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Process mining methodology for health process tracking using real-time indoor location systems

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Process mining methodology for health process tracking using real-time indoor location systems

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dc.contributor.author Fernández Llatas, Carlos es_ES
dc.contributor.author Lizondo, Aroa es_ES
dc.contributor.author Montón Sánchez, Eduardo es_ES
dc.contributor.author Benedí Ruiz, José Miguel es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.date.accessioned 2016-05-16T15:57:59Z
dc.date.available 2016-05-16T15:57:59Z
dc.date.issued 2015-11
dc.identifier.issn 1424-8220
dc.identifier.uri http://hdl.handle.net/10251/64124
dc.description.abstract [EN] The definition of efficient and accurate health processes in hospitals is crucial for ensuring an adequate quality of service. Knowing and improving the behavior of the surgical processes in a hospital can improve the number of patients that can be operated on using the same resources. However, the measure of this process is usually made in an obtrusive way, forcing nurses to get information and time data, affecting the proper process and generating inaccurate data due to human errors during the stressful journey of health staff in the operating theater. The use of indoor location systems can take time information about the process in an unobtrusive way, freeing nurses, allowing them to engage in purely welfare work. However, it is necessary to present these data in a understandable way for health professionals, who cannot deal with large amounts of historical localization log data. The use of process mining techniques can deal with this problem, offering an easily understandable view of the process. In this paper, we present a tool and a process mining-based methodology that, using indoor location systems, enables health staff not only to represent the process, but to know precise information about the deployment of the process in an unobtrusive and transparent way. We have successfully tested this tool in a real surgical area with 3613 patients during February, March and April of 2015. es_ES
dc.description.sponsorship The authors want to acknowledge the work MySphera Company and Hospital General for their invaluable support. This work was supported in part by several projects; FASyS-Absolutely Safe and Healthy Factory (Spanish Ministry of Industry. CEN-20091034), MOSAIC-Models and simulation techniques for discovering diabetes influence factors (ICT-FP7-600914) and HEARTWAYS-Advanced Solutions for Supporting Cardiac Patients in Rehabilitation (ICT-SME-315659) EU Projects; and organizations like Tecnologias para la Salud y el Bienestar (TSB S.A.) and the Universitat Politecnica de Valencia. en_EN
dc.language Inglés es_ES
dc.publisher MDPI es_ES
dc.relation.ispartof Sensors es_ES
dc.rights Reconocimiento (by) es_ES
dc.subject Process mining es_ES
dc.subject Indoor location systems es_ES
dc.subject Health process es_ES
dc.subject.classification LENGUAJES Y SISTEMAS INFORMATICOS es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title Process mining methodology for health process tracking using real-time indoor location systems es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.3390/s151229769
dc.relation.projectID info:eu-repo/grantAgreement/MICINN//CEN-20091034/ES/FÁBRICA ABSOLUTAMENTE SEGURA Y SALUDABLE/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/600914/EU/MOSAIC - MOdels and Simulation techniques for discovering diAbetes Influence faCtors/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/EC/FP7/315659/EU/HeartWays - Advanced Solutions for Supporting Cardiac Patients in Rehabilitation/
dc.rights.accessRights Abierto es_ES
dc.contributor.affiliation Universitat Politècnica de València. Instituto Universitario de Aplicaciones de las Tecnologías de la Información - Institut Universitari d'Aplicacions de les Tecnologies de la Informació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Sistemas Informáticos y Computación - Departament de Sistemes Informàtics i Computació es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería Electrónica - Departament d'Enginyeria Electrònica es_ES
dc.description.bibliographicCitation Fernández Llatas, C.; Lizondo, A.; Montón Sánchez, E.; Benedí Ruiz, JM.; Traver Salcedo, V. (2015). Process mining methodology for health process tracking using real-time indoor location systems. Sensors. 12:29821-29840. https://doi.org/10.3390/s151229769 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion http://dx.doi.org/10.3390/s151229769 es_ES
dc.description.upvformatpinicio 29821 es_ES
dc.description.upvformatpfin 29840 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 12 es_ES
dc.relation.senia 303422 es_ES
dc.identifier.pmid 26633395 en_EN
dc.identifier.pmcid PMC4721690 en_EN
dc.contributor.funder European Commission
dc.contributor.funder Ministerio de Ciencia e Innovación es_ES
dc.contributor.funder Universitat Politècnica de València es_ES
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