Context Aware Surgical Training Environment

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Context Aware SurgicalTraining Environment

• Operating Room of the Future 23 June 2006

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Vision

• ORs and surgical training centers will be
  pervasive computing environments
• Devices, sensors, tags, trainers, PDAs, monitors
  will discover one another and interoperate
• Components will require access to a context model
  to manage resources effectively
• The context model includes relevant information
  on people, roles, activities, events, workflow,
  devices,
• Intelligent components will be able to recognize
  events and activities
• Even in the presence of noisy or incomplete data

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SimCenter Context Model

• The people, devices, environmental factors,
  information resources,
• The activities and events going on and the roles
  people are playing in them
• The anticipated schedules, workflow models,
  training plans,
• Profiles of the training resources
  capabilities, complexity, ...
• Profiles of the trainees, their background,
  training goals, training history, training plans
• Privacy and information sharing constraints and
  policies

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SimCenter Interoperability

• Interoperability means that devices and systems
  can share and use models, data and services as
  needed
• Training resources can be intelligently matched
  to the training needs and profiles of trainees
• Constructing and monitoring customized training
  experiences, enabled by data and model
  interoperability
• Capability of end-to-end simulation fly throughs

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ORs will be data rich
Drugs
RFID
RFID
CAST
Tools
RFID
Patient Monitors
Staff

• ORs will be awash in low-level data, much of it
  noisy or incomplete
• Challenges include coping with the noise and
  interpreting the low-level data to recognize
  high-level events and activities

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Streaming Database Engines

• TelegraphCQ Data Stream Management System from UC
  Berkeley

• Data is stored/indexed in system
• Queries applied to stored data as they stream
  through

• Queries stored/indexed in system
• Data applied to stored queries as they stream
  through

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Example Streams Queries

• Data Sources (DS)
• Continually send data to streams
• PUSH or PULL sources
• Stream
• create stream traffic.incidents (
• incidentID integer,
• city varchar(100),
• street varchar(100),
• description varchar(1000),
• tcqtime timestamp TIMESTAMPCOLUMN
• ) type archived
• 2207, Dublin, JWO HACIENDA DR, Traffic Hazard -
  Debris/Objects, Wed Jul 16 183700 PDT 2003
• Continuous queries must be specified over a time
  window
• Select count() from mer.rfid range by 30
  seconds slide by 10 seconds group by tag_id
• Window is relative to the most recently arrived
  tuple.

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System Architecture
Trend Analyzer
Rule Base
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Simulations and Results

• The Human Patient Simulator (HPS) from METI
• Designed to react like a human
• Used for training resident doctors
• Responds to medical treatment
• Physiological data sets from HPS

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Scenario and Patient Profile

• HPS can run patient profiles
• Data logs from simulations used to evaluate the
  system
• Significant events for a blunt trauma multiple
  injuries profile include hypovolemia, tension
  pneumothorax, decompression and fluid infusions
• Provides data for Medical Encounter Record
• Ran 30 simulations on 7 profiles measuring false
  positives negatives and latency in detecting
  events

Patient Profile
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Status and Plans

• Were building on work done for Trauma Pod
• Preliminary work has produced a scalable
  architecture based on TelegraphCQ, the Jess rule
  based reasoning engine and relational databases
• Fall 2006
• Install equipment in SimCenter in Fall 2006
• Investigate use of LiveDatas system for output
• Develop ontologies in RDF/OWL to support semantic
  interoperability
• Spring 2007
• Develop and implement scenarios for proof of
  concept and evaluation

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Key Enablers

• Semantic interoperability
• Develop and use standard ontologies and data
  models to maximize information sharing
• Use evolving standards (XML, RDF, )
• Greater context awareness and intelligence
• The whole is more than the sum of its parts --
  drawing inferences, integrating across all
  entities, and recognizing patterns
• A service oriented architecture
• Distributed programs and resources share common
  interface standards and publish their APIs

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http//ebiquity.umbc.edu/