A Multistakeholder-Centric Data Analytics Governance Framework for Medication Adherence and Improvement in Rural Settings

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Abstract

Good medication adherence is directly proportional to good health recovery and general improvement of a patient’s health condition. Although many good medication adherence monitoring methods/techniques exist, the level of medication adherence for some chronic diseases by patients in rural settings is still suboptimal. Hence, the need for healthcare organisations to devise viable governance frameworks that will facilitate effective medication adherence monitoring and improved adherence by patients. This paper presents the conceptual overview of a governance framework for medication adherence monitoring and improvement that enables the collaboration of multiple stakeholders and data analytics (MUCODAF) in support of the patient in the treatment journey. The framework allows relevant stakeholders such as Healthcare workers (HCW), family members, and close friends to collaborate in support of a patient through the engagement of critical human factors such as empathy, motivation, encouragement, flexibility, and negotiation. The use cases of the framework, its technical composition, and the implementation plan are discussed in this paper. A concrete example of the application of the governance framework for medication adherence monitoring and improvement for a Tuberculosis patient in the African Country of Lesotho is presented to highlight the plausibility of the framework.

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APA

Daramola, O., & Nyasulu, P. (2020). A Multistakeholder-Centric Data Analytics Governance Framework for Medication Adherence and Improvement in Rural Settings. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 12067 LNCS, pp. 402–413). Springer. https://doi.org/10.1007/978-3-030-45002-1_35

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