hlthinfo 730 – lecture 13 slide #1 hlthinfo 730 healthcare decision support systems lecture 13:...

HLTHINFO 730 – Lecture 13 Slide #1

HLTHINFO 730Healthcare 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– ‘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

• Classic app is ECG monitoring

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

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


Another view of the ECG

• Oneheart-beat

Particularly want to look out for lengthening Q-T


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


AM / FM• Can encode signals by changing (“modulating”)

amplitude or frequency (or phase) of a carrier signal


Basics of signal processing

• Sampling frequency– Must take samples frequently enough– The Nyquist

rate 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

• 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

• 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

• A sine wave is the pure ‘spike’ once Fourier transformed

• Square wavesand pulsesmake morecomplexpatterns

Time domain Frequency domain


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

• 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

• Have you experienced any good (or not so good) automated monitors?


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• 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?

• 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

• We’ve created an information model for care plans (Khambati, Warren, Grundy and Hosking)


Model (contd.)


Designing a care plan in the model


Care plan in the model (contd.)


Automated interface generation

• We’ve prototyped a process for generating multiple user interface implementations for an individual care plan around the care plan model

Model a care planusing the care plan

visual language

Guideline Implementer

Instantiate the care plantemplate for a patient

Provider (e.g., GP)

Care Plan Template

Care Plan Instance

Model a suitable visual-isation for representing a

care plan on a specificdevice

User InterfaceProgrammer

Care Plan Visual-isation Definition

Generate an applicationfor a user to visualise a

care plan instance

Visualisation Generator

OpenLaszlo scriptrepresenting end-user application

Create runnableapplication

OpenLaszlo Compiler

Shockwave FlashObjects

DHTML


Example interfaces

• Part of a diabetes monitoring care plan being tailored in our care plan instantiation application


Example interfaces

• End-user Flash application compiled from OpenLaszlo

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


“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

• JapaneseRI-MAN (incidentally, that’s a doll it’s lifting) – still highly experimental


Tele-presence healthbot

• Much more common

… and further along toward real-world use


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

• Working with ETRI (Korean Robotics Institute)– Looking at adapting an inexpensive

robot 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

• 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

hlthinfo 730 – lecture 13 slide #1 hlthinfo 730 healthcare decision support systems lecture 13:...

Documents

seconds slide

voltage slide

carrier signal slide

longterm monitoring

healthy behaviour slide

ecg monitoring

lengthening qt slide

prof jim warren slide