How medical devices help fill EHRs with clinically useful data for comparative effectiveness research and data interoperability. This talk was given at the IEEE Baltimore Section EMB Society

1. Connected Medical Devices
How medical devices help fill EHRs with clinically
useful data for comparative effectiveness research
and data interoperability
Shahid N. Shah, CEO

2. Who is Shahid?
• 20+ years of software engineering and
multi-site healthcare system deployment
experience
• 12+ years of healthcare IT and medical
devices experience (blog at
http://healthcareguy.com)
• 15+ years of technology management
experience (government, non-profit,
commercial)
• 10+ years as architect, engineer, and
implementation manager on various EMR
and EHR initiatives (commercial and nonprofit)
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Author of Chapter 13,
“You’re the CIO of your Own
2
Office”

3. What’s this talk about?
Health IT / MedTech Landscape
Key Takeaways
• Data has potential to solve
some hard healthcare
problems and change how
medical science is done.
• The government is paying for
the collection of clinical data
(Meaningful Use or “MU”).
• All the existing MU incentives
promote the wrong kinds of
data collection: unreliable,
slow, and error prone.
• Medical devices are the best
sources of quantifiable,
analyzable, and reportable
clinical data.
• New devices must be
designed and deployed to
support inherent connectivity.
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4. What problems can data help solve?
Cost per patient per
procedure / treatment
going up but without
ability to explain why
Cost for same
procedure / treatment
plan highly variable
across localities
Unable to compare
drug efficacy across
patient populations
Unable to compare
health treatment
effectiveness across
patients
Variability in fees and
treatments promotes
fraud
Lack of visibility of
entire patient record
causes medical errors
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5. Data changes the questions we ask
Simple visual facts
Complex visual facts
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Complex computable
facts
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6. Data can change medical science
The old way
The new way
Identify problem
Identify data
Ask questions
Generate questions
Collect data
Mine data
Answer questions
Answer questions
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7. Evidence-based medicine is our goal
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8. Evidence-based Medicine and
Comparative Effectiveness Research
Early 1970s
1978
1990’s
Today
Medical
Technology
Assessment
(MTA)
National
Center for
Health
Technology
Assessment
Agency for
Healthcare
Research and
Quality
(AHRQ)
Comparative
Effective
Research
(CER)
Success factor: large well-designed effectiveness studies with mountains of data
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9. AHRQ’s definition of CER process
Identify new and
emerging clinical
interventions.
Review and synthesize
current medical
research.
Identify gaps between
existing medical
research and the needs
of clinical practice.
Translate and
disseminate research
findings to diverse
stakeholders.
Train and develop
clinical researchers.
Promote and generate
new scientific evidence
and analytic tools.
Reach out to
stakeholders via a
citizens forum.
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Source: http://effectivehealthcare.ahrq.gov/index.cfm/what-is-comparative-effectiveness-research1/
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10. CER is about the patient
• CER sounds like it’s all about the government
and evidence-based medicine to contain
healthcare costs but ultimately it’s about
providing treatment comparison choices to
help make informed decisions.
• Healthcare professionals must deliver tools to
the patient that can help the patient and
their families select the right treatment
options.
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11. Healthcare landscape background
• The government (through Meaningful Use &
ACO incentives) is paying for the collection of
clinical data.
• Medical devices are the best sources of
quantifiable, analyzable, and reportable clinical
data.
• Most medical devices today are not connected
so you do not have access to the best data.
• New devices are being design and deployed to
support connectivity.
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12. What if we had access to all this data?
Source: Jan Whittenber, Philips Medical Systems
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13. Unstructured patient data sources
Patient
Source
Self reported
by patient
Health
Professional
Observation
s by HCP
Labs &
Diagnostics
Computed
from
specimens
Errors
High
Medium
Slow
Slow
Low
Medium
Megabytes
Megabytes
PDFs,
images
PDFs,
images
Common
Common
Common
Uncommon
PDFs,
images
Availability
Uncommon
Megabytes
Data type
Computed
from
specimens
High
Data size
Computed
real-time
from patient
Medium
Reliability
Biomarkers /
Genetics
Low
Time
Medical
Devices
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14. Structured patient data sources
Patient
Source
Self reported
by patient
Health
Professional
Observations
by HCP
Labs &
Diagnostics
Specimens
Medical
Devices
Real-time
from patient
Biomarkers /
Genetics
Specimens
Errors
High
Medium
Low
Low
Low
Time
Slow
Slow
Medium
Fast
Slow
Reliability
Low
Medium
High
High
High
Kilobytes
Kilobytes
Kilobytes
Megabytes
Gigabytes
Gigabytes
Gigabytes
Discrete size
Streaming
size
Availability
Uncommon
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Common
Somewhat
Common
Uncommon Uncommon
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15. The need for connected devices
• Meaningful Use and CER advocates are
promoting (structured) data collection for
reduction of medical errors, analysis of
treatments and procedures, and research for
new methods.
• All the existing MU incentives promote the
wrong kinds of collection: unreliable, slow, and
error prone.
• Accurate, real-time, data is only available from
connected medical devices
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16. Medical Device Connectivity is a must
Most obvious benefit
Least attention
Most promising
capability
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This talk focuses on
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connected devices

