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Technology for large-scale translation of clinical practice guidelines : a pilot study of the performance of a hybrid human and computer-assisted approach

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LT3
Abstract
Background: The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, was initiated in 2011 to optimize quality of care by promoting evidence-based decision-making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL Multiterm termbases) from medical termbases and support from online machine translation. This has resulted in a validated translation memory which is now in use for the translation of new and updated guidelines. Objective: The objective of this study was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator. Methods: We conducted a pilot trial in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (a) by certificated junior translators without medical specialization using the hybrid method (b) by an experienced medical translator without this support and (c) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The Human Translation Edit Rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy. Results: The average number of words per guideline was 1,195 and the mean total translation time was 100.2 min/1,000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 min/1,000 words faster (95% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. Conclusions: Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator)
Keywords
clinical practice guidelines, translation memory, HTER, machine translation, termbases, translation

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Chicago
Van de Velde, Stijn, Lieve Macken, Koen Vanneste, Martine Goossens, Jan Vanschoenbeek, Bert Aertgeerts, Klaar Vanopstal, Robert Vander Stichele, and Joost Buysschaert. 2015. “Technology for Large-scale Translation of Clinical Practice Guidelines : a Pilot Study of the Performance of a Hybrid Human and Computer-assisted Approach.” Jmir Medical Informatics 3 (4).
APA
Van de Velde, Stijn, Macken, L., Vanneste, K., Goossens, M., Vanschoenbeek, J., Aertgeerts, B., Vanopstal, K., et al. (2015). Technology for large-scale translation of clinical practice guidelines : a pilot study of the performance of a hybrid human and computer-assisted approach. JMIR MEDICAL INFORMATICS, 3(4).
Vancouver
1.
Van de Velde S, Macken L, Vanneste K, Goossens M, Vanschoenbeek J, Aertgeerts B, et al. Technology for large-scale translation of clinical practice guidelines : a pilot study of the performance of a hybrid human and computer-assisted approach. JMIR MEDICAL INFORMATICS. Canada: JMIR Publications; 2015;3(4).
MLA
Van de Velde, Stijn, Lieve Macken, Koen Vanneste, et al. “Technology for Large-scale Translation of Clinical Practice Guidelines : a Pilot Study of the Performance of a Hybrid Human and Computer-assisted Approach.” JMIR MEDICAL INFORMATICS 3.4 (2015): n. pag. Print.
@article{6959016,
  abstract     = {Background: The construction of EBMPracticeNet, a national electronic point-of-care information platform in Belgium, was initiated in 2011 to optimize quality of care by promoting evidence-based decision-making. The project involved, among other tasks, the translation of 940 EBM Guidelines of Duodecim Medical Publications from English into Dutch and French. Considering the scale of the translation process, it was decided to make use of computer-aided translation performed by certificated translators with limited expertise in medical translation. Our consortium used a hybrid approach, involving a human translator supported by a translation memory (using SDL Trados Studio), terminology recognition (using SDL Multiterm termbases) from medical termbases and support from online machine translation. This has resulted in a validated translation memory which is now in use for the translation of new and updated guidelines.
Objective: The objective of this study was to evaluate the performance of the hybrid human and computer-assisted approach in comparison with translation unsupported by translation memory and terminology recognition. A comparison was also made with the translation efficiency of an expert medical translator.
Methods: We conducted a pilot trial in which two sets of 30 new and 30 updated guidelines were randomized to one of three groups. Comparable guidelines were translated (a) by certificated junior translators without medical specialization using the hybrid method (b) by an experienced medical translator without this support and (c) by the same junior translators without the support of the validated translation memory. A medical proofreader who was blinded for the translation procedure, evaluated the translated guidelines for acceptability and adequacy. Translation speed was measured by recording translation and post-editing time. The Human Translation Edit Rate was calculated as a metric to evaluate the quality of the translation. A further evaluation was made of translation acceptability and adequacy.
Results: The average number of words per guideline was 1,195 and the mean total translation time was 100.2 min/1,000 words. No meaningful differences were found in the translation speed for new guidelines. The translation of updated guidelines was 59 min/1,000 words faster (95\% CI 2-115; P=.044) in the computer-aided group. Revisions due to terminology accounted for one third of the overall revisions by the medical proofreader. 
Conclusions: Use of the hybrid human and computer-aided translation by a non-expert translator makes the translation of updates of clinical practice guidelines faster and cheaper because of the benefits of translation memory. For the translation of new guidelines there was no apparent benefit in comparison with the efficiency of translation unsupported by translation memory (whether by an expert or non-expert translator)},
  articleno    = {JMIR Med Inform 2015;3(4):e33},
  author       = {Van de Velde, Stijn and Macken, Lieve and Vanneste, Koen and Goossens, Martine  and Vanschoenbeek, Jan and Aertgeerts, Bert and Vanopstal, Klaar and Vander Stichele, Robert and Buysschaert, Joost},
  issn         = {2291-9694},
  journal      = {JMIR MEDICAL INFORMATICS},
  keyword      = {clinical practice guidelines,translation memory,HTER,machine translation,termbases,translation},
  language     = {eng},
  number       = {4},
  pages        = {9},
  publisher    = {JMIR Publications},
  title        = {Technology for large-scale translation of clinical practice guidelines : a pilot study of the performance of a hybrid human and computer-assisted approach},
  url          = {http://dx.doi.org/10.2196/medinform.4450},
  volume       = {3},
  year         = {2015},
}

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