000095051 001__ 95051 000095051 005__ 20220426091144.0 000095051 0247_ $$2doi$$a10.2196/18150 000095051 0248_ $$2sideral$$a118509 000095051 037__ $$aART-2020-118509 000095051 041__ $$aeng 000095051 100__ $$aKardas, Przemyslaw 000095051 245__ $$aThe need for patient adherence standard measures for Big Data 000095051 260__ $$c2020 000095051 5060_ $$aAccess copy available to the general public$$fUnrestricted 000095051 5203_ $$aDespite 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. 000095051 540__ $$9info:eu-repo/semantics/openAccess$$aby$$uhttp://creativecommons.org/licenses/by/3.0/es/ 000095051 590__ $$a5.428$$b2020 000095051 591__ $$aMEDICAL INFORMATICS$$b5 / 30 = 0.167$$c2020$$dQ1$$eT1 000095051 591__ $$aHEALTH CARE SCIENCES & SERVICES$$b10 / 108 = 0.093$$c2020$$dQ1$$eT1 000095051 592__ $$a1.446$$b2020 000095051 593__ $$aHealth Informatics$$c2020$$dQ1 000095051 655_4 $$ainfo:eu-repo/semantics/article$$vinfo:eu-repo/semantics/publishedVersion 000095051 700__ $$0(orcid)0000-0001-7293-701X$$aAguilar-Palacio, Isabel$$uUniversidad de Zaragoza 000095051 700__ $$aAlmada, Marta 000095051 700__ $$aCahir, Caitriona 000095051 700__ $$aCosta, Elíseo 000095051 700__ $$aGiardini, Anna 000095051 700__ $$0(orcid)0000-0002-7194-8275$$aMalo, Sara$$uUniversidad de Zaragoza 000095051 700__ $$aMassot Mesquida, Mireia 000095051 700__ $$aMenditto, Enrica 000095051 700__ $$aMidão, Luis 000095051 700__ $$aParra-Calderón, Carlos Luis 000095051 700__ $$aPepiol Salom, Enrique 000095051 700__ $$aVrijens, Bernard 000095051 7102_ $$11011$$2615$$aUniversidad de Zaragoza$$bDpto. Microb.Ped.Radio.Sal.Pú.$$cÁrea Medic.Prevent.Salud Públ. 000095051 773__ $$g22, 8 (2020), e18150 [21 pp.]$$pJMIR, J. med. internet res.$$tJournal of Medical Internet Research$$x1438-8871 000095051 8564_ $$s514453$$uhttps://zaguan.unizar.es/record/95051/files/texto_completo.pdf$$yVersión publicada 000095051 8564_ $$s206935$$uhttps://zaguan.unizar.es/record/95051/files/texto_completo.jpg?subformat=icon$$xicon$$yVersión publicada 000095051 909CO $$ooai:zaguan.unizar.es:95051$$particulos$$pdriver 000095051 951__ $$a2022-04-26-08:58:56 000095051 980__ $$aARTICLE