• J Gröschel, F Philipp, St Skonetzki, H Genzwürker, Th Wetter, and K Ellinger.
• Institut für Anästhesiologie und Operative Intensivmedizin, Universitätsklinikum Mannheim, Fakultät für Klinische Medizin Mannheim, Universität Heidelberg, 68135 Mannheim, Germany. j.groeschel@web.de
• Resuscitation. 2004 Feb 1; 60 (2): 205212205-12.

AbstractPrecise documentation of medical treatment in emergency medical missions and for resuscitation is essential from a medical, legal and quality assurance point of view [Anästhesiologie und Intensivmedizin, 41 (2000) 737]. All conventional methods of time recording are either too inaccurate or elaborate for routine application. Automated speech recognition may offer a solution. A special erase programme for the documentation of all time events was developed. Standard speech recognition software (IBM ViaVoice 7.0) was adapted and installed on two different computer systems. One was a stationary PC (500MHz Pentium III, 128MB RAM, Soundblaster PCI 128 Soundcard, Win NT 4.0), the other was a mobile pen-PC that had already proven its value during emergency missions [Der Notarzt 16, p. 177] (Fujitsu Stylistic 2300, 230Mhz MMX Processor, 160MB RAM, embedded soundcard ESS 1879 chipset, Win98 2nd ed.). On both computers two different microphones were tested. One was a standard headset that came with the recognition software, the other was a small microphone (Lavalier-Kondensatormikrofon EM 116 from Vivanco), that could be attached to the operators collar. Seven women and 15 men spoke a text with 29 phrases to be recognised. Two emergency physicians tested the system in a simulated emergency setting using the collar microphone and the pen-PC with an analogue wireless connection. Overall recognition was best for the PC with a headset (89%) followed by the pen-PC with a headset (85%), the PC with a microphone (84%) and the pen-PC with a microphone (80%). Nevertheless, the difference was not statistically significant. Recognition became significantly worse (89.5% versus 82.3%, P<0.0001 ) when numbers had to be recognised. The gender of speaker and the number of words in a sentence had no influence. Average recognition in the simulated emergency setting was 75%. At no time did false recognition appear. Time recording with automated speech recognition seems to be possible in emergency medical missions. Although results show an average recognition of only 75%, it is possible that missing elements may be reconstructed more precisely. Future technology should integrate a secure wireless connection between microphone and mobile computer. The system could then prove its value for real out-of-hospital emergencies.

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