Calit2
Gov. Gray
Davis
Institute
for Science
and
Innovation
Calit2: Path Forward
The Digital Transformation of
Health • California is a Microcosm of American Health Care
Challenges
– Increasing Chronic Illness as Population Ages
– Skyrocketing Health Care Costs & Tight Budgets
• Digital Transformation of Health Care is Underway
– Explosion of Health Care Data
– Increased Data from Biomedical Devices
– Greater Genomic and Proteomic Understanding
• New Generation of Health Care Will Focus on
Wellness
– Increased Focus on Children’s Health
– Real Time Monitoring for Preventive Intervention and
Behavioral Modification to Enhance Wellness
• Calit2 will Lead Collaborations with Engineers,
Clinicians, Physicians, and Social Science to
Prototype the Infrastructure Necessary to Realize
This Vision
IT in the Doctor’s Office
4
Timeline-Based Visualization of Patient-Provider
Interaction Patterns from Coded Video
Alan Calvitti, PhD,1 Zia Agha, MD, MS,1 Debra Roter, PhD,2 Barbara
Gray, MA,1 Neil Farber, MD,1, Danielle Zuest, MA1
HSRD VA San Diego Healthcare System & Dept. of Med. UCSD.1
Dept. Health Policy and Management, Johns Hopkins School of
Public Health, Baltimore, MD.2
CureTogether -IT for Selfhelp
5
QUALCOMM Life
LONOCLOUD Proprietary & Confidential
LonoCloud Federation of Nodes for Smart and Connected Healthcare
7
Nationwide
Network of
Patient Sensors
Data Collection
& Ingest Lono Nodes
Patient Self-Service
Reporting Portal
Electronic Medical Records
Patient History
Lab Data
Patient Genetic Information
Data Fabric &
Analytics
Processing
Clinical
Researchers
Cloud Monitoring
Dashboard
Data Fabric &
Reporting
Database Gateway
Integration to Fabric
Physicians &
Hospitals
On-Premise Data &
Reporting & Analysis
Lono Edge Node
Operations Center
Policy Makers
Data Fabric &
Reporting
Tom Caldwell, Ingolf Krueger
www.lonocloud.com
LONOCLOUD Proprietary & Confidential
Remote sensors Advanced Monitoring systems provide: 1. Wireless or remote patient monitoring to
share data outside the immediate patient care area.
2. Features: Basic Remote Tracking 3. Face to Face Interaction Patient – Clinician 4. Data sorting vast amounts placed in context of patient conditions
LonoCloud for mHealth – the future is here
LonoCloud
8
Tom Caldwell, Ingolf Krueger
www.lonocloud.com
Sensing Devices and Systems
As worn
Gas exchange
monitors
ECG Holter
monitor
Accelerometers
Suunto: Heart
Rate monitor,
foot pod,
temperature and
altitude
Polar: Heart
Rate, stride
and cadence
sensors and
GPS
GoWear: Calorie
burn, Galvanic
Skin Response,
near and far skin
temperatures,
step sensors
RF ID
Algorithmically Derived Metrics
• Excess Post Exercise Oxygen
Consumption
– Disruption of Homeostasis,
Training Effect
• VO2 max
– Adaptation to endurance
• Polar Running Index
– Race completion times
• Heart-Breath Synchronicity
– Relaxation and Meditation
• ZQ
– Quality of Sleep
• FirstBeat athlete
– Stress and Recovery
Refining a Run
Empty Stomach
Mashed Potatoes
Energy Gels
Endura Optimizer
Gait Analysis: April 17th, 2010
13
Long term trends
A finer look at Yoga
And Music?
16
Ra Ma Da Sa Sa Say So Hung
Monkey Brain
Akaal Hari. Apaal Hari…
Entrainment (N=2)
17
Wavelet coherence
N signals Wavelet coherence
Coherence Network connections
• A3: Shoulder shrugs (2 minutes), LF and HF
Coherence Network connections
• Interlude, LF and HF
Coherence Network connections
• A4: Pressure against nose bridge (3.5 minutes),
• LF and HF
Coherence Network connections
• Interlude, LF and HF
Coherence Network connections
• Act 5: Ek Ong Kar, Sat Nam Siri, Whahay Guru
• 11 min.
• LF and HF
Coherence Network connections
• Interlude, LF and HF
Coherence Network connections
• Act. 7: Ek Ong Kar, Sat Gurprasad, Sat Gurprasad, Ek Ong Kar
• 11 minutes
• LF and HF
Real Time
healthware.ucsd.edu
Estimating (Heart) Age
29
Relations between age and HRV determined by
SDNN index (A), rMSSD (B) and pNN50 (C) in
healthy subjects.
• Solid lines = fitted regression lines and
upper and lower 95% confidence limits.
• Dashed lines = published cutpoints for
increased risk of mortality (SDNN index 30
ms, rMSSD 15 ms, pNN50 0.75%).
"Twenty-Four Hour Time Domain Heart Rate
Variability and Heart Rate: Relations to Age
and Gender Over Nine Decades,"
K Umetani, DH Singer, R McCraty, and M
Atkinson, J. Am. Coll. Cardiol. 1998;31;593-
601
• Yoga and Meditation
– Sensing the heart beat
– Sensing pulsations of
blood flow
– Ento-optic patterns
– Sensing electrical
discharge
Sensations to Sense (Algorithmically?)
• Running
– Patterns of Breath
– Shift in Gait
– Runny Nose
– Retching
– Loss of circulation and
numbness in (left) arm
– Knees buckle
30
SenseWear™ activity monitor (3-axis accelerometry, skin temp, GSR, and more), 23 hrs/day
Polar Team™ heart rate monitor, 12-23 hrs/day
Philips-Respironics Actiwatch™, 12-23 hrs/day
Dexcom 7+™ continuous glucose monitor, 24 hrs/day (research CGM had “blinded” display; this subject also wore his own CGM during the study)
Insulin pump, 24 hrs/day (subjects wore their own pumps, in this case OmniPod™ with site on lower back, pictured)
Zeo™ sleep monitor headband, worn from bedtime to waking
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6/20/2011 19:12 6/21/2011 0:00 6/21/2011 4:48 6/21/2011 9:36 6/21/2011 14:24 6/21/2011 19:12 6/22/2011 0:00
BG
BPM
Bolus
Snack
Meal
METs
DMITRI pilot study: Nate Heintzman
Calit2: At the Cutting Edge
Ramesh Rao,
Director,
UCSD Division
Calit2
Professor of
Electrical and
Computer
Engineering
Jacobs School of
Engineering