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Harnessing DDS in Next Generation
Healthcare Systems Webcast
November 19, 2014
Webcast Presenters
Julian M. Goldman - Director, MD PnP Program / Director, CIMIT Interoperability Program / Medical Director, Partners HealthCare Biomedical Engineering
Julian M. Goldman, MD is an anesthesiologist at the Massachusetts General Hospital, Medical Director of Biomedical Engineering for Partners HealthCare, and the Director/PI of the Program on Medical Device Interoperability - a multi-institutional federally funded program founded in 2004 to advance medical device interoperability to improve patient safety and HIT innovation.
Dr. Goldman completed his residency and medical informatics fellowship at the University of
Colorado, and served as a Visiting Scholar in the FDA Medical Device Fellowship Program as well as an executive of a pulse oximetry company. His awards include the AAMI Foundation/Institute for Technology in Health Care Clinical Application Award and the International Council on Systems Engineering Pioneer Award.
E-card: www.jgoldman.info
David Arney - Lead Engineer, MD PnP Program David Arney is the Lead Engineer for the Medical Device Plug and Play Program. He has been working on applying
formal methods to medical device software since 2001 and was a scholar in residence at the FDA's Center for Devices and Radiological Health in the Office of Science and Engineering Laboratories. He was involved in writing the ASTM 2761-09 ICE standard for interoperable medical devices. He started at the MD PnP program in September of 2010, and is currently writing his dissertation for a PhD in computer science under Professor Insup Lee at the University of Pennsylvania.
Jeff Plourde - Applications Developer, MD PnP Program Jeff Plourde is an Application Developer for the MD PnP program. Previously he worked in the hedge fund industry.
As a portfolio manager Jeff built and analyzed statistical trading models using high-throughput backtesting techniques and deployed those models with his own high frequency trading algorithms. He holds a BS in Computer Science and a BS in Biology from Rensselaer Polytechnic Institute. Jeff began working with the MD PnP program in May 2012.
Jeffrey Peterson - Clinical Engineer, MD PnP Program Jeffrey Peterson is currently a Clinical Engineer for the MD PnP program. Previously, he worked as a Clinical Engineer
at UMass Memorial Medical Center in Worcester, MA. His projects were focused on healthcare technology management, clinical alarm management and distribution and medical device integration. He holds an MS Biomedical and Clinical Engineering and a BS in Biomedical Engineering, both from the University of Connecticut. Jeff completed his Master’s thesis on clinical alarm management during his time at UMass.
Webcast Presenters
Angelo Corsaro – CTO, PrismTech
Angelo Corsaro, Ph.D. is Chief Technology Officer (CTO) at PrismTech where he directs the technology
strategy, planning, evolution, and evangelism. Angelo leads the strategic standardization at the Object Management Group (OMG), where he co-chairs the Data Distribution Service (DDS) Special Interest Group and serves on the Architecture Board. Angelo is a widely known and cited expert in the field of real-time and distributed systems, middleware, and software patterns, has authored several international standards and enjoys over 10+ years of experience in technology management and design of high performance mission- and business-critical distributed systems. Angelo received a Ph.D. and a M.S. in Computer Science from the Washington University in St. Louis, and a Laurea Magna cum Laude in Computer Engineering from the University of Catania, Italy.
Webcast Presenters
Angelo Corsaro, PhD Chief Technology Officer
DDS Overview
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DDS is an Object Management Group (OMG) Standard for efficient, secure and interoperable, platform- and programming-language independent data sharing
DDS standardises: - Programming API - Interoperable wire-protocol - Extensible Type System - Data Modeling - Security Framework - Remote Procedure Call
The DDS Standard
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DDS provides a Global Data Space abstraction that allows applications to autonomously, anonymously, securely and efficiently share data
DDS’ Global Data Space is fully distributed (decentralised), highly efficient and scalable
Data Distribution Service (DDS)
DDS Global Data Space
...
