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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
Published on: October 24, 2019
Scoping review and bibliometric analysis of Big Data applications for Medication adherence: an explorative
Salvatore Pirri1, Valentina Lorenzoni2, Giuseppe Turchetti2
1Institute of Management, Scuola Superiore Sant'Anna, Pisa, Italy. s.pirri@santannapisa.it.
Background:
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.
Methods:
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.
Results:
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.
Conclusions:
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.
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