95051 20220426091144.0 doi 10.2196/18150 sideral 118509 ART-2020-118509 eng Kardas, Przemyslaw The need for patient adherence standard measures for Big Data 2020 Access copy available to the general public Unrestricted Despite half a century of dedicated studies, medication adherence remains far from perfect, with many patients not taking their medications as prescribed. The magnitude of this problem is rising, jeopardizing the effectiveness of evidence-based therapies. An important reason for this is the unprecedented demographic change at the beginning of 21st century. Ageing leads to multimorbidity and complex therapeutic regimens that create fertile ground for non-adherence. As this scenario is a global problem, it needs a worldwide answer. Might this answer be provided, given the new opportunities created by the digitization of healthcare? Day by day health-related information is collected in electronic health records, pharmacy dispensing databases, health insurance systems and national health system records. These Big Data repositories offer a unique chance to study adherence both retrospectively and prospectively, at population level, as well as its related factors. In order to make the full use of this opportunity, there is a need to develop standardised measures of adherence, which can be applied globally to Big Data and will inform scientific research, clinical practice and public health. These standardized measures may also enable a better understanding of the relationship between adherence and clinical outcomes, and allow for fair benchmarking of effectiveness and cost-effectiveness of adherence-targeting interventions. Unfortunately, despite this obvious need, such standards are still lacking. Therefore, the aim of this paper is to call for producing a consensus on global standards for measuring adherence with Big Data. More specifically, sound standards of formatting, and analysing Big Data are needed in order to assess, uniformly present and compare patterns of medication adherence across studies. Wide use of these standards may improve adherence, and make healthcare systems more effective and sustainable. info:eu-repo/semantics/openAccess by http://creativecommons.org/licenses/by/3.0/es/ 5.428 2020 MEDICAL INFORMATICS 5 / 30 = 0.167 2020 Q1 T1 HEALTH CARE SCIENCES & SERVICES 10 / 108 = 0.093 2020 Q1 T1 1.446 2020 Health Informatics 2020 Q1 info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion Aguilar-Palacio, Isabel Universidad de Zaragoza (orcid)0000-0001-7293-701X Almada, Marta Cahir, Caitriona Costa, Elíseo Giardini, Anna Malo, Sara Universidad de Zaragoza (orcid)0000-0002-7194-8275 Massot Mesquida, Mireia Menditto, Enrica Midão, Luis Parra-Calderón, Carlos Luis Pepiol Salom, Enrique Vrijens, Bernard 1011 615 Universidad de Zaragoza Dpto. Microb.Ped.Radio.Sal.Pú. Área Medic.Prevent.Salud Públ. 22, 8 (2020), e18150 [21 pp.] JMIR, J. med. internet res. Journal of Medical Internet Research 1438-8871 514453 http://zaguan.unizar.es/record/95051/files/texto_completo.pdf Versión publicada 206935 http://zaguan.unizar.es/record/95051/files/texto_completo.jpg?subformat=icon icon Versión publicada oai:zaguan.unizar.es:95051 articulos driver 2022-04-26-08:58:56 ARTICLE