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An analysis of medication adherence through Electronic Health Records (EHR) data

As researchers in Pharmacoepidemiology and Pharmacovigilance focused on studying the use, adherence, effectiveness and safety of drugs in different conditions and populations in Primary Care using data proceeding from the Electronic Health Records (EHR), understanding and managing such massive data poses a significant challenge for our research group. We have access to a large amount of data on drug exposure in the EHR obtained from the electronic prescription and invoice data generated from the dispensing of drugs reimbursed by the National Health system.

Our focus on the use and adherence to drugs involves determining the extent to which patients collect their medications from the pharmacy as prescribed. However, it remains challenging to ascertain the actual intake of medications at home. Identifying when a patient switches, discontinues or intensifies a specific drug treatment is often intricate due to complex treatment patterns, making analysis and interpretation difficult. Failure to assign the correct drug treatments to patient data can introduce errors when studying medication adherence.

To address this issue, our team here at the BEAMER project has designed a novel algorithm known as Smooth, aiming to obtain the most probable treatment patterns of a single drug in monotherapy or combined drugs over time to study treatment switch, addition, discontinuation and adherence using EHR. The Smooth algorithm is an automated approach that standardizes, simplifies and improves data processing in pharmacoepidemiological studies analyzing drug exposure data.

The algorithm has undergone validation using clinical data and was recently published in the JMIR Medical Informatics journal. Its implementation will facilitate the analysis of drug exposure and adherence data for BEAMER’s task 3.1 and task 2 within our specific setting.

We also developed an R package called smoothy which can be used to model drug exposure in pharmacoepidemiological studies, thereby allowing the study of adherence with real-world data available in our setting

A recent example of our research using the Smooth algorithm is an observational study in a large cohort of atrial fibrillation patients treated with oral anticoagulants (OAC) for stroke prevention in Catalonia, Spain, spanning from 2011 to 2020. 

Our first article published within this study described sex and gender differences in patients starting OAC treatment, calculating adherence and persistence for those receiving direct OAC. We found a frequency of underdosing (ranging from 15-39%, depending on the drug), yet over 80% of patients exhibited adherence to these drugs.

In the second article published, we found that being adherent to direct OAC in comparison to those people who were non-adherent to medication resulted in a lower risk of suffering a stroke and having cerebral bleeding. We concluded that considering patients’ baseline characteristics and comorbidities when prescribing OAC is crucial for maximising the clinical benefit, particularly in stroke prevention.

The Smooth algorithm’s validation with different types of medications allows for its generalization to model drug exposure data and study treatment switches, additions, discontinuations, or adherence across various treatments.

About the authors:
Maria Giner-Soriano, PharmD, PhD. Pharmacoepidemiology Researcher IDIAPJGol, Barcelona, Spain.

Rosa Morros Pedrós, Silvia Fernández García, IDIAPJGol, Barcelona, Spain.

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Healthcare Professional

The primary aim of the end user personas is to support the creation of materials to support the implementation of the BEAMER model framework and to help define requirements for the elements of the BEAMER model framework. Hence, healthcare professionals (HCPs) represent the primary envisaged end user group of the BEAMER model framework and the associated Adherence Intelligence Visualisation Platform (AIVP)

It is one learning from the joint design process that the job titles of healthcare professional team members do not necessarily predict the roles they would play within the change management process for implementing BEAMER and installing it as a standard model within healthcare. Additionally, the role and responsibilities of certain job titles, for example nurse, varies across different healthcare systems and would affect how they interact with the BEAMER model outputs and the access they would be permitted and so it would not be helpful to include these job titles: The four personas represent role-independent archetypes within the group of HCPs. They encompass a Managerial HCP Persona, an Implementer HCP Persona, a Support HCP Persona, and a Established HCP Persona.

These healthcare professional personas may be further tailored to specific healthcare settings depending on the needs of the individual pilot sites. Thus, adapted or spin-off versions of these original personas may be considered. The persona displays include a summarising statement, goals, challenges, experience, and needs to enhance the accessibility and usability of the model while minimising user burden.

Patient Organisation

Patient organisations are considered potential users of the model outputs. Consequently, personas were designed for these groups to assure that the implementation materials may also support their needs in the longer term, thus fostering sustainability of the project outputs.

The identified focus areas within this persona are goals, needs, skills and tools, along with potential challenges anticipated during the implementation process. The persona emphasises awareness-raising, capacity building, education, peer support provision, and the promotion of research and development in therapeutic care.

The patient organisation persona serves as a theoretical framework representing how patient organisations could benefit from and include the BEAMER model framework in their therapy and care related as well as their organisational work. This persona comprises the needs, goals, challenges and necessary tools, facilitating preparation and implementation of the model and optimising the user experience of patient organisations as end users of the BEAMER model framework. It can be used as a guide to identify potential obstacles and understand the prerequisites for a patient organisation to successfully adopt and integrate the BEAMER model framework.

“In implementing the BEAMER model, we want to be able to respond to the different needs of our patients to ensure their adherence, build a supportive community and improve outcomes.”