HLTHINFO 730 Healthcare Decision Support Systems Lecture 13: Monitoring

1. HLTHINFO 730Healthcare Decision Support SystemsLecture 13: Monitoring Lecturer: Prof Jim Warren

2. 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

3. 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

4. Another view of the ECG • Oneheart-beat Particularly want to look out for lengthening Q-T

5. 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

6. AM / FM • Can encode signals by changing (“modulating”) amplitude or frequency (or phase) of a carrier signal

7. 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

8. 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)

9. 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

10. Fourier transform results • A sine wave is the pure ‘spike’ once Fourier transformed • Square wavesand pulsesmake morecomplexpatterns Time domain Frequency domain

11. 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)

12. 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

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

15. 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

16. 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)

17. 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?

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

19. Model (contd.)

20. Designing a care plan in the model

21. Care plan in the model (contd.)

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

23. Example interfaces • Part of a diabetes monitoring care plan being tailored in our care plan instantiation application

24. Example interfaces Auto-generated interfaces are still a bit basic, but better than nothing • End-user Flash application compiled from OpenLaszlo

25. “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…

26. Robots that can lift and carry • JapaneseRI-MAN (incidentally, that’s a doll it’s lifting) – still highly experimental

27. Tele-presence healthbot • Much more common … and further along toward real-world use

28. 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.

29. 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

30. 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