Data Writer
Data Writer
Data Writer
Data Reader
Data Reader
Data Reader
Data Reader
Data Writer
TopicAQoS
TopicBQoS
TopicCQoS
TopicDQoS
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4DataWriters and DataReaders are automatically and dynamically matched by the DDS Discovery
A rich set of QoS allows to control existential, temporal, and spatial properties of data
Data Distribution Service (DDS)
DDS Global Data Space
...
Data Writer
Data Writer
Data Writer
Data Reader
Data Reader
Data Reader
Data Reader
Data Writer
TopicAQoS
TopicBQoS
TopicCQoS
TopicDQoS
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Elegant and High Level Data Sharing Abstraction
Polyglot and platform independent
• Java, Scala, C, C++, C#, JavaScript, CoffeeScript etc.
• Android, Windows, Linux, VxWorks, etc.
Peer-to-Peer by nature, Brokered when useful
Key Highlights
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Content and Temporal Filtering (both sender and receiver filtering supported)
Queries
20+ QoS to control existential, temporal, and spatial properties of data
Key Highlights
DDS in Action
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Sample Oximeter Data Modelmodule dds { module demo { /** The Oximetry Parameters are split into two topics to take into account the different update rates. Since the pleth parameter is updated with a ~10ms period, it is sent on its own topic to avoid sending over and over data that changes every second. **/ struct Oximetry { string deviceId; long deviceType; long spO2; long bpm; long rr; }; #pragma keylist Oximetry deviceId
/** * High frequency pleth data used for graphing * and analysis. */ struct Pleth { string deviceId; float pleth; }; #pragma keylist Pleth deviceId
/** * Topic used to advertise the presence of a device. */ struct DeviceInfo { string deviceId; string description; }; #pragma keylist DeviceInfo deviceId }; };
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Producing Oximetry
DomainParticipant dp(0); Topic<Oximetry> topic(dp, “TOximetry”); Publisher pub(dp); DataWriter<Oximetry> dw(pub, topic);
while (true) { Oximetry o = getOximetry(); dw << o; // -‐or-‐ dw.write(o) sleep(1); }
C++ CoffeScriptrt = new dds.Runtime() rt.onconnect = () -‐> topic = new Topic(0, “TOximetry”, “dds::demo::Oximetry”) rt.registerTopic(topic) dw = new DataWriter(rt, topic)
writeOximetry = () -‐> o = getOximetry() dw.write(o)
setInterval(writeOximetry, 1000)
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C++ CoffeScript
Consuming Oximetry
DomainParticipant dp(0); Topic<Oximetry> topic(dp, “TOximetry”); Subscriber sub(dp); DataReader<Oximetry> dr(pub, topic); LambdaDataReaderListener<Oximetry> listener;
listener.data_available = [](DataReader<Oximetry>& dr) { auto samples = dr.read(); showOximetry(samples); }; dr.listener(&listener, StatusMask::data_available());
rt = new dds.Runtime() rt.onconnect = () -‐> topic = new Topic(0, “TOximetry”, “dds::demo::Oximetry”) rt.registerTopic(topic) dr = new DataReader(rt, topic) dr.addListener((o) -‐> showOximetry(o))
Integrating the Clinical Environment: Implications for healthcare quality, safety, and
innovation
Julian M. Goldman, MD Director, Program on Interoperability, Mass General Hospital
Medical Director, Partners HealthCare Biomedical Engineering Anesthesiologist, MGH/Harvard Medical School
Contact information: www.jgoldman.info
Prismtech Webinar Nov 19, 2014
• 1999 IOM published “To Err Is Human” up to 98,000 people a year die because of mistakes in hospitals.
• 2010 the Office of Inspector General for Health and Human Services said that bad hospital care contributed to the deaths of 180,000 patients in Medicare alone in a given year.
• 2013 Journal of Patient Safety: between 210,000 and 440,000 patients each year who go to the hospital for care suffer some type of preventable harm that contributes to their death.
• “That would make medical errors the third-leading cause of death in America, behind heart disease, which is the first, and cancer, which is second. “
1999
2013
Devices, processes, non-integrated system errors
Home ventilator
OR scene – patient’s life saved: Clinicians need timely, accurate data to reduce error, treatment delays, injuries
and deaths. Is that how we practice today? Where are innovative solutions?
