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TADAA - Towards Automated Detection of Anaesthetic Activity

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Presented by Bryan Houliston
Auckland University of Technology

Published in: Health & Medicine, Technology
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TADAA - Towards Automated Detection of Anaesthetic Activity

  1. 1. HINZ_PostGrad_13 TADAA 1Aura Laboratory
  2. 2. HINZ_PostGrad_13 TADAA 2My road to Health Informatics1Anaesthetic activity 2OutlineResearch methodology3Design and development 4Results and Conclusion5
  3. 3. HINZ_PostGrad_13 TADAA 3Your Text here Your Text hereYour Text hereYour Text hereMy road to Health Informatics• 1991 Graduated B.Comm• 91 - 02 Worked in software development• 03 – 04 M.InfoTech at AUT
  4. 4. HINZ_PostGrad_13 TADAA 4Your Text here Your Text hereYour Text hereYour Text hereAnaesthesia• “Extreme approximation of death” (Euliano, 2004)• “…that even today we understand but partly”(Eger, 2006)• “Every complication has the potential tocause lasting harm to the patient…deviations from the norm must be recognisedpromptly and managed appropriately”(Aitkenhead, 2007)
  5. 5. HINZ_PostGrad_13 TADAA 5Your Text here Your Text hereYour Text hereYour Text hereComplications• 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)
  6. 6. HINZ_PostGrad_13 TADAA 6Your Text here Your Text hereYour Text hereYour Text hereSolutions• Standard procedures• WHO Safer Surgery Checklist• Recording, Adherence to procedures
  7. 7. HINZ_PostGrad_13 TADAA 7Task analysis• “A scientific description of theanaesthetist’s task patterns and workloadwould aid in our understanding of thenature of anaesthetist’s job…and providea rational basis for making improvements”(Weinger, 1994)• “A scientific description of theanaesthetist’s task patterns and workloadwould aid in our understanding of thenature of anaesthetist’s job…and providea rational basis for making improvements”(Weinger, 1994)• Evidence-based medicine requiresscientific data to justify improvements
  8. 8. HINZ_PostGrad_13 TADAA 8Gold standard for data collection
  9. 9. HINZ_PostGrad_13 TADAA 9‘Scientific description’ ?ObservationDetailed ? NoObjective ? NoConsistent ? Unlikely(Slagle, 2002)
  10. 10. HINZ_PostGrad_13 TADAA 10Can we build a system ableto capture more scientificdata, with less risk ofdistraction, and lowerongoing cost?Scientificvalue ?PotentialdistractionExpensiveAutomated Observation ?
  11. 11. HINZ_PostGrad_13 TADAA 11Design Science methodology(Offermann, 2009)Humans are not idealinstrument for captureof scientific dataAnaesthetic recordDrug PrepLocation + orientationAURA LabACSC field testSimulated procedures
  12. 12. HINZ_PostGrad_13 TADAA 12Hidden MarkovModelBayesiannetworkA priori rulesBody movementLocationObject useVoice / soundVideoAccelerometerRFIDAudioMotion detectorsContact switchesFlow metersSensors Measure InferenceActivity detection systems
  13. 13. HINZ_PostGrad_13 TADAA 13RulesHMMs(Hidden MarkovModels)ProximityLOS(Location +Orientation +Stance)RFID(RadioFrequencyIdentification)Sensors Measure InferenceTADAA
  14. 14. HINZ_PostGrad_13 TADAA 14Anaesthetic Record Action ZoneRule: If reader detects any wristband tag thenRecording is happening
  15. 15. HINZ_PostGrad_13 TADAA 15ARAZ resultsLab Field tests Simulations98 8110077 6696 471000 0Specificity97%Sensitivity69%
  16. 16. HINZ_PostGrad_13 TADAA 16Drug Trolley Action Zone• Rule: If reader detects any wristband tagthen Drug Prep is happening
  17. 17. HINZ_PostGrad_13 TADAA 17DTAZ resultsField tests SimulationsSpecificity73%Sensitivity56%0100 1001064 10099
  18. 18. HINZ_PostGrad_13 TADAA 18Activity Fingerprinting• Signal strength „fingerprint‟ built up frommultiple tags and readers
  19. 19. HINZ_PostGrad_13 TADAA 19Activity Fingerprinting 2• Fingerprints associated with a location +orientation through SOM clustering
  20. 20. HINZ_PostGrad_13 TADAA 20Activity Fingerprinting 3= Drug Admin IV• Location + orientation sequences associatedwith activity through HMM analysis1 second at drug trolleythen 2 seconds at machinethen 3 seconds at patient
  21. 21. HINZ_PostGrad_13 TADAA 21Activity Fingerprinting 4
  22. 22. HINZ_PostGrad_13 TADAA 22AF resultsLab Field tests SimulationsSOMaccuracy99%HMMaccuracy97%SOMaccuracy88%HMMaccuracy10%SOMaccuracy97%On new data66%
  23. 23. HINZ_PostGrad_13 TADAA 23Distraction• Rated on VAS, converted to 0-10001020304050607080Tags Readers ObserverDistraction - Tags & Readers vs Observer (n=20)
  24. 24. HINZ_PostGrad_13 TADAA 24TADAA ObserverHardwareReaders x3Tags x16CablingLaptop$450$950$200$600Tablet PC $1000OTSSoftwareCOM monitor $50Labour Install (4 hours) $100Ongoing(annual)Replace tags $190 Wage $40000 ?Cost
  25. 25. HINZ_PostGrad_13 TADAA 25Conclusion• ARAZ very good at sensing Recording activity• DTAZ good at sensing Drug Prep– But needs more rules to distinguish otheractivity at drug trolley• AF very good at sensing anaesthetist location +orientation– But requires better activity inferencemechanism• RFID sensors less distracting than observers• Higher upfront cost, but lower ongoing cost
  26. 26. HINZ_PostGrad_13 TADAA 26Future development• Refine rules– Switching semi-HMM? (Duong, 2005)• Identify lower level activities• Additional sensors– Tag objects - syringes, intubation equipment– Voice detection for „conversing‟ activities– Gaze detection for „observing‟ activities
  27. 27. HINZ_PostGrad_13 TADAA 27Future 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
  28. 28. HINZ_PostGrad_13 TADAA 28Aura Laboratory
  29. 29. HINZ_PostGrad_13 TADAA 29ReferencesAitkenhead, 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 NewZealand 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 andAbnormality Detection with the Switching Hidden Semi-Markov Model. Paper presented atthe 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 IEEEWorld Conference on Computational Intelligence, Hong Kong, June 1-6.29Anaesthesia > Task Analysis > TADAA > Evaluation > Conclusion
  30. 30. HINZ_PostGrad_13 TADAA 30ReferencesPeffers, K., Tuunanen, T., Rothenberger, M. A., & Chatterjee, S. (2008). A Design ScienceReseach Methdology for Information Systems Research. Journal of ManagementInformation Systems, 24(3), 45-77.Slagle, J., Weinger, M. B., Dinh, M. T. T., Brumer, V. V., & Williams, K. (2002). Assessment ofthe 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 inAnaesthesia Providers. Anesthesiology, 80(1), 77-92.30Anaesthesia > Task Analysis > TADAA > Evaluation > Conclusion

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