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My road to Health Informatics1
Anaesthetic activity 2
Outline
Research methodology3
Design and development 4
Results and Conclusion5
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My road to Health Informatics
• 1991 Graduated B.Comm
• 91 - 02 Worked in software development
• 03 – 04 M.InfoTech at AUT
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Anaesthesia
• “Extreme approximation of death” (Euliano, 2004)
• “…that even today we understand but partly”
(Eger, 2006)
• “Every complication has the potential to
cause lasting harm to the patient…
deviations from the norm must be recognised
promptly and managed appropriately”
(Aitkenhead, 2007)
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Complications
• 49% of preventable adverse events due to
„system factors‟
• Poor record keeping
• Lack of information
• Few standard procedures
• Failure to adhere to standards
• Poor communication
• Organisational culture (Davis, 2003)
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Solutions
• Standard procedures
• WHO Safer Surgery Checklist
• Recording, Adherence to procedures
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Task analysis
• “A scientific description of the
anaesthetist’s task patterns and workload
would aid in our understanding of the
nature of anaesthetist’s job…and provide
a rational basis for making improvements”
(Weinger, 1994)
• “A scientific description of the
anaesthetist’s task patterns and workload
would aid in our understanding of the
nature of anaesthetist’s job…and provide
a rational basis for making improvements”
(Weinger, 1994)
• Evidence-based medicine requires
scientific data to justify improvements
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Can we build a system able
to capture more scientific
data, with less risk of
distraction, and lower
ongoing cost?
Scientific
value ?
Potential
distraction
Expensive
Automated Observation ?
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Design Science methodology
(Offermann, 2009)
Humans are not ideal
instrument for capture
of scientific data
Anaesthetic record
Drug Prep
Location + orientation
AURA Lab
ACSC field test
Simulated procedures
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Hidden Markov
Model
Bayesian
network
A priori rules
Body movement
Location
Object use
Voice / sound
Video
Accelerometer
RFID
Audio
Motion detectors
Contact switches
Flow meters
Sensors Measure Inference
Activity detection systems
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Rules
HMMs
(Hidden Markov
Models)
Proximity
LOS
(Location +
Orientation +
Stance)
RFID
(Radio
Frequency
Identification)
Sensors Measure Inference
TADAA
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Activity Fingerprinting 3
= Drug Admin IV
• Location + orientation sequences associated
with activity through HMM analysis
1 second at drug trolley
then 2 seconds at machine
then 3 seconds at patient
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AF results
Lab Field tests Simulations
SOM
accuracy
99%
HMM
accuracy
97%
SOM
accuracy
88%
HMM
accuracy
10%
SOM
accuracy
97%
On new data
66%
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Distraction
• Rated on VAS, converted to 0-100
0
10
20
30
40
50
60
70
80
Tags Readers Observer
Distraction - Tags & Readers vs Observer (n=20)
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Conclusion
• ARAZ very good at sensing Recording activity
• DTAZ good at sensing Drug Prep
– But needs more rules to distinguish other
activity at drug trolley
• AF very good at sensing anaesthetist location +
orientation
– But requires better activity inference
mechanism
• RFID sensors less distracting than observers
• Higher upfront cost, but lower ongoing cost
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Future development
• Real-time viewer
– Communication to staff outside theatre
• Repository of activity records
– Research unfamiliar procedures
– Mine by anaesthetist
– Mine by procedure type, patient condition, etc
• Formulate „best practice‟ for procedure
– Recognise deviations in real-time, raise alarm
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References
Aitkenhead, A. R., Smith, G., & Rowbotham, D. J. (Eds.). (2007). Textbook of Anaesthesia
(Fifth ed.): Elsevier Limited.
Davis, P., Lay-Yee, R., Briant, R., Ali, W., Scott, A., & Schug, S. (2003). Adverse events in New
Zealand public hospitals II: preventability and clinical context. New Zealand Medical Journal,
116(1183).
Duong, T. V., Bui, H. H., Phung, D. Q., & Venkatesh, S. (2005). Activity Detection and
Abnormality Detection with the Switching Hidden Semi-Markov Model. Paper presented at
the IEEE Conference on Computer Vision and Pattern Recognition.
Euliano, T. Y., & Gravenstein, J. S. (2004). Essential Anaesthesia From Science to Practice.
Cambridge, UK: Cambridge University Press.
Kohonen, T. (2008). Data Management by Self-Organising Maps. Paper presented at the IEEE
World Conference on Computational Intelligence, Hong Kong, June 1-6.
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Anaesthesia > Task Analysis > TADAA > Evaluation > Conclusion
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References
Peffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2008). A Design Science
Reseach Methdology for Information Systems Research. Journal of Management
Information Systems, 24(3), 45-77.
Slagle, J., Weinger, M. B., Dinh, M. T. T., Brumer, V. V., & Williams, K. (2002). Assessment of
the Intrarater and Interrater Reliability of an Established Clinical Task Analysis Methodology.
Anesthesiology, 96(5), 1129-1139.
Smith, A. F. (2009). In Search of Excellence in Anesthesiology. Anesthesiology, 110(1), 4-5.
Weinger, M. B., Herndon, O. W., Zornow, M. H., Paulus, M. P., Gaba, D. M., & Dallen, L. T.
(1994). An Objective Methodology for Task Analysis and Workload Assessment in
Anaesthesia Providers. Anesthesiology, 80(1), 77-92.
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Anaesthesia > Task Analysis > TADAA > Evaluation > Conclusion
Notes de l'éditeur
Lab and field tests measured response rates when writing on different parts of the clipboard.Lower rates in field test because clipboard was on metal work surface of anaesthetic machine.
Field tests measured response rates at different areas of the drug trolley.
Polar plots show 6-point ‘fingerprints’ – signal strength values from 2 tags at 3 readers
Very simple explanation of how sequences of fingerprints build up an activity
Field tests highlight existing SOMs much less accurate on new data => inconsistency of signal strength from day-to-day