Proposal: Can an Integrated Clinical Environment (ICE) add “error
resistance” to healthcare delivery?
Apps for smart alarms; med safety; equipment management
What if… Asking a lot of the platform
“Medical IoT” (Internet of Things)
Pulse Oximeters measure oxygen saturation – displayed as SpO2 %
Julian M. Goldman, MD / MGH
Pulse Oximeter oxygen saturation is 84% on instrument display and in EHR
EMR
Medical Devices generate “First Mile” of data (from patient)
Blood Pressure
Bluetooth pulse oximeter
Example - Infusion technology: 1. Decision support? 2. Prevent contra-indicated
infusion? 3. “Artificial pancreas”
Capabilities? (closed loop) 4. Consolidate all data for
adverse event analysis? 5. Check device status,
software version? Recall? 6. Adjust settings remotely?
Medical Devices are also the “Last Mile” (data back to devices)
These infusion pumps are for use on ONE patient
Patient-Controlled Analgesia (PCA) system safety concerns
PCA Pump
(With patient button)
Nurse call Patient
• Patients can call to request more pain medication, but, cannot call
for help when over-medicated.
• Over-medication can cause respiratory and cardiac arrest
• Comprehensive monitoring is not typically used due to high false
and nuisance alarm rate
• “Safe PCA System” prototype uses information from medical
monitors, EMR, and IV pump to prevent overdose
Patient Controlled Analgesia (PCA)
PCA Pump
(With patient button)
Nurse call Patient
1. Up to 6,875 serious preventable PCA-related adverse events
are estimated to occur annually. NO official figures. NO
reporting
2. PCA can be fixed! Digital platform of interoperable devices +
apps -> safer medication administration
• Why is data fusion and decision support so challenging to
implement?
Typical Patient Controlled Analgesia System
Oxygen Level Low WHY????
Pulse Oximeter Data example
JM Goldman MD / MGH
Not reall low oxygen “Bad” data
BP cuff - Pulse Oximeter Interaction
JM Goldman MD / MGH
Baseline Cuff inflates – loss of finger signal Blood returns to finger
Monitor Displays Low Oxygen Level (SpO2) Alarm Event “84%” at 2:07
No evidence of 84% SpO2 in EHR (Blue ticks representing SpO2 values Don’t change)
We must have a system perspective Sampling error for transient events
Julian M. Goldman, MD / MGH
Sources of variation in EHR documentation due to Data Sampling
Patient’s “actual” SpO2 minimum = 70%
60 Seconds
Example of possible EHR sample points for 1-minute recording
Based on this example, EHR May record SpO2 as: 98% 92% 80% 75% Etc.
Cardio-Pulmonary Bypass (Heart-lung machine)
Normal routine: Switch from anesthesia machine
ventilator to cardiopulmonary bypass machine, and
back to ventilator (after bypass)
or
Failure to Ventilate after Bypass
• Adverse Anesthetic Outcomes Arising from Gas Delivery Equipment: A Closed Claims Analysis.
• Anesthesiology. 87(4):741-748, October 1997
• “… In the second case, the anesthesiologist forgot to resume ventilation after separation from cardiopulmonary bypass. The delayed detection of apnea was attributed to the fact that the audible alarms for the pulse oximeter and capnograph had been disabled during bypass and had not been reactivated. Both patients sustained permanent brain damage.”
Almost every surgical team has experienced this error!
17 years
Cardio-Pulmonary Bypass
Smart system would provide warning if ventilator off
and bypass pump flow = 0.
