an open test bed for medical device integration and coordination zahid mian november 7, 2012 andrew...

An Open Test Bed for Medical Device Integration and

Coordination

Zahid MianNovember 7, 2012

Andrew King, Sam Procter, Dan Andersen, John Hatcliff, Steve Warren (Kansas State Univerty)

William Spees, Raoul Jetley, Paul Jones, Sandy Weininger (US FDA)

Worcester Polytechnic Institute

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Introduction

• Lack of Medical Device Integration • V & V Techniques for Single Systems• Developers More Focused on Firmware Dev—

Not “formal” QA Techniques• Most Devices Have Connectivity, But Not Well

Integrated• Many Commercial Companies Are Producing

Integrated Products—Somewhat Dangerous

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Challenges

• Choosing Middleware & Integration Architectures to Support Integration

• Choosing Programming Models for V&V, Certification, RAD, etc.

• Appropriate V & V Techniques• Can Existing Regulatory Guidelines be

Extended• Innovation of New Technology—Safe/Effective• Interoperability & Security

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Medical Device Coordination Framework (MDCF)

• Three Contexts– Clinical (Room-Oriented)– Alarm Integration and Forwarding– Critical Care

• Flexible Pub/Sub middleware architecture using JMS

• Model-Based Programming

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Clinical (Room Oriented)

• Intensive Care Ward– Several Stand Alone Devices, Each Having it’s Own

Logging/Monitoring Tools (EHR, Billing, etc.)

– Inefficiencies: • different interfaces (confusion)• physically separated• different roles/views• separate logs

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Clinical – Integration Solution

• EHR DB is Single Consumer –aggregates device data into one place

• Heads Up Display—info from multiple devices displayed on Monitor(s) near patient bed

• Eg: CareWare uses IBM’s Eclipse Framework– Define “view(s)” based on device

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Clinical – Integration Solution

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Clinical – Implementation

• Requirements– Support different data amounts/rates

• Pulse oximeter—updated every 10 seconds• Electronic stethoscope—8 kilosamples/second

– Integration of “Data Transformations”• Filters, aggregations, etc.

– Allow definition of producers, consumers, transformers

– Provide facilitates for validation and auditing– Single Server or Server/Room?

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Clinical – V&V and Regulatory

• Performance– Unacceptable Latencies and Jitter?– Impact of Heightened Activity in Another Room

• Security– Private data, unobservable, unalterable

• Safety– Redisplay must be faithful to the precision &

presentation of original

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Alarm Integration & Forwarding

• Devices Produce Alarms – IEC 60601-1-8 Standard—distributed alarm system

• Problem of False Positives– “Smart Alarms” – Fuzzy Logic (reasoning)– Consider: patient body type, weight, history

• Eg: pulse oximeter and respiratory monitor• Solution:

– Priority/source of alarm – Information signals from monitoring devices– Programmable support to correlate data from many sources

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Critical Care Device Coordination

• Not just unidirectional flow• Automated Agent Control to Communicate

Between Devices• Eg: X-ray/Ventilator

– Acquiring chest x-rays from patients on ventilators– Doctors must turn off Ventilator – Human Error– Automatically Coordinate

• Ventilator can identify full inhalation/exhalation• Capture x-ray at optimal point

• Eg: “Smart Pumps” (fluid infusion)

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Critical Care Device Coordination

• Integration Solution– Network capable devices (MAC based ID)– DB for scripts written by experts– Allow clinician to choose appropriate script– Script “selects” necessary devices – Script may run uninterrupted or stop for input

• Issues– Coordination comps as simple automata– Support rigorous validation for regulatory oversight– Server per Room (too critical)

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Critical Care Device Coordination

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Goals of MDCF

• Provide middleware to enable integration of devices from different vendors with minimal effort

• Support for common data formats• Enable transformation of data streams• Support “realistic” device integration contexts• Performance/programmability scales• Options for guaranteed delivery, logs/audits, message

persistence• Script programming from building blocks• Infra should be freely available and open source

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Goals of MDCF

• Standards-based Framework for enterprise-level• Support real and simulated devices

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MOM Foundation

• Messaging-Oriented-Middleware– Based on JMS

• Meets the Goals of MDCF– Flexible messaging, open source, enterprise-level, etc.

