Building open source safety-critical
medical device platforms and
Meaningful Use EHR gateways Inherent connectivity creates significant
opportunities in medical science
NETSPECTIVE
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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 non-profit)
Author of Chapter 13, “You’re
the CIO of your Own Office”
NETSPECTIVE
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What’s this talk about?
Health IT / MedTech Landscape
• 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.
Key Takeaways
• Medical devices are the best sources of quantifiable, analyzable, and reportable clinical data.
• New devices must be designed and deployed to support inherent connectivity.
• OSS is ideal for next generation and innovative medical devices and gateways
NETSPECTIVE
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What if we had access to all this data?
Source: Jan Whittenber, Philips Medical Systems
• Cardiac output
monitors
• Defibrillators
• Fetal monitors
• Electrocardiographs
• Infant incubators
• Infusion pumps
• Intelligent medical
device hubs
• Interactive infusion
pumps
• MRI machines
• X-Ray machines
• Physiologic monitors
• Ventilators
• Vital signs monitors
NETSPECTIVE
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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
NETSPECTIVE
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Data changes the questions we ask
Simple visual facts Complex visual facts Complex computable
facts
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Data can change medical science
The old way
Identify problem
Ask questions
Collect data
Answer questions
The new way
Identify data
Generate questions
Mine data
Answer questions
NETSPECTIVE
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Evidence-based medicine is our goal
Eminence
• Trust me
Evidence
• Prove it
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Types of medical data we care about
Proteome is our set of proteins expressed by our genome and changes regularly over time Proteomics is the study of the proteome
Proteome
Genotype is the entirety of our hereditary information (DNA, RNA, etc.)
Genetics is the study of the genome
Genome
Phenotype is a composite of our observable characteristics or traits
This is what we’ve been studying for centuries
Phenome
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Unstructured patient data sources Patient Health
Professional
Labs &
Diagnostics
Medical Devices Biomarkers /
Genetics
Source Self reported by
patient
Observations by
HCP
Computed from
specimens
Computed real-
time from patient
Computed from
specimens
Errors High Medium Low
Time Slow Slow Medium
Reliability Low Medium High
Data size Megabytes Megabytes Megabytes
Data type PDFs, images PDFs, images PDFs, images
Availability Common Common Common Uncommon Uncommon
NETSPECTIVE
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Structured patient data sources Patient Health
Professional
Labs &
Diagnostics
Medical Devices Biomarkers /
Genetics
Source Self reported by
patient
Observations by
HCP
Specimens Real-time from
patient
Specimens
Errors High Medium Low Low Low
Time Slow Slow Medium Fast Slow
Reliability Low Medium High High High
Discrete size Kilobytes Kilobytes Kilobytes Megabytes Gigabytes
Streaming size Gigabytes Gigabytes
Availability Uncommon Common Somewhat
Common
Uncommon Uncommon
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Predictions for Device Hardware
Thick Devices Thin Devices Virtual Devices
Sensors Only with Built-in
Wireless
Consumerization of Devices
Sensors on mobile phones,
platforms
NETSPECTIVE
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Predictions for Device Software
Software for algorithms
Software for functionality
Software for connectivity
Software only
Consumerization of Apps
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Predictions for Device Connectivity
Stand-alone and
monolithic
Connectivity within own
organization
Multi-vendor connectivity
System of Systems
(SoS)
Consumerization of IT
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Predictions for Gateways
Single-purpose devices
standalone
Multi-purpose standalone
Multi-purpose with
documentation connectivity
Multi-purpose with cooperating
connectivity
Multi-purpose with analytical
connectivity
Changes in Practice Models
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Predictions for Self-Management
Physicians manage paper
“charts” independently
Physicians and Hospitals
manage paper charts together
Electronic Health Records (EHRs) manage data in
systems
Health Information
Exchange allow coordination
Patients manage their
own data
The Patient is in charge
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Implications
Make sure the patient is in the
middle
Move from hardware to
software focus
Move to algorithms and
analytics
Understand system of
systems (SoS)
Plan for integration and
coordination
Start building simulators
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OSS revolution in device design
Device Components 3rd Party Plugins
App #1
App #2
Security and Management Layer Device OS (QNX, Linux, Windows)
Sensors Storage Display Plugins
Web Server, IM Client
Connectivity Layer (DDS, HTTP, XMPP)
• Presence • Messaging • Registration • JDBC, Query
Plugin Container
Event Architecture
Location Aware
1 2
3
4
5
6
7
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OSS revolution in device capabilities Most obvious benefit Least attention
Most promising
capability
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OSS revolution in Gateways
Corporate Gateway (ESB)
Service
DB
Manag
em
ent
Service
s
Secu
rity Fire
wall
HTTPS, REST, SOAP
SFTP, SCP, HL7, X.12
SMTP, XMPP, DDS
HTTPS, SOAP, REST, HTTP
SFTP, SCP, HL7, X.12
SMTP, XMPP, DDS
Customers & Partners
Apps MQs Services Apps Services
Corporate Cloud (Data Center)
Development
App
DB Central
DB
Registry
Rem
ote
Facilitie
s
VPN
NOTE: Initial design is for a non-federated
backbone. If performance or security
demands require it, a federated solution will
be deployed.
NETSPECTIVE
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OSS revolution in integration
Cloud
Services
Management
Dashboards
Data Transformation (ESB, HL7)
Device Gateway
(DDS, XMPP, ESB)
Enterprise
Data
Inventory
Cross Device
App Workflows
Alarm
Notifications
Patient Context
Monitoring
Device
Teaming
Device
Management Report
Generation
HIT
Integration
Remote
Surveillance
Device
Data
SSL VPN
Patient
Self-Management
Platforms
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OSS revolution in manageability
Security
• Is the device
authorized?
Inventory
• Where is the device?
Presence
• Is a device connected?
Teaming
• Device grouping
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Key OSS questions
Will the FDA accept
open source in
safety-critical
systems?
Is open source safe
enough for medical
devices?
NETSPECTIVE
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Simple answers
Will the FDA accept
OSS in safety-
critical systems?
Is OSS safe enough
for medical
devices?
Yes
Yes but you must prove it
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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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But it’s not easy either…we need
Risk
Assessments Hazard Analysis
Design for
Testability
Design for
Simulations
Documentation Traceability Mathematical
Proofs Determinism
Instrumentation Theoretical
foundations
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OSS / open standards applicability
Project / Standard Subject area D G Comments
Linux or Android Operating system Various distributions
OMG DDS (data distribution
service)
Publish and subscribe
messaging
Open standard with open source
implementations
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 Build for extensibility
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