Call for Papers
About the Journal
Editorial Board
Publication Ethics
Instructions for Authors
Current Issue
Back Issues
Search for Articles
Search for Articles

JCSE, vol. 6, no. 1, pp.51-59, 2012


Collective Experience: A Database-Fuelled, Inter-Disciplinary Team-Led Learning System

Leo A. Celi, Roger G. Mark, Joon Lee, Daniel J. Scott, Trishan Panch
Harvard-MIT Division of Health Sciences and Technology, Cambridge, and Beth Israel Deaconess Medical Center, Boston, MA, USA/ Harvard-MIT Division of Health Sciences and Technology, Cambridge, MA, USA

Abstract: We describe the framework of a data-fuelled, interdisciplinary team-led learning system. The idea is to build models by using patients from one???own institution whose features are similar to an index patient as regards an outcome of interest. This is done in order to predict the utility of the diagnostic tests and interventions as well as to inform prognosis. The Laboratory of Computational Physiology at the Massachusetts Institute of Technology developed MIMIC-II and maintains it, a public de-identified high- resolution database of patients admitted to Beth Israel Deaconess Medical Center. It hosts teams of clinicians (nurses, doctors, pharmacists) and scientists (database engineers, modelers, epidemiologists) who translate the day-to-day questions during rounds that have no clear answers in the current medical literature into study designs, perform the modeling and analysis. Then, they publish their findings. The studies fall into the following broad categories: identification and interrogation of practice variation, predictive modeling of clinical outcomes within the patient subsets and comparative effectiveness research on diagnostic tests and therapeutic interventions. Clinical databases such as MIMIC-II, where recorded health care transactions - clinical decisions linked with patient outcomes - are constantly uploaded. They become the centerpiece of a learning system.

Keyword: Collective experience; Intensive care; Electronic medical database; Clinical decision support

Full Paper:   213 Downloads, 5021 View

ⓒ Copyright 2010 KIISE – All Rights Reserved.    
Korean Institute of Information Scientists and Engineers (KIISE)   #401 Meorijae Bldg., 984-1 Bangbae 3-dong, Seo-cho-gu, Seoul 137-849, Korea
Phone: +82-2-588-9240    Fax: +82-2-521-1352    Homepage:    Email: