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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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