Programmaleider Communicatie in de Gezondheidszorg; bijzonder hoogleraar 'Communicatie in de gezondheidszorg', Radboudumc
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Automated identification of yellow flags and their signal terms in physiotherapeutic consultation transcripts.
Wegstapel, J., Hartog, T. den, Sneekes, M., Staal, B., Scheer-Horst, E. van der, Dulmen, S. van, Brinkkemper, S. Automated identification of yellow flags and their signal terms in physiotherapeutic consultation transcripts. In: Proceedings of the 16th International Joint Conference on Biomedical Engineering Systems and Technologies BIOSTEC - (Volume 5) / [ed] Federico Cabitza, Ana Fred and Hugo Gamboa, SciTePress, 2023, Vol. 5, p. 530-537
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This paper investigates the possibility of automating the process of identifying yellow flags and their signal terms in physiotherapeutic consultation transcripts from patients with low back pain, using Automated Text Identification. It is part of the Automated Medical Reporting research domain. In physiotherapy focused on low back pain, yellow flags are considered psycho-social predictors of poor recovery and risk factors for chronic disability development. This paper uses a 6-step mixed method approach. Consultation transcripts and yellow flag assessment guidelines were collected, an automated identification tool was built and the OSPRO assessment guideline was used to test the tool for accuracy. It was found that it is possible to identify Yellow Flags and their Signal Terms automatically with the tool developed in this experiment. However, this is just the beginning, and much more research must be done in the future to further enhance the tool, mainly to improve precision.
This paper investigates the possibility of automating the process of identifying yellow flags and their signal terms in physiotherapeutic consultation transcripts from patients with low back pain, using Automated Text Identification. It is part of the Automated Medical Reporting research domain. In physiotherapy focused on low back pain, yellow flags are considered psycho-social predictors of poor recovery and risk factors for chronic disability development. This paper uses a 6-step mixed method approach. Consultation transcripts and yellow flag assessment guidelines were collected, an automated identification tool was built and the OSPRO assessment guideline was used to test the tool for accuracy. It was found that it is possible to identify Yellow Flags and their Signal Terms automatically with the tool developed in this experiment. However, this is just the beginning, and much more research must be done in the future to further enhance the tool, mainly to improve precision.