Institute of Management, Scuola Superiore Sant'Anna, Pisa, Italy.
BMC Health Serv Res. 2020 Jul 24;20(1):688. doi: 10.1186/s12913-020-05544-4.
Medication adherence has been studied in different settings, with different approaches, and applying different methodologies. Nevertheless, our knowledge and efficacy are quite limited in terms of measuring and evaluating all the variables and components that affect the management of medication adherence regimes as a complex phenomenon. The study aim is mapping the state-of-the-art of medication adherence measurement and assessment methods applied in chronic conditions. Specifically, we are interested in what methods and assessment procedures are currently used to tackle medication adherence. We explore whether Big Data techniques are adopted to improve decision-making procedures regarding patients' adherence, and the possible role of digital technologies in supporting interventions for improving patient adherence and avoiding waste or harm.
A scoping literature review and bibliometric analysis were used. Arksey and O'Malley's framework was adopted to scope the review process, and a bibliometric analysis was applied to observe the evolution of the scientific literature and identify specific characteristics of the related knowledge domain.
A total of 533 articles were retrieved from the Scopus academic database and selected for the bibliometric analysis. Sixty-one studies were identified and included in the final analysis. The Morisky medication adherence scale (36%) was the most frequently adopted baseline measurement tool, and cardiovascular/hypertension disease, the most investigated illness (38%). Heterogeneous findings emerged from the types of study design and the statistical methodologies used to assess and compare the results.
Our findings reveal a lack of Big Data applications currently deployed to address or measure medication adherence in chronic conditions. Our study proposes a general framework to select the methods, measurements and the corpus of variables in which the treatment regime can be analyzed.
药物依从性已在不同环境下进行了研究，采用了不同的方法，并应用了不同的方法。然而，在衡量和评估所有影响药物依从性管理的变量和因素方面，我们的知识和效果相当有限，因为这是一个复杂的现象。本研究旨在绘制在慢性病中应用的药物依从性测量和评估方法的最新情况。具体来说，我们感兴趣的是目前用于解决药物依从性的方法和评估程序。我们探讨了大数据技术是否被用于改善关于患者依从性的决策程序，以及数字技术在支持改善患者依从性和避免浪费或伤害的干预措施中的可能作用。
采用了范围综述和文献计量分析。采用了阿克塞尔和奥马利的框架来确定综述过程的范围，并进行了文献计量分析，以观察科学文献的演变并确定相关知识领域的特定特征。
从 Scopus 学术数据库中检索到 533 篇文章，并对其进行了文献计量分析。确定了 61 项研究并将其纳入最终分析。Morisky 药物依从性量表（36%）是最常采用的基线测量工具，心血管/高血压疾病（38%）是研究最多的疾病。从研究设计类型和用于评估和比较结果的统计方法来看，结果存在异质性。
我们的发现表明，目前尚未部署大数据应用来解决或衡量慢性病中的药物依从性。我们的研究提出了一个通用框架，用于选择方法、测量和变量的语料库，在其中可以分析治疗方案。