No App for that
and
Ebola
In Hospital
X We need to move personnel away from patient area
Remote data display, remote device control, apps, and
complete data collection to monitor individual and
population health
1 provider in room
Medical Device “Plug-and-Play”
Interoperability Program (MD PnP)
Founded in 2004, the MD PnP research program is a
multi-institutional community with Lab based at
Massachusetts General Hospital (MGH), with grant
support from NIH/NIBIB, NSF, DoD/TATRC, and NIST
Mission: lead the adoption of open standards and
technologies for medical device interoperability to
improve patient safety
Goals of the MD PnP Program
1. Lead the adoption of open standards and related technology to support medical device interoperability and system solutions
2. Define a regulatory pathway in partnership with the FDA and other regulators
3. Elicit clinical requirements for the proposed interoperable solutions
4. Use our vendor-neutral laboratory to: • evaluate interoperability standards and solutions • serve as a community resource
5. Investigate safety of proposed engineering solutions
MD PnP Lab at MGH/PHS
• Vender-neutral sandbox for experimenting with device interoperability solutions (standards technologies, products)
• Contains devices – production and research
• Supports diverse collaborators
Safety Culture
Based on Reason, Managing the Risks of Organisational Accidents, 1977
http://www.coloradofirecamp.com/just-culture/definitions-principles.htm
What success will look like …
• DATA: Widespread availability of complete clinical and medical device performance data for …
• Real Time Safety: Close the loop on clinical care, workflow, and implement “error resistant” systems
• Unleash innovation in patient care, patient safety, and healthcare efficiency to save hundreds of thousands of lives and billions of $.
• A “systems engineering” platform is widely available for research, development, and deployment.
Dr. Julian M. Goldman, M.D. – Principle Investigator
Jeffrey Plourde – Lead Developer
Jeffrey Peterson – Clinical Engineer
David Arney – Lead Engineer
MD PnP Interoperability Program
Massachusetts General Hospital
Harvard Medical School
Harnessing
DDS in Next
Generation
Healthcare Systems
19 Nov 2014
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Integrated Clinical Environment
ASTM-F2761-09 ICE Multi-manager (apps)
Data Logger
Functional architecture
No technical specification (yet)
Device adapter
Replaced by native speakers
19 Nov 2014
2
OpenICE Prototype in the Lab
ASTM-F2761-09 ICE
19 Nov 2014
3
Middleware
(DDS)
OpenICE Prototype
Realization of the ICE functional architecture
Fully open source
Initially targeted at clinical research community.
19 Nov 2014
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OpenICE On The Web
MD PnP Program
http://www.mdpnp.org
SourceForge
http://mdpnp.sourceforge.net
OpenICE.info
http://www.openice.info
19 Nov 2014
5
What is a parallel system?
Multiple processors run multiple software programs
concurrently within the same computer
There is shared “memory” that all processors can
access
Memory
Email Web Browser Word Processor
6
19 Nov 2014
What is a distributed system?
A collection of networked computers coordinating
their actions by passing messages
Each computer has isolated memory
No one computer can know at any instant the true
contents of all isolated computer memories
Memory
Medical Device Supervisor Application
Memory Memory
Messages Messages
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19 Nov 2014
Message Oriented Middleware Innumerable examples: JMS, MQ Series, TIBCO
Rendezvous
Devise messages to ensure consistent information throughout system nodes.
Not a natural analog to non-distributed parallel systems; source of error.
Memory
Medical Device Supervisor Application
Memory Memory
Messages Messages
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19 Nov 2014
Data Distribution Service
An abstraction layer (API) that simplifies distributed
system development by providing all computers
with a reproduction of shared memory
Memory
DDS DDS DDS
Memory Memory
Messages Messages
Medical Device Supervisor Application
Memory
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19 Nov 2014
Quality of Service Policies Reliability
Configurable to seek acknowledgment of emitted data automatically.
Liveliness
Automatic detection of defunct remote data emitters.
Destination Order
Guarantees ordering of data
Any single policy would require a great deal of implementation upon a Message Oriented Substrate.
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19 Nov 2014
DDS & MDPnP OMG Standard
An open, vibrant standard with associated community
Supports multiple languages C, C++, Java, etc.
