title: Enhancing Collaborative Case Diagnoses Through Unified Medical Language System-Based Disambiguation: A Case Study of the Zika Virus.
abstract: During clinical case diagnoses, especially in low-resourced areas, the use of vocabularies within Unified Medical Language System (UMLS) can strengthen discussions between health professionals and, in certain cases, eliminate the need, enabling faster treatment.
  
  
  
  This article presents the benefits of using UMLS as a collaborative discussion tool and verifies its impact.
  
  
  
  The Sanar system has been improved by UMLS when using text retrieval to extract relevant medical concepts from cases investigated by the user and to provide contextualized searches of related articles. An experiment was conducted, focused on team engagement and discussion of a Zika virus case using Sanar, both with and without UMLS contextualization.
  
  
  
  The use of the tool was measured, and it was determined that the discussion in the group with UMLS support was more complete based on better information and inclusion of more variables. Clinicians involved responded to a questionnaire evaluating the relevance of functions.
  
  
  
  From the questionnaire showed that most of the group supported UMLS as important in complex diagnostics; the use of knowledge extraction before discussion is relevant to align knowledge of participants with more variables, such as the Zika virus, and to minimize the need for interaction in widely discussed cases.
  
  
  
  Based on the results obtained with the questionnaire, the use of UMLS provides acceleration in the diagnostic process that precedes interaction with other health professionals through clinical discussion tools. For future work, a mobile version will support offline navigation for locations with limited Internet access.
authors.affiliation: 1 Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid , Madrid, Spain .
authors.forename: Albert
authors.initials: A
authors.lastname: Moreira
authors.affiliation: 1 Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid , Madrid, Spain .
authors.forename: Raúl
authors.initials: R
authors.lastname: Alonso-Calvo
authors.affiliation: 2 Faculty of Medicine, Universidad Complutense de Madrid , Madrid, Spain .
authors.forename: Alberto
authors.initials: A
authors.lastname: Muñoz
authors.affiliation: 1 Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid , Madrid, Spain .
authors.forename: José
authors.initials: J
authors.lastname: Crespo
mesh_terms: D006801:Humans; D020407:Internet; D000070516:Intersectoral Collaboration; D017432:Unified Medical Language System / Q000592:standards*; D018875:Vocabulary, Controlled; D000071244:Zika Virus / Q000145:classification*; D000071243:Zika Virus Infection / Q000145:classification* / Q000175:diagnosis*
keywords: UMLS; Zika virus; collaboration; medical informatics