This document summarizes the work of Ashwin Belle on using data science and machine learning techniques to develop clinical decision support tools. It discusses collecting vast amounts of patient data from monitors, pumps, ventilators and other sources to predict patient deterioration. One such tool, called AHI, analyzes hemodynamic instability and has so far correctly predicted issues requiring rapid response teams from 20 minutes to 38 hours in advance based on validation of 22 cases. Another tool called Barreleye is being developed to support care of traumatic brain injury patients. The document emphasizes moving research from the lab to actual clinical use to improve patient outcomes.

1. SMART(er) MEDICINE
Ashwin Belle, PhD
Data Scientist, Analytics Architect, MCIRCC
Assistant Research Scientist, Department of Emergency Medicine
Michigan Center for Integrative Research in Critical Care
University of Michigan

2. Disclosures
• Some of the technologies in this presentation is under
active consideration for licensing by Trove Analytics,
Inc.
• Co-Founder of Trove Analytics, Inc.

3. MEDICINE DATA SCIENCE

4. MICHIGAN CENTER FOR INTEGRATIVE
RESEARCH IN CRITICAL CARE

5. Rapid Progression
Through
Echelons of Care

6. Situational Awareness: Volume + Variety + Velocity + Voracity
Pumps:
• Medications,
• Amounts
• & Infusion Rates
Monitors:
• ECG
• Arterial Pressure
• Intracranial Pressure
• Cardiac Output
• Pulse Oximetry
• CVP
• EEG
• Other Waveforms Ventilator Data:
• Pressure & Volume
• Waveforms/Loops
• Endtidal CO2
• Airway Pressures
• Oxygen Needs
• Nitric Oxide
• Urine Output
• Imaging • Lab Data • Medical Record • Pharmacy • Blood
Up to 200 time-related variables
Most can only handle two
unassisted

7. Sampling Rate vs. Predictability
The sampling rate and volume of data in an experiment is directly
proportional to the predictability of the next experiment
https://www.sigmod.org/2013/keynote1_slides.pdf, http://www.computerworlduk.com/data/boeing-787s-create-half-terabyte-of-data-per-flight-says-virgin-atlantic-3433595/
1
0.5
0.0001
Data Size
Data Collected (TB)
Stock Trader Boieng 747 Patient
/Day /Flight /Patient

10. Research Pipeline
Waveform
Clinical Records
Harmonize
Annotation View
Research View
Clinical Decision
Support System
Clinical Decision
Support System
New Patients Physicians

11. Michigan
Medicine
Community
Hospitals

12. Cloud

14. AHI
Analytics for Hemodynamics Instability

15. <iframe width="560" height="315" src="https://www.youtube.com/embed/hvoHOleaIjk?rel=0&amp;start=396;end=420" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen></iframe>

16. Data
AHI Current Lead-Time Validation Cohort
Retrospective cases of Rapid Response Team (RRT) and code calls,
which require high acuity response team of health care providers
that responds to hospitalized patients with signs of deterioration
on patients to prevent respiratory or cardiac arrest.
Currently working with UM’s clinicians to validate AHI’s
performance. 22 case reviews have been completed so far,
with a goal of completing 40-50.
In 100% of the 22 cases, AHI correctly predicted either
hemodynamic instability (HI) arising from several different
causes or no HI from RRT call causes such as self-extubation or
routine ventilator adjustment settings.
In other words, 13 cases of predicted HI (red) from 20 min to 38
hours in advance; 9 cases where AHI correctly stayed green.
Zero cases where AHI didn’t get the right answer.

17. Barreleye©
A decision support tool for TBI care

19. Bench to Bedside

20. THANK YOU