1. “Risky States”
Optimizing ICU Safety Through Patient Engagement, System Science and Information Technology
Beth Israel Deaconess Medical Center; MIT; Aptima
Abstract
Methods
“ICU Intensity Index” Dashboard
Jason
I will be adding a live link under the Intensity Index I may also take the Environmental predictors and the Harm predictors and have them as powerpoint links.
We used existing data and expert analysis to identify fundamental risky states (risk events/event drivers) in the ICU environment, system and people in the system that impact the likelihood of risky events or actual harm to occur.
We identified corresponding harm outcomes during and following these “risky states” and will use them to inform the development of new holistic mitigation strategies
Identified drivers through retrospective review of root cause analyses
Identified harms (voluntary reports, billing data, IHI ICU global triggers tool
Analyzed 2 years of retrospective data for all ICU patients 2012 – 2014
Objective
Acquired and reviewed 2 years of retrospective data
Analyzed retrospective data and develop model
Designed application to display real time information to ICU staff about risky level
Conclusions
It was impossible to create a working list of MD’s and RN’s assigned to care for each ICU patient at any point in time from existing electronic data sources
It will be important to capture “drivers” that we have not considered in the first analysis of retrospective data
Results
Team
Collaborators
Funded by the generosity of the
Gordon and Betty Moore Foundation

4. Moderate Risk State

5. High Risk State

6. Add a Patient Concern

7. Add a Unit Concern