hlthinfo 730 healthcare decision support systems lecture 13 monitoring l.
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HLTHINFO 730 Healthcare Decision Support Systems Lecture 13: Monitoring Lecturer: Prof Jim Warren Monitoring A few different domains Critical care monitoring – reporting back to humans who will respond quickly

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monitoring
Monitoring
  • A few different domains
    • Critical care monitoring – reporting back to humans who will respond quickly
    • ‘Ubiquitous’ monitoring – getting data (probably over a long period of time) without being too obvious about it
    • Participatory monitoring – patients get a sense of engagement by participating in the medical record
    • ‘Coaching’ – the interaction is mostly about encouraging healthy behaviour
critical care systems
Critical care systems
  • Classic app is ECG monitoring

P - R interval

QRS complex duration

Q - T interval corrected for heart rate (QTc) QTc = QT/ RR interval

0.12 - 0.2 seconds (3-5 small squares of standard ECG paper)

less than or equal to 0.1 seconds (2.5 small squares)

less than or equal to 0.44 seconds

See http://www.nda.ox.ac.uk/wfsa/html/u11/u1105_01.htm

another view of the ecg
Another view of the ECG
  • Oneheart-beat

Particularly want to look out for lengthening Q-T

amplitude frequency phase
Amplitude, Frequency, Phase

Amplitude is ‘displacement’ (a distance) in a physical vibration and then is usually transformed to an electric current and is measured in voltage

am fm
AM / FM
  • Can encode signals by changing (“modulating”) amplitude or frequency (or phase) of a carrier signal
basics of signal processing
Basics of signal processing
  • Sampling frequency
    • Must take samples frequently enough
    • The Nyquistrate istwice thefrequency ofthe highestfrequencycomponentof the signal
    • If there’s something higher frequency, then you’ll get aliasing – an incorrect interpretation of the signal
sampling in ecg
Sampling in ECG
  • In ECG we have a lot of concern with interval lengths
    • Equipment commonly samples at 100Hz (mobile devices) to 1000Hz (high resolution)
    • At 100Hz, due to the Nyquist rate, you miss any high-frequency features with a period of less than 0.02s (i.e., 20ms) (Period = 1 / frequency)
    • Moreover, at 100Hz, you can be up to 10ms late in seeing a rise or fall, and thus up to 20ms inaccurate in estimate of an interval
  • Sampling requirements (now talking ECG or other apps) put demands on
    • the speed of your equipment to process
    • the bandwidth of your transmission (esp. in telemonitoring)
    • the size of your database (esp. for long-term monitoring)
signal classification
Signal classification
  • Algorithms can classify signals based on features of the signal
    • Might be straightforward (e.g., time between lowest and highest amplitude – but keep in mind all those sampling errors!)
    • Signal can be mathematically transformed
      • Fourier transform – transforms from amplitude over time -> amplitude over frequency
      • We can then extract features from the transformed signal
  • Classifiers can then use whatever machine learning methods
    • Multiple regression, artificial neural networks, induced decision trees, etc.
    • Can classify the ‘system’ (e.g., the patient’s heart) as being in any of a variety of states
    • And you can layer symbolic reasoning (production rules) and fuzzy logic on top of the signal-feature-based classifiers
fourier transform results
Fourier transform results
  • A sine wave is the pure ‘spike’ once Fourier transformed
  • Square wavesand pulsesmake morecomplexpatterns

Time domain Frequency domain

markov model
Markov model
  • Based on the ‘memoryless’ (or Markov) property (“M” either way!)
    • Your previous states say nothing; only need to think about current state and probability/rate of progression to other states from there

e.g., P(Bt+1 | At) = 0.9

Can describe the system with a square matrix, NxN, where N is the number of states

