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The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint.

ABSTRACT: 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 the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a global problem, it needs a worldwide answer. Could this answer be provided, given the new opportunities created by the digitization of health care? Daily, health-related information is being 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 the population level, as well as its related factors. In order to make full use of this opportunity, there is a need to develop standardized 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 the 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 a consensus on global standards for measuring adherence with big data. More specifically, sound standards of formatting and analyzing 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 health care systems more effective and sustainable.

SUBMITTER: Kardas P

PROVIDER: S-EPMC7484771 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint.

Kardas Przemyslaw P Aguilar-Palacio Isabel I Almada Marta M Cahir Caitriona C Costa Elisio E Giardini Anna A Malo Sara S Massot Mesquida Mireia M Menditto Enrica E Midão Luís L Parra-Calderón Carlos Luis CL Pepiol Salom Enrique E Vrijens Bernard B

Journal of medical Internet research 20200827 8

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 the 21st century. Aging leads to multimorbidity and complex therapeutic regimens that create a fertile ground for nonadherence. As this scenario is a globa ...[more]

PMID: 32663138

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Project description:BackgroundThe ownership of patient information in the context of big data is a relatively new problem, which is not yet fully recognized by the medical academic community. The problem is interdisciplinary, incorporating legal, ethical, medical, and aspects of information and communication technologies, requiring a sophisticated analysis. However, no previous scoping review has mapped existing studies on the subject.ObjectiveThis study aims to map and assess published studies on patient data ownership in the context of big data as viewed by the academic community.MethodsA scoping review was conducted based on the 5-stage framework outlined by Arksey and O'Malley and further developed by Levac, Colquhoun, and O'Brien. The organization and reporting of results of the scoping review were conducted according to PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses and its extensions for Scoping Reviews). A systematic and comprehensive search of 4 scientific information databases, PubMed, ScienceDirect, Scopus, and Springer, was performed for studies published between January 2000 and October 2019. Two authors independently assessed the eligibility of the studies and the extracted data.ResultsThe review included 32 eligible articles authored by academicians that correspond to 3 focus areas: problem (ownership), area (health care), and context (big data). Five major aspects were studied: the scientific area of publications, aspects and academicians' perception of ownership in the context of big data, proposed solutions, and practical applications for data ownership issues in the context of big data. The aspects in which publications consider ownership of medical data are not clearly distinguished but can be summarized as ethical, legal, political, and managerial. The ownership of patient data is perceived primarily as a challenge fundamental to conducting medical research, including data sales and sharing, and to a lesser degree as a means of control, problem, threat, and opportunity also in view of medical research. Although numerous solutions falling into 3 categories, technology, law, and policy, were proposed, only 3 real applications were discussed.ConclusionsThe issue of ownership of patient information in the context of big data is poorly researched; it is not addressed consistently and in its integrity, and there is no consensus on policy decisions and the necessary legal regulations. Future research should investigate the issue of ownership as a core research question and not as a minor fragment among other topics. More research is needed to increase the body of knowledge regarding the development of adequate policies and relevant legal frameworks in compliance with ethical standards. The combined efforts of multidisciplinary academic teams are needed to overcome existing gaps in the perception of ownership, the aspects of ownership, and the possible solutions to patient data ownership issues in the reality of big data.

Project description:BackgroundPositive airway pressure (PAP) has become a prominent treatment for children with sleep-disordered breathing. However, there are no large-scale studies to clarify whether PAP is well tolerated in children, and which factors are associated with better adherence to PAP therapy. In this study, we aimed to clarify adherence patterns of PAP therapy in a large paediatric population.MethodsWe did a cross-sectional big-data analysis in children from Oct 1, 2014, to Aug 1, 2018, using existing data derived from PAP devices uploaded nightly in the AirView cloud database. The AirView database is a usage tracking system available to all patients who are assigned PAP therapy, which requires consent from the patient or parent or guardian. All patients older than 4 years and younger than 18 years who used continuous or automated PAP devices were evaluated. Only patients living in the USA and enrolled with a single insurance company were included. If patients were participating in an engagement programme, programme onset must have been within 7 days of therapy onset. Our primary outcome was the proportion of patients who used PAP continuously over 90 days. The primary outcome was assessed in all patients who met the age inclusion criterion and had reliable age data available. Data on missing PAP use were imputed as zero, but data on other metrics were not imputed and excluded from analysis.FindingsWe used data recorded from Oct 1, 2014, to Aug 1, 2018. Of 40 140 children screened, 36 058 (89·8%) were US residents and 20 553 (90·1%) of them met the eligibility criteria and had accessible data (mean age 13·0 years [SD 3·7]). On the basis of 90 days of monitoring data, 12 699 (61·8%) patients continuously used PAP. Factors significantly associated with adherence included age group, residual apnoea-hypopnoea index, use and onset of patient engagement programmes, PAP pressure, and nightly median PAP mask leak, all over the 90-day study period.InterpretationTo our knowledge, our study represents the largest analysis of children using PAP therapy to date. The findings suggest that adherence to PAP therapy is lower than in previous reports from adults. However, numerous actionable factors were associated with improvements in adherence and should be used strategically in clinical decision making to improve PAP adherence in children.FundingResMed.

