Critical Care Informatics

Massachusetts Institute Of Technology, Cambridge MA

Investigators

Linked publications & trials

Abstract

Abstract This is a renewal application for the Critical Care Informatics grant (NIBIB R01 EB017205) that was awarded to the Laboratory for Computational Physiology (LCP) in 2014. This grant has supported the development of the Medical Information Mart for Intensive Care (MIMIC) research database, which is a de-identified database for research and health data science education around the world. We aim to address the foremost issues in machine learning in healthcare today, focusing on health disparities, algorithmic bias, and understanding the barriers to effective and equitable implementation of algorithmic models. Our proposal will enrich MIMIC with new data types and sources, including publicly available population health datasets, advance progress on a federated critical care dataset, and add a new module with COVID-19 specific ontology and codes. Our research will build on prior findings showing the pervasiveness of hidden socio-demographic bias in data sources including clinical data, medical images, and narrative patient documentation. Health care data science ultimately exists for the purpose of improving human health. Yet, extremely few models published in research papers have impacted clinical care due to challenges in implementation. In layman’s terms, this means knowledge gained about tests and treatments leads to the best possible outcome for every patient in the intensive care unit (ICU) regardless of demographic. We will conduct a rigorous qualitative research study to better understand the barriers faced by key stakeholders - clinicians and data scientists - in the development and implementation of equity-centered artificial intelligence. This information will be used to develop guidelines to integrate with implementation science frameworks to support the effective implementation of equitable AI in clinical settings. With these aims, MIMIC will significantly expand the relevance of this research resource to a greater diversity of investigators including those in the social and behavioral sciences and public health and continue to be a resource for clinical research and increasingly sophisticated model development, advancing our understanding of critical issues of fairness and equity in healthcare, data science, and the broader society.