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Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers

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Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers

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dc.contributor.author Marco-Ruiz, Luis es_ES
dc.contributor.author Bones, Erlend es_ES
dc.contributor.author de la Asuncion, Estela es_ES
dc.contributor.author Gabarron, Elia es_ES
dc.contributor.author Aviles-Solis, Juan Carlos es_ES
dc.contributor.author Lee, Eunji es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.contributor.author Sato, Keiichi es_ES
dc.contributor.author Bellika, Johan G. es_ES
dc.date.accessioned 2020-10-17T03:32:19Z
dc.date.available 2020-10-17T03:32:19Z
dc.date.issued 2017-10 es_ES
dc.identifier.issn 1532-0464 es_ES
dc.identifier.uri http://hdl.handle.net/10251/152267
dc.description.abstract [EN] Symptom checkers are software tools that allow users to submit a set of symptoms and receive advice related to them in the form of a diagnosis list, health information or triage. The heterogeneity of their potential users and the number of different components in their user interfaces can make testing with end-users unaffordable. We designed and executed a two-phase method to test the respiratory diseases module of the symptom checker Erdusyk. Phase I consisted of an online test with a large sample of users (n = 53). In Phase I, users evaluated the system remotely and completed a questionnaire based on the Technology Acceptance Model. Principal Component Analysis was used to correlate each section of the interface with the questionnaire responses, thus identifying which areas of the user interface presented significant contributions to the technology acceptance. In the second phase, the think-aloud procedure was executed with a small number of samples (n = 15), focusing on the areas with significant contributions to analyze the reasons for such contributions. Our method was used effectively to optimize the testing of symptom checker user interfaces. The method allowed kept the cost of testing at reasonable levels by restricting the use of the think-aloud procedure while still assuring a high amount of coverage. The main barriers detected in Erdusyk were related to problems understanding time repetition patterns, the selection of levels in scales to record intensities, navigation, the quantification of some symptom attributes, and the characteristics of the symptoms. (C) 2017 Elsevier Inc. All rights reserved. es_ES
dc.description.sponsorship This work was supported by Helse Nord [grant HST1121-13], the Faculty of Health Sciences from UIT The Arctic University of Norway [researcher code 1108], and The Research Council of Norway [grant 248150/O70]. We thank Professor Emeritus Rafael Romero-Villafranca for reviewing the statistical analysis of this paper. es_ES
dc.language Inglés es_ES
dc.publisher Elsevier es_ES
dc.relation Helse Nord RHF/HST1121-13 es_ES
dc.relation RCN/248150/O70 es_ES
dc.relation.ispartof Journal of Biomedical Informatics es_ES
dc.rights Reconocimiento - No comercial - Sin obra derivada (by-nc-nd) es_ES
dc.subject Human computer interaction es_ES
dc.subject Usability testing es_ES
dc.subject Clinical decision support systems es_ES
dc.subject Symptom checkers es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1016/j.jbi.2017.09.002 es_ES
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Marco-Ruiz, L.; Bones, E.; De La Asuncion, E.; Gabarron, E.; Aviles-Solis, JC.; Lee, E.; Traver Salcedo, V.... (2017). Combining multivariate statistics and the think-aloud protocol to assess Human-Computer Interaction barriers in symptom checkers. Journal of Biomedical Informatics. 74:104-122. https://doi.org/10.1016/j.jbi.2017.09.002 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1016/j.jbi.2017.09.002 es_ES
dc.description.upvformatpinicio 104 es_ES
dc.description.upvformatpfin 122 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 74 es_ES
dc.identifier.pmid 28893671 es_ES
dc.relation.pasarela S\359733 es_ES
dc.contributor.funder Helse Nord RHF es_ES
dc.contributor.funder Research Council of Norway es_ES
dc.contributor.funder UiT The Arctic University of Norway es_ES


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