BEhavioral and Adherence Model for Improving Quality, Health Outcomes and Cost-Effectiveness of healthcaRe

Condition(s) studied

Investigational drug(s) / intervention(s)

B-COMPASS implementation: The patients in the intervention arm will receive B-COMPASS enhanced engagement in addition to standard care. The enhanced engagement is implemented as educational material to the HCP who is engaging with the patient. The content of the educational material will be based on the patient's B-COMPASS patient description. The engagement will either be in person or via phone call depending on the patient visiting schedule of each recruited patient.

Study summary

Lack of adherence to treatment is a widespread issue worldwide, which leads to higher healthcare utilisation rates and even premature death. While the level of adherence may differ based on the specific condition and treatment, studies estimate that approximately 50% of medications are not taken according to the prescribed instructions. In addition, adherence rates tend to decrease even further when the treatment requires a behavioural change. Literature reviews about factors that affect people's adherence show that it is challenging to predict whom can be considered to have adherent and non-adherent behaviours. In addition, the studies highlight that it is challenging to support a person to be adherent. Based on this knowledge the BEAMER project was established (Behavioural and Adherence Model for improving quality, health outcomes and cost-Effectiveness of healthcaRe). The overall goal of the project is to improve the quality of life of individuals, enhance healthcare accessibility and sustainability, thereby transforming the way healthcare stakeholders engage with patients to understand their condition and adherence levels throughout their healthcare journey. To address the overall goal, the BEAMER project has developed a disease agnostic model named "B-COMPASS: BEAMER-COmputational Model for Patient Adherence and Support Solutions". The aim of the B-COMPASS is to identify patients' needs and preferences which enables the creation of patient-specific supports, with the intention of improving their adherence to treatment within the heterogeneity of the different disease-areas and healthcare contexts. Based on the validated BEAMER questionnaire, the B-COMPASS predicts relative adherence and offers an elicitation process of patient needs and preferences to enable targeted supports to improve patient adherence. This results in an allocation of patients to different groups based on their needs and preferences. Overall, the B-COMPASS provides patient insights that will enable more effective design of patient support, most likely resulting in better patient experience, improved adherence and lower healthcare and societal costs.

So far, several activities from a technical and user perspective have already been conducted in the project to refine the B-COMPASS. This has been done by applying an iterative mixed method approach were both stakeholders (regulator, pharma, academic/research and small and medium-sized enterprises) and end users (patients, health providers and health systems) have been involved. Despite the finetuning of the B-COMPASS, the effectiveness of the B-COMPASS hinges on empirical investigations into the structural elements that impact patient behaviour and the identification of predictive factors that can assist healthcare providers' (HCP) and Research Leads in designing more effective treatment plans (the term HCPs/Research Lead include both the individuals and the institutions where care is delivered). Therefore, validation studies will be conducted to assess the B-COMPASS's performance in six therapeutic areas (cardiovascular, endocrinology, immunology, neurology, oncology and rare diseases) with patients recruited in at least Italy (FISM), Portugal (APDP and MEDIDA) Norway (AHUS), Spain (FHUNJ and FIIBAP), The Netherlands (WDO), and Germany (UDUS). The collected data will be used to evaluate the B-COMPASS's capacity to attend to a variety of needs and challenges for adherence.

Eligibility

Primary outcome measure(s)

• Accuracy of the B-COMPASS to predict adherence — At first data collection
  Predictive accuracy of B-COMPASS of adherence to treatment will be validated against TAP-Q in Visit 1 and other specific adherence measurements for all participants. The B-COMPASS is an adherence prediction model based on the BEAMER questionnaire, which is a questionnaire that contains 22 questions about subjective health experience and subjective awareness of health condition. Likert scale with 7 response options, 1 = Fully disagree, 7 = Fully agree (11 questions). Likert scale with 6 response options, 1 = Fully disagree, 6 = Fully agree (4 questions). Likert scale with 5 response options, 1 = Fully disagree, 5 = Fully agree (5 questions). Score on a 10-step ladder where step 1 = The day at the past 4 weeks I felt at my worst, step 10 = The day in the past 4 weeks I felt at my best (2 questions).
• Validity of B-COMPASS groupings — At second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.
  B-COMPASS groupings will be validated through the qualitative thematic analysis of interviews and focus groups with patients and HCPs in the intervention groups.
• Accuracy of identification of patients' support needs — At second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.
  The accuracy of the support needs identified by the B-COMPASS will also be analysed through qualitative analysis of focus groups and interviews with the intervention groups.
• Reliability of B-COMPASS prediction over time — At first data collection and then aging at second data collection which is 2 weeks - 6 months after first data collection (first and second data collection). It varies between pilot sites and disease area.
  The over-time reliability of the B-COMPASS will be measured by comparing its groupings and adherence prediction results at the first and second visits for the control group.

Trial sites (10)

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