Context Most local public health departments serve limited English proficiency groups but lack sufficient resources to translate the health promotion materials that they produce into different languages. Machine translation (MT) with human postediting could fill this gap and work toward decreasing health disparities among non–English speakers. Objectives (1) To identify the time and costs associated with human translation (HT) of public health documents, (2) determine the time necessary for human postediting of MT, and (3) compare the quality of postedited MT and HT. Design A quality comparison of 25 MT and HT documents was performed with public health translators. The public health professionals involved were queried about the workflow, costs, and time for HT of 11 English public health documents over a 20-month period. Three recently translated documents of similar size and topic were then machine translated, the time for human postediting was recorded, and a blind quality analysis was performed. Setting Seattle/King County, Washington. Participants Public health professionals. Main Outcome Measures (1) Estimated times for various HT tasks; (2) observed postediting times for MT documents; (3) actual costs for HT; and (4) comparison of quality ratings for HT and MT. Results Human translation via local health department methods took 17 hours to 6 days. While HT postediting words per minute ranged from 1.58 to 5.88, MT plus human postediting words per minute ranged from 10 to 30. The cost of HT ranged from $130 to $1220; MT required no additional costs. A quality comparison by bilingual public health professionals showed that MT and HT were equivalently preferred. Conclusions MT with human postediting can reduce the time and costs of translating public health materials while maintaining quality similar to HT. In conjunction with postediting, MT could greatly improve the availability of multilingual public health materials.

This paper reports on the design process of a web-based collaborative system for the production of multilingual health communication materials. The system is based on a workflow combining machine translation and human post-editing and has been designed for public health professionals who are bilingual domain experts but not necessarily trained translators. Our initial data gathering phase involved interviews and focus groups with local and regional public health departments. Based on the design recommendations extracted from the data, we implemented a web-based prototype collaborative translation management system. We further refined the system through an iterative design process that included informal user testing with multilingual participants. Future work will include usability studies with public health workers and the integration of additional collaborative features.