B R Houliston1, D T Parry, A F Merry. 1. AURA Laboratory, School of Computing & Mathematical Sciences, Auckland University of Technology, Auckland, New Zealand.
Abstract
BACKGROUND: Task analysis is a valuable research method for better understanding the activity of anaesthetists in the operating room (OR), providing evidence for designing and evaluating improvements to systems and processes. It may also assist in identifying potential error paths to adverse events, ultimately improving patient safety. Human observers are the current 'gold standard' for capturing task data, but they are expensive and have cognitive limitations. OBJECTIVES: Towards Automated Detection of Anaesthetic Activity (TADAA)--aims to produce an automated task analysis system, employing Radio Frequency Identification (RFID) technology to capture anaesthetists' location, orientation and stance (LOS). This is the first stage in a scheme for automatic detection of activity. METHODS: Active RFID tags were attached to anaesthetists and various objects in a high fidelity OR simulator, and anesthetic procedures performed. The anaesthetists' LOSs were calculated using received signal strength (RSS) measurements combined with machine learning tools including Self-Organizing Maps (SOMs). These LOSs were compared to those derived from video recordings. RESULTS: SOM clustering was effective at determining anaesthetists' LOS from RSS data for each procedure. However cross-procedure comparison was less reliable,probably because of changes in the environment. CONCLUSIONS: Active RFID tags provide potentially useful information on LOS at a low cost and with minimal impact on the work environment. Machine learning techniques may be employed to handle the variable nature of RFID's radio signals. Work on mapping LOS data to activities will involve integration with other sensors and task analysis techniques.
BACKGROUND: Task analysis is a valuable research method for better understanding the activity of anaesthetists in the operating room (OR), providing evidence for designing and evaluating improvements to systems and processes. It may also assist in identifying potential error paths to adverse events, ultimately improving patient safety. Human observers are the current 'gold standard' for capturing task data, but they are expensive and have cognitive limitations. OBJECTIVES: Towards Automated Detection of Anaesthetic Activity (TADAA)--aims to produce an automated task analysis system, employing Radio Frequency Identification (RFID) technology to capture anaesthetists' location, orientation and stance (LOS). This is the first stage in a scheme for automatic detection of activity. METHODS: Active RFID tags were attached to anaesthetists and various objects in a high fidelity OR simulator, and anesthetic procedures performed. The anaesthetists' LOSs were calculated using received signal strength (RSS) measurements combined with machine learning tools including Self-Organizing Maps (SOMs). These LOSs were compared to those derived from video recordings. RESULTS: SOM clustering was effective at determining anaesthetists' LOS from RSS data for each procedure. However cross-procedure comparison was less reliable,probably because of changes in the environment. CONCLUSIONS: Active RFID tags provide potentially useful information on LOS at a low cost and with minimal impact on the work environment. Machine learning techniques may be employed to handle the variable nature of RFID's radio signals. Work on mapping LOS data to activities will involve integration with other sensors and task analysis techniques.