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SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project

Authors Leonie Westerbeek , Noman Dormosh , André Blom, Martijn Heymans , Meefa Hogenes, Annemiek Linn , Stephanie Medlock, Martijn Schut, Nathalie van der Velde , Henk van Weert , Julia van Weert , Ameen Abu-Hanna

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Author Details

Leonie Westerbeek
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Noman Dormosh
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
André Blom
  • Uw Zorg Online, Amsterdam, The Netherlands
Martijn Heymans
  • Department of Epidemiology and Data Science, Amsterdam UMC location Vrije Universiteit , Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Meefa Hogenes
  • Department of Medical Content, ExpertDoc, Rotterdam, The Netherlands
Annemiek Linn
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Stephanie Medlock
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Martijn Schut
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Department of Laboratory Medicine, Amsterdam UMC location Vrije Universiteit Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Nathalie van der Velde
  • Department of Internal Medicine, Section of Geriatric Medicine, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Henk van Weert
  • Department of General Practice, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Julia van Weert
  • Amsterdam School of Communication Research/ASCoR, University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands
Ameen Abu-Hanna
  • Department of Medical Informatics, Amsterdam UMC location University of Amsterdam, The Netherlands
  • Amsterdam Public Health Research Institute, The Netherlands

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Leonie Westerbeek, Noman Dormosh, André Blom, Martijn Heymans, Meefa Hogenes, Annemiek Linn, Stephanie Medlock, Martijn Schut, Nathalie van der Velde, Henk van Weert, Julia van Weert, and Ameen Abu-Hanna. SeNiors empOWered via Big Data to Joint-Manage Their Medication-Related Risk of Falling in Primary Care: The SNOWDROP Project. In Commit2Data. Open Access Series in Informatics (OASIcs), Volume 124, pp. 4:1-4:12, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/OASIcs.Commit2Data.4

Abstract

In older persons, falls are the leading cause of injuries, often resulting in emergency room visits, serious injuries, and possibly even death. 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 personalized risk of falling that effectively supports joint medication management between seniors and GPs. The project has two objectives. First, we aim to develop and validate prediction models from electronic health records for assessing individualized risk of medication-related falls. Data science challenges include free text analysis; accounting for missing values; searching medication hierarchies; engineering new predictors, and understanding limitations of our approach. Second, we aim to develop and evaluate a joint medication management strategy for older patients and GPs, consisting of a clinical decision support system (CDSS) and a patient portal. We evaluate the effects of this strategy on changes in the quality of shared decision-making during a medication review consultation, medication management, and patient outcomes. The learnings from this project and the architecture underpinned by predictive modelling to support both GPs and patients can also be applied to other major health problems in the future.

Subject Classification

ACM Subject Classification
  • Applied computing → Life and medical sciences
Keywords
  • accidental falls
  • fall risk
  • medication
  • prediction model
  • clinical decision support
  • patient portal
  • shared decision-making

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