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A Multi-modal Alert System for Anaesthesia Diagnosis Part IV Project – Software Engineering

A Multi-modal Alert System for Anaesthesia Diagnosis Part IV Project – Software Engineering Tyler & Ken. Dr. Michael Harrison – Faculty of Medical & Health Dr. Beryl Plimmer – Department of Computer Science. Background.

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A Multi-modal Alert System for Anaesthesia Diagnosis Part IV Project – Software Engineering

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  1. A Multi-modal Alert System for Anaesthesia Diagnosis Part IV Project – Software Engineering Tyler & Ken Dr. Michael Harrison – Faculty of Medical & Health Dr. Beryl Plimmer – Department of Computer Science

  2. Background • A use of medicine to prevent the feeling of pain or another sensation during the operation • Few fatal errors occur during Anaesthesia administration • 80% are preventable human mistakes • Needs of Computerised Diagnosis

  3. How it works

  4. Current Issues • Increasing number of monitors in operating room • Low identification rate / High false alarm rate • Discrepancies between clinical and perceived urgencies • Ineffective Display

  5. Gathering User Requirements • Research previous works • One-to-One interviews with Anaesthesiologists • Field Trips • Consultation with an area expert

  6. Findings – 1/2 • 15” Touch Screen • Design to run in Windowsbased OS • Low occurrence rate • Management Protocol required

  7. Findings – 2/2 • User-centred Visualisation • Auditory Alarms • Physiological data through S5 Anaesthesia monitor • Categorised into 6 different Anaesthesia Status

  8. Lo-Fi Prototypes – 1/2

  9. Lo-Fi Prototypes – 2/2

  10. Suggested Solutions • Use of Multi-modal alarms with Speech • Bluetooth technology • Display with User-Centred UI

  11. Development Tool • Required to use LabVIEW 8.5 • Graphical programming for measurement and automation • For easy transition of project

  12. Key Implementations • Visualising low fidelity prototypes • Text-to-Speech in LabVIEW 8.5 • Plotting graph with real-time (time series)

  13. Conclusions • Clear and Easy Multi-Modal alarms • High Identification Rate without further learning progress • No interference to others (surgeons / patients) • Improved user control setting

  14. The End

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