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A dashboard-based system for supporting diabetes care

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A dashboard-based system for supporting diabetes care

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dc.contributor.author Dagliati, Arianna es_ES
dc.contributor.author Sacchi, Lucia es_ES
dc.contributor.author Tibollo, V. es_ES
dc.contributor.author Cogni, G. es_ES
dc.contributor.author Teliti, M. es_ES
dc.contributor.author Martinez-Millana, Antonio es_ES
dc.contributor.author Traver Salcedo, Vicente es_ES
dc.contributor.author Segagni, Daniele es_ES
dc.contributor.author Posada, J. es_ES
dc.contributor.author Ottaviano, M. es_ES
dc.contributor.author Fico, Giuseppe es_ES
dc.contributor.author Arredondo, M.T. es_ES
dc.contributor.author De Cata, P. es_ES
dc.contributor.author Chiovato, L. es_ES
dc.contributor.author Bellazzi, Riccardo es_ES
dc.date.accessioned 2020-04-28T06:01:47Z
dc.date.available 2020-04-28T06:01:47Z
dc.date.issued 2018-02 es_ES
dc.identifier.issn 1067-5027 es_ES
dc.identifier.uri http://hdl.handle.net/10251/141636
dc.description.abstract [EN] Objective To describe the development, as part of the European Union MOSAIC (Models and Simulation Techniques for Discovering Diabetes Influence Factors) project, of a dashboard-based system for the management of type 2 diabetes and assess its impact on clinical practice. Methods The MOSAIC dashboard system is based on predictive modeling, longitudinal data analytics, and the reuse and integration of data from hospitals and public health repositories. Data are merged into an i2b2 data warehouse, which feeds a set of advanced temporal analytic models, including temporal abstractions, care-flow mining, drug exposure pattern detection, and risk-prediction models for type 2 diabetes complications. The dashboard has 2 components, designed for (1) clinical decision support during follow-up consultations and (2) outcome assessment on populations of interest. To assess the impact of the clinical decision support component, a pre-post study was conducted considering visit duration, number of screening examinations, and lifestyle interventions. A pilot sample of 700 Italian patients was investigated. Judgments on the outcome assessment component were obtained via focus groups with clinicians and health care managers. Results The use of the decision support component in clinical activities produced a reduction in visit duration (P¿¿¿.01) and an increase in the number of screening exams for complications (P¿<¿.01). We also observed a relevant, although nonstatistically significant, increase in the proportion of patients receiving lifestyle interventions (from 69% to 77%). Regarding the outcome assessment component, focus groups highlighted the system¿s capability of identifying and understanding the characteristics of patient subgroups treated at the center. Conclusion Our study demonstrates that decision support tools based on the integration of multiple-source data and visual and predictive analytics do improve the management of a chronic disease such as type 2 diabetes by enacting a successful implementation of the learning health care system cycle. es_ES
dc.description.sponsorship This work was supported by the European Union in the Seventh Framework Programme, grant number 600914. es_ES
dc.language Inglés es_ES
dc.publisher Oxford University Press es_ES
dc.relation.ispartof Journal of the American Medical Informatics Association es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Clinical decision support systems es_ES
dc.subject Data integration es_ES
dc.subject Temporal data analytics es_ES
dc.subject Type 2 diabetes es_ES
dc.subject.classification TECNOLOGIA ELECTRONICA es_ES
dc.title A dashboard-based system for supporting diabetes care es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1093/jamia/ocx159 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.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 Dagliati, A.; Sacchi, L.; Tibollo, V.; Cogni, G.; Teliti, M.; Martinez-Millana, A.; Traver Salcedo, V.... (2018). A dashboard-based system for supporting diabetes care. Journal of the American Medical Informatics Association. 25(5):538-547. https://doi.org/10.1093/jamia/ocx159 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1093/jamia/ocx159 es_ES
dc.description.upvformatpinicio 538 es_ES
dc.description.upvformatpfin 547 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 25 es_ES
dc.description.issue 5 es_ES
dc.identifier.pmid 29409033 es_ES
dc.relation.pasarela S\351931 es_ES
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