Calit2

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
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
4
CureTogether -IT for Selfhelp
5
QUALCOMM Life
LonoCloud Federation
of Nodes for Smart
and Connected
Healthcare
Electronic Medical Records
Patient History
Lab Data
Patient Genetic Information
Patient Self-Service
Reporting Portal
Database Gateway
Integration to Fabric
Data Fabric &
Analytics
Processing
Data Fabric &
Reporting
Nationwide
Network of
Patient Sensors
Clinical
Researchers
Data Collection
& Ingest Lono Nodes
Data Fabric &
Reporting
Cloud Monitoring
Dashboard
Operations Center
LONOCLOUD Proprietary &
Confidential
On-Premise Data &
Reporting & Analysis
Lono Edge Node
Policy Makers
Physicians &
Hospitals
Tom Caldwell, Ingolf Krueger
www.lonocloud.com
7
LonoCloud for mHealth – the future is here
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
LONOCLOUD Proprietary &
Confidential
Tom Caldwell, Ingolf Krueger
www.lonocloud.com
8
Sensing Devices and Systems
As worn
Gas exchange
GoWear: Calorie
monitors
burn, Galvanic
Skin Response,
near and far skin
temperatures,
ECG Holter
step sensors
monitor
Suunto: Heart
Rate monitor,
foot pod,
temperature and
altitude
Polar: Heart
Rate, stride
and cadence
sensors and
GPS
Accelerometers
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
Energy Gels
Mashed Potatoes
Endura Optimizer
Gait Analysis: April 17th, 2010
13
Long term trends
A finer look at Yoga
And Music?
Monkey Brain
Ra Ma Da Sa
Sa Say So Hung
Akaal Hari.
Apaal Hari…
16
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
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;593601
29
Sensations to Sense (Algorithmically?)
• Running
–
–
–
–
–
Patterns of Breath
Shift in Gait
Runny Nose
Retching
Loss of circulation and
numbness in (left) arm
– Knees buckle
• Yoga and Meditation
– Sensing the heart beat
– Sensing pulsations of
blood flow
– Ento-optic patterns
– Sensing electrical
discharge
30
Zeo™ sleep monitor
headband, worn from
bedtime to waking
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
Insulin pump, 24 hrs/day
(subjects wore their own pumps, in
this case OmniPod™ with site on
lower back, pictured)
Dexcom 7+™ continuous glucose monitor, 24 hrs/day
(research CGM had “blinded” display; this subject also
wore his own CGM during the study)
DMITRI pilot study: Nate
Heintzman
250
12
10
200
8
BG
150
BPM
6
Bolus
Snack
Meal
100
4
50
2
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6/20/2011 19:12
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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
METs
Calit2: At the Cutting Edge
Ramesh Rao,
Director,
UCSD Division
Calit2
Professor of
Electrical and
Computer
Engineering
Jacobs School of
Engineering