17. It’s not as hard as we think…
• Modern real-time operating systems (open
source and commercial) are reliable for
safety-critical medical-grade requirements.
• Open standards such as TCP/IP DDS, HTTP
,
,
and XMPP can pull vendors out of the 1980’s
and into the 1990’s. 
• Open source and open standards that
promote enterprise IT connectivity can pull
vendors into the 2010’s and beyond.
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18. Sampling of OSS / open standards
Comments
Project / Standard
Subject area
D
G
Linux or Android
Operating system


OMG DDS (data
distribution service)
Publish and subscribe
messaging


AppWeb, Apache
Web/app server


OpenTSDB
Time series database

Open source project
Mirth
HL7 messaging engine

Built on Mule ESB
Alembic Aurion
HIE, message
exchange

Successor to CONNECT
HTML5, XMPP, JSON
Various areas


Don’t reinvent the wheel
SAML, XACML
Security and privacy


DynObj, OSGi, JPF
Plugin frameworks


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Open standard with open
source implementations
Build for extensibility
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19. Ask for device connectivity
Physical
• Wired, wireless (WiFi, cellular, etc.)
Logical
• Device  Gateway  Data Routers  Systems
Structural
• Security, Numbers, Units of Measure, etc.
Semantic
• Presence, Vitals, Glucose, Heartbeats, etc.
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20. Ask for better manageability
Security
• Is the device
authorized?
Teaming
Inventory
• Device grouping
• Where is the device?
Presence
• Is a device
connected?
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21. Ask for extensible devices
Legacy
Devices
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Future
Devices
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22. Appreciate tradeoffs
Integrationfriendliness
The more connectionfriendly a device, the
harder it is to validate it
Ease of
validation
Lesson: Demand Testability
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23. Ultimate Connectivity Architecture
5
Device Components
Sensors
Storage
Display
3rd Party Plugins
Web Server, IM Client
• Presence
6
• Messaging
App
#1
• Registration
• JDBC, Query
Plugins
App
#2
4 Connectivity Layer (DDS, HTTP, XMPP)
3 Plugin Container
2 Security and Management Layer
1
Device OS
Event Architecture
Location
Aware
7
(QNX, Linux, Windows)
SSL VPN
Healthcare Enterprise
8
Patient Context
Device Gateway (DDS, ESB)
Inventory
Workflow
Notifications
Cloud
Services
Data Transformation (ESB, HL7)
Management
Dashboards
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Enterprise
Data
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24. Ultimate Architecture Core
Connectivity is
built-in, not added
Device Components
Think about
Plugins from day 1
Connectivity Layer (DDS, HTTP, XMPP)
Plugin Container
Security and Management Layer
Device OS
(QNX, Linux, Windows)
Don’t create
your own OS!
Build on
Open Source
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Security isn’t
added later
Create code as
a last resort
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25. Connectivity components
Surveillance &
“remote display”
Remote Access
Alarms
Device Components
Event Viewer
Design all
functions as
plugins
Web Server, IM Client
• Presence
• Messaging
• Registration
• JDBC, Query
Connectivity Layer (DDS, HTTP, XMPP)
Plugin Container
Device OS
(QNX, Linux, Windows)
Security and Management Layer
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26. OSS enables enterprise integration
Device
Teaming
Cloud
Services
SSL VPN
Device Gateway
(DDS, XMPP, ESB)
Patient Context
Monitoring
Device
Data
Cross Device
App Workflows
Data Transformation (ESB, HL7)
Remote
Surveillance
Management
Dashboards
HIT
Integration
Report
Generation
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Enterprise
Data
Alarm
Notifications
Device
Management
Inventory
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27. Ultimate Connectivity Architecture
Device Components
Sensors
Storage
Event Architecture
Location
Aware
Display
3rd Party Plugins
Web Server, IM Client
• Presence
• Messaging
• Registration
• JDBC, Query
Plugins
App
#1
App
#2
Connectivity Layer (DDS, HTTP, XMPP)
Plugin Container
Security and Management Layer
Device OS
(QNX, Linux, Windows)
SSL VPN
Healthcare Enterprise
Device Gateway (DDS, ESB)
Patient Context
Inventory
Workflow
Notifications
Cloud
Services
Data Transformation (ESB, HL7)
Management
Dashboards
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Enterprise
Data
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28. Conclusion and Questions