Vendor Neutral
Declarative Non-Functional Characteristics
Quality of Service Policies
Interoperability
Uses familiar data access paradigms
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19 Nov 2014
ICE Device
Adapter Web Server
(socket.io)
openice.info
ICE
Supervisor
Data Logger
Device Device
ICE Device
Adapter
DDS Participant
Web OpenICE Prototype 12
DDS Websocket Proprietary
OpenICE Data Model
19 Nov 2014
DDS Websocket Proprietary
Ivy Vital-Guard 450C
Dräger Evita XL
Beaglebone Black
Network
Web Browser
Server
OpenICE Supervisor
OpenICE Prototype 13
19 Nov 2014
Current Deployment Equipment
OpenICE Prototype Standard Ethernet
Zero configuration required
WiFi 802.11n enabled
Hardware
Standard PCs (Mac, Windows, Linux) running Supervisor
BeagleBones running “device adapter” software
Standard Intel server running Ubuntu
These are not the only possibilities for deployment
19 Nov 2014
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Device Functionality Problems
Medical devices today were not designed with ICE
functionality in mind
Time of data export is varied (PB840)
Alarm data export behavior differs (Ivy alarm silence)
Waveform export is highly varied (Philips batching)
Nomenclature is not standardized (NIST, 11073, etc.)
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19 Nov 2014
Data Logger - Clinical Black Box Record device data in an open, standardized, and time-synchronized
manner
The log will include:
Commands, Button presses, Location, Status
Physiologic and technical alarms
Physiologic vital sign data from patients
Device connections and disconnections from ICE
Data Log supports Analysis and Playback for two complementary purposes:
Analysis of device interactions (debugging, root cause analysis)
Analysis of adverse events involving patients (clinical)
19 Nov 2014
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PCA Safety Demo
Open sourced
Download from mdpnp.sourceforge.net
Closed loop control of PCA pump
Can use devices or simulators
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19 Nov 2014
PCA Safety Demo
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19 Nov 2014
Thank You
Learn more:
https://OpenICE.info
http://mdpnp.org – membership signup for news
19 Nov 2014
19
Angelo Corsaro, PhD Chief Technology Officer
VORTEX Overview and Healthcare Applications
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VORTEX is a ubiquitous data sharing platform for the Internet of Things providing scalable end-to-end seamless, efficient, secure and timely data sharing for IoT supporting device, edge, gateways and cloud
VORTEX platform coverage, performance and scalability make it the only viable choice from consumer to demanding business critical, industrial, real time, IoT applications
VORTEX simplifies IT/OT integration, and is the only platform that holistically addresses IT and OT requirements
Introducing Vortex
Sensors/ Things/ Devices
Connectivity/ Transport
Edge Management/ Telemetry
Application Platform
Enterprise Systems
Ubiquitous Data Sharing peer-to-peer, device-to-cloud, cloud-to-cloud
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Specialised device implementations optimally addressing requirements of OT and IT platforms
VORTEX can readily deal with data ingestion seamlessly integrating with other protocols, e.g. MQTT, CoAP, etc.
VORTEX leverages the DDS standard for interoperability and uniquely extends it with support for Internet Scale systems, mobility and Web 2.0 applications
The VORTEX PlatformDevice
Tools
Integration
Vortex Cloud
MaaS
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VORTEX Architecture
IP
UDP TCP
DTLS TLS
WebSocketDDSI Wire Protocol
VORTEX Café
VORTEX Cloud
VORTEX Web
VORTEX Gateway
VORTEX Lite VORTEX OpenSplice
MQTT AMQP XMPP HTTP HBase DMBS TCP/IP, UDP/IP
…
VORTEX Café
DDS API
DDS Security
Use Cases
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E-Health Use Case
Hospital A Private Cloud
AnalyticsBig Data Store
Hospital B
Further information – MD PnP
▶ OpenICE
▶ http://www.mdpnp.org/MD_PnP_Program___OpenICE.html
▶ For further information please contacct
▶ Andrea Lenco – [email protected]
▶ www.mdpnp.org )
Further information - PrismTech ▶ For further information please contact us directly
▶ angelo [email protected]
▶ www.prismtech.com (on-demand live demo available)
▶ Or via social media
www.twitter.com/prismtech
www.linkedin.com/company/prismtech
info.prismtech.com/facebook
www.slideshare.net/prismtech1