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JMS Primary Objects

• Client uses JNDI to get Connection Factory• Create “Active” Connection • Exception Listener monitors problems• If Conn is good, client creates a JMS Session• Session is Single Threaded (serial delivery)

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JMS Primary Objects

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JMS Destinations

• Dest is “abstract” entity (to/from, pub/sub)• Session creates MessageProducers/Consumers• Client requests a Message, updates it, and

sends it using MessageProducer• Clients can add filter expressions• Supports diff message formats: text (eg. HL7)

and objects (eg. DICOM images)

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JMS Destinations

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JMS Message Format

Key-value pairs

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MDFC Modules

• Device Connection Manager– Listens on JMS channel for desired connections– Assumes every device has JVM– JVM-capable adapter available for non-JVM device

• HHSQL (stores device, driver info)• Consoles

– Maintenance (allow installation/updates)– Monitoring (flow of events)– Clinician (data visuals, invocation of scripts)

• Scenario Manager (manages life-cycle of objects within a script, teardown of objects)

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Programming Model

• Component-Based Programming– Abstract details of lower-level system– Rapid assembly of integration scenarios

• Supports “typed” input/output event ports• Supports multiple categories of comps

– Data producers, data transformers, data consumers

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Cadena Framework

• IDE• Component-based meta-modeling• Cadena generates

– Component interface editor … define comp types– System scenario editor … allocate/connect comps– Builds executable system

• “Active Typing”: checks for type correctness

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ICU Scenario Components

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OR Scenario Components

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CORBA Component Model

• Generates Java Skeleton/Container• Has all logic required for framework• Code “Business Logic” Only• Analyzes scenario model; gen xml spec file

– details of the scenario model– location of class files

• Reduces Programming errors

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Experiments

• Baseline– Simple producer/consumer; measure raw perf

• Clinical– Asses ability to support typical usage modes

• Categories of Data– Device data– Alarm events– Medial informatics (patient, images, drug, etc.)

• Parameter settings (rates set to worst-case)

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Baseline Configurations

• Simple Event Notifications– No payload (10 bytes)

• HL7– 313-byte (vaccine)– 2227-byte (adverse reactions to vaccine)– 4312-byte (additional vaccine events)

• DICOM– Chest (379 kb), knee (130 kb), shoulder (70 kb)

• Connection Topologies– Likely “real world’ setup

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Baseline Experimental ResultsT

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Producers to Consumers

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Baseline Experimental Results

• Message Size + Topology Affect TP• Larger Message reduces TP rate (marshalling)• Greatly affected by Topology• Increasing Producers; limited impact • Increasing Consumers; high impact• Possibly due to Queue sharing Messages

– Many producers: msgs arrive in Q at once– Many consumers: msg removed from Q and copy to

many worker threads

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Critical Care Device Coordination

• OR equipped with– Anesthesia machine with integrated ventilator,

ECG, and blood pressure cuff– Large “heads-up” displays (render data)– Transformer (software) preprocessor for ECG– Results (latency) –shows the framework can

support coordinated activitiesWhy so high?

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Integrated Displays and Alarms

• Large ICU ward with multiple rooms– Equipped with blood pressure cuff, cardiac monitor,

intravenous medicator, pulse oximeter, and ventilator.

– device produces data/alarm– room has monitor to render data– room has a nurse’s station display (subs to alarms)

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Integrated Results

Scales to 20 rooms

Max latency 4 sec

Max latency 3 sec

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Conclusions

• The Good– Provides scalability– Enterprise-Level architecture– “Solid” performance with open source– Loosely coupled component-model programming

• The Bad– Unacceptable performance with persistence

• More Work– Expand list of devices– Include wearable, ambulatory sensor-based devices