000096080 001__ 96080 000096080 005__ 20210902121610.0 000096080 0247_ $$2doi$$a10.1016/j.jbi.2019.103305 000096080 0248_ $$2sideral$$a115725 000096080 037__ $$aART-2020-115725 000096080 041__ $$aeng 000096080 100__ $$0(orcid)0000-0003-2608-6431$$aRoca, Surya$$uUniversidad de Zaragoza 000096080 245__ $$aMicroservice chatbot architecture for chronic patient support 000096080 260__ $$c2020 000096080 5060_ $$aAccess copy available to the general public$$fUnrestricted 000096080 5203_ $$aChatbots are able to provide support to patients suffering from very different conditions. Patients with chronic diseases or comorbidities could benefit the most from chatbots which can keep track of their condition, provide specific information, encourage adherence to medication, etc. To perform these functions, chatbots need a suitable underlying software architecture. In this paper, we introduce a chatbot architecture for chronic patient support grounded on three pillars: scalability by means of microservices, standard data sharing models through HL7 FHIR and standard conversation modeling using AIML. We also propose an innovative automation mechanism to convert FHIR resources into AIML files, thus facilitating the interaction and data gathering of medical and personal information that ends up in patient health records. To align the way people interact with each other using messaging platforms with the chatbot architecture, we propose these very same channels for the chatbot-patient interaction, paying special attention to security and privacy issues. Finally, we present a monitored-data study performed in different chronic diseases, and we present a prototype implementation tailored for one specific chronic disease, psoriasis, showing how this new architecture allows the change, the addition or the improvement of different parts of the chatbot in a dynamic and flexible way, providing a substantial improvement in the development of chatbots used as virtual assistants for chronic patients. 000096080 536__ $$9info:eu-repo/grantAgreement/ES/DGA/T31-17R$$9info:eu-repo/grantAgreement/ES/MINECO/BES-2017-082017$$9info:eu-repo/grantAgreement/ES/MINECO/TIN2016-76770-R 000096080 540__ $$9info:eu-repo/semantics/openAccess$$aby-nc-nd$$uhttp://creativecommons.org/licenses/by-nc-nd/3.0/es/ 000096080 590__ $$a6.317$$b2020 000096080 591__ $$aMEDICAL INFORMATICS$$b3 / 30 = 0.1$$c2020$$dQ1$$eT1 000096080 591__ $$aCOMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS$$b13 / 112 = 0.116$$c2020$$dQ1$$eT1 000096080 592__ $$a1.056$$b2020 000096080 593__ $$aHealth Informatics$$c2020$$dQ1 000096080 593__ $$aComputer Science Applications$$c2020$$dQ1 000096080 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/acceptedVersion 000096080 700__ $$0(orcid)0000-0001-8518-6884$$aSancho, Jorge$$uUniversidad de Zaragoza 000096080 700__ $$0(orcid)0000-0001-9485-7678$$aGarcía, José$$uUniversidad de Zaragoza 000096080 700__ $$0(orcid)0000-0002-5254-1402$$aAlesanco, Álvaro$$uUniversidad de Zaragoza 000096080 7102_ $$15008$$2560$$aUniversidad de Zaragoza$$bDpto. Ingeniería Electrón.Com.$$cÁrea Ingeniería Telemática 000096080 773__ $$g102 (2020), 103305 1-9$$pJ. Biomed. Inform$$tJournal of Biomedical Informatics$$x1532-0464 000096080 8564_ $$s10366696$$uhttps://zaguan.unizar.es/record/96080/files/texto_completo.pdf$$yPostprint 000096080 8564_ $$s232445$$uhttps://zaguan.unizar.es/record/96080/files/texto_completo.jpg?subformat=icon$$xicon$$yPostprint 000096080 909CO $$ooai:zaguan.unizar.es:96080$$particulos$$pdriver 000096080 951__ $$a2021-09-02-08:40:46 000096080 980__ $$aARTICLE