Project description:BackgroundDiagnostic excellence refers to the optimal process to attain an accurate and precise explanation about a patient's condition and incorporates the perspectives of patients and their care partners. Patient-reported measures (PRMs), designed to capture patient-reported information, have potential to contribute to achieving diagnostic excellence. We aimed to craft a set of roadmaps illustrating goals and guiding the development of PRMs for diagnostic excellence ("Roadmaps").MethodsWe used iterative inputs from environmental literature scans, expert consultations, and patient voice and employed human-centred design (HCD) and equity-focused road-mapping. The culminating activity of these approaches was an Expert Convening.ResultsUse of PRMs can achieve multiple goals for diagnostic excellence, including but not limited to: (1) PRMs for diagnostic continuity, (2) diagnostic PRM alerts, (3) PRM-based quality improvement, (4) PRMs for research, (5) PRMs for routine screening, (6) PRM-based diagnostic excellence population-level patterns, and (7) PRMs supporting patient storytelling. Equity is considered as a cross-cutting goal. Altogether these and future goals support operationalising a vision of patient-reported diagnostic excellence. Roadmaps were developed as a dynamic tool to illustrate PRMs in relation to specific steps with feedback loops to accomplish goals, anticipated timeframes (8-15 years), synergies to foster, and challenges to overcome. Roadmaps are practical in their following PRMs through the stages of development, endorsement, implementation and scaling, and acting upon those measures. Timeframe estimates assume immediate transitions between these stages and no acceleration through incentives and active coordination.ConclusionPRMs for diagnostic excellence have potential to connect patient perspectives, equity, and achievable goals. Roadmaps offer a design approach to enable coordinating measurement activities among diverse stakeholders. Roadmaps also highlight versatility in ways patient-reported information can be collected and used, from clinical settings to public health contexts. Patient-reported diagnostic excellence cannot be established as a solely top-down endeavour, but inherently benefits from bottom-up approaches.

Project description:BackgroundMedication adherence is essential for improving the health outcomes of patients. Various patient-reported outcome measures (PROMs) have been developed to measure medication adherence in patients. However, no study has summarized the psychometric properties of these PROMs to guide selection for use in clinical practice or research.ObjectiveThis study aims to evaluate the quality of the PROMs used to measure medication adherence.MethodsThis study was guided by the PRISMA (Preferred Reporting Items for Systematic Review and Meta-Analysis) guidelines. Relevant articles were retrieved from the EMBASE, PubMed, Cochrane Library, Web of Science, and CINAHL (Cumulative Index to Nursing and Allied Health Literature) databases. The PROMs were then evaluated based on the COnsensus-based Standards for the selection of health Measurement Instruments (COSMIN) guidelines.ResultsA total of 121 unique medication adherence PROMs from 214 studies were identified. Hypotheses testing for construct validity and internal consistency were the most frequently assessed measurement properties. PROMs with at least a moderate level of evidence for ≥5 measurement properties include the Adherence Starts with Knowledge 20, Compliance Questionnaire-Rheumatology, General Medication Adherence Scale, Hill-Bone Scale, Immunosuppressant Therapy Barrier Scale, Medication Adherence Reasons Scale (MAR-Scale) revised, 5-item Medication Adherence Rating Scale (MARS-5), 9-item MARS (MARS-9), 4-item Morisky Medication Adherence Scale (MMAS-4), 8-item MMAS (MMAS-8), Self-efficacy for Appropriate Medication Adherence Scale, Satisfaction with Iron Chelation Therapy, Test of Adherence to Inhalers, and questionnaire by Voils. The MAR-Scale revised, MMAS-4, and MMAS-8 have been administered electronically.ConclusionsThis study identified 121 PROMs for medication adherence and provided synthesized evidence for the measurement properties of these PROMs. The findings from this study may assist clinicians and researchers in selecting suitable PROMs to assess medication adherence.

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The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint.

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The Need to Develop Standard Measures of Patient Adherence for Big Data: Viewpoint.

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