Again, only accurate if the system is memoryless with respect to those states

Can use a series of low probability transitions to indicate that the system has changed (and throw an alert)

applications
Applications
  • ICU (esp. PICU) monitoring
    • Respiration, blood glucose, etc. – classify and alert on changes
  • Worn heart monitors
    • http://www.nlm.nih.gov/medlineplus/news/fullstory_64123.html
    • Also, worn accelerometers for falls detection
  • ‘Smart’ homes
    • Monitor usage patterns of lights, water, refrigerator etc. and also track motion
discussion
Discussion
  • Have you experienced any good (or not so good) automated monitors?
participatory home telemedcare
Participatory Home Telemedcare
  • Home ECG, lung function, blood oxygen saturation, glucose, weight, BP
  • All with feedback so patient sees their state and their progress
  • Can, for instance, learn to deal with an asthma attack (possibly on phone to nurse) without called ambulance
reminders life coaches
Reminders, life coaches
  • STOMP – txt messaging to quite smoking
    • “chewing gum for the fingers” – automated ‘friend’ totxt whencraving
    • Plus stagedsupportivemessagesandmonitoring
  • Significantquit effect(Maori andnon-Maoriat 6 months
  • Other obvious apps are exercise coaches, drug administration reminders and (esp. w. video phones) guides (e.g., for insulin dosing or nebulizer spacer technique)
what is a care plan anyway
What is a ‘care plan’ anyway?
  • Fundamental to monitoring or health promotion should be the notion of the care plan for a patient
    • What are our objectives (specified as goals and target values)?
    • What interventions do we have in place to achieve those objectives?
    • How often do we monitor status?
    • When do we plan to re-plan?
care plan model
Care plan model
  • We’ve created an information model for care plans (Khambati, Warren, Grundy and Hosking)
automated interface generation
Automated interface generation
  • We’ve prototyped a process for generating multiple user interface implementations for an individual care plan around the care plan model
example interfaces
Example interfaces
  • Part of a diabetes monitoring care plan being tailored in our care plan instantiation application
example interfaces24
Example interfaces

Auto-generated interfaces are still a bit basic, but better than nothing

  • End-user Flash application compiled from OpenLaszlo
your plastic pal that s fun to be with
“Your plastic pal that’s fun to be with”
  • Healthcare robots (or healthbots) are being considered to supplement human personnel
    • Particularly in low-intensity monitoring situations such as aged care
    • ‘Robot’ is from a Czech word for ‘to work’
      • But many practical robots are actually more focused on being mobile sensor platforms and computer terminals
      • Real work robots are possible when fixed to an automotive assembly line, but not yet practical for dealing with people
      • Which doesn’t mean the Japanese aren’t trying…
robots that can lift and carry
Robots that can lift and carry
  • JapaneseRI-MAN (incidentally, that’s a doll it’s lifting) – still highly experimental
tele presence healthbot
Tele-presence healthbot
  • Much more common

… and further along toward real-world use

robots for companionship
Robots for companionship
  • Gladys Moore, a resident at the NHC Healthcare assisted-living facility in Maryland Heights, Missouri, plays with AIBO, a robotic dog, in this undated handout photo. Researchers found that the robot dog was about as good as a real dog at easing the loneliness of nursing home residents in a study.
uoa health robotics centre
UoA Health Robotics Centre
  • Working with ETRI (Korean Robotics Institute)
    • Looking at adapting an inexpensiverobot for elder care
    • Combination of companion-ship and monitoringcapabilities
    • Strong emphasis on speechinteraction
    • More autonomous adjunct tohuman healthcare workers, ratherthan for tele-presence
    • Possibly supplement othersmart home equipment

Ultrasonic sensors to avoid bumping into things

summary
Summary
  • Monitoring is a major class of health IT activity
  • It leads to the embedding of sometimes non-trivial artificial intelligence in devices (often with reliance on traditional signal processing)
  • Monitors may be overt or ubiquitous
  • They may engage the consumer
    • In fact, engaging the consumer may be the main point!
  • Monitoring implies the knowledge engineering of guidelines