SNOWDROP: Big Data for the joint management of medication-related falls for senior citizens
Big Data for the collaborative management of medication-related falls for the elderly. In the elderly, falls are the leading cause of injury. Medications are a major risk factor for falls. Because we lack tools to assess individualized risk, general practitioners (GPs) struggle with fall-related medication management for seniors and senior patients are not well equipped to engage in collaborative management of their medications.
Our goal in this project is to develop and evaluate a comprehensive data-driven scientific approach for valid prediction of personal fall risk that effectively supports joint medication management between seniors and family physicians.
The project has two objectives:
- Develop and validate prediction models based on electronic medical records (EMR) for assessing individualized risk of medication-related falls, and understand model limitations;
- Develop and evaluate a collaborative medication management strategy for older patients and family physicians. The prediction model, along with statements, will be embedded in NHGdoc, a decision support system, and provide personalized information relevant to GPs through their EMR, and personalized information relevant to patients through a patient portal.
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Project news
NWO interview - Predicting and preventing falls
People over the age of 65 have a high chance of tripping and falling, and medication use is most often the cause of this. However, which individuals are most at risk and how can that risk specifically be reduced? The big data project SNOWDROP is deve...
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