all your medmal and patient safety news here.
Communication Failures Linked to 1,744 Deaths in Five Years, US Malpractice Study Finds
News
STAT News requested exclusive rights to release the first feature article related to CRICO's 2015 Annual Benchmarking Report | Malpractice Risks in Communication Failures.
Multisource Feedback Process Helps Surgeons Assess and Improve Teamwork Skills
News
In Research and Publication
The Journal of American College of Surgeons study reports that a multisource evaluation tool used in other industries is accurate and well-accepted by surgical teams.
The Journal of American College of Surgeons study reports that a multisource evaluation tool used in other industries is accurate and well-accepted by surgical teams.
In the Wake of a New Report on Diagnostic Errors SIDM Invites Collaboration and Policy Action
News
A new report by CRICO and Johns Hopkins Armstrong Institute Center for Diagnostic Excellence provides the first national estimate of permanent morbidity and mortality resulting from diagnostic errors across all clinical settings. The Society to Improve Diagnosis in Medicine (SIDM) works to raise awareness of the burden of diagnostic error as a major public health issue and calls for collaboration and policy action on the issue.
Robot helps nurses schedule tasks on labor floor
News
A CRICO grant helped provide research support for a new system developed by Massachusetts Institute of Technology to assist hospital staff in scheduling room assignments and suggesting which nurses to assign to patients for C-sections and other procedures.