Big Data for the joint management of medication-related falls for senior citizens
In older persons, falls are the leading cause of injuries. Medications are a major risk factor for falls. Because we lack tools to assess individualized risks, general practitioners (GPs) struggle with fall-related medication management for seniors, and senior patients are not properly equipped to engage in the joint management of their medications.
Our aim in this project is to develop and evaluate a comprehensive data-driven science approach for valid prediction of personalised risk of falling that effectively supports joint medication management between seniors and GPs. The project has two objectives: (1) Develop and validate prediction models from electronic medical records (EMR) for assessing individualized risk of medication-related falls, and understand model limitations; (2) Design and evaluate a joint medication management strategy for older patients and GPs. The prediction model, together with explanations, will be embedded in NHGdoc, a decision support system, and provide personalised information relevant to GPs via their EMR, and personalised information relevant to patients via a patient portal.
Partners: Amsterdam UMC Department of Medical Informatics , Elsevier BV , Pharmeon BV , ExpertDoc BV
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