OPTIMIZING CLINICAL PATHWAYS WITH FEDERATED DATA

  • Tim Müller
  • , Rosanne J. Turner
  • , Saba Amiri
  • , Corinne Allaart
  • , L. Thomas van Binsbergen
  • , Lea Dijksman
  • , Tom van Engers
  • , Adam Belloum
  • , Paola Grosso
  • , Peter Grünwald
  • , Karin Hoogoort
  • , Aki Härmä
  • , Johanna Maria Hegeman
  • , Jamila Alsayed Kassem
  • , Milen Kebede
  • , Cees de Laat
  • , Paul van der Nat
  • , Auke Pals
  • , Floortje Scheepers
  • , Sander Klous

Research output: Contribution to journalArticleAcademicpeer-review

Abstract

Clinical pathways are currently diffi cult to optimize due to sensitive data that is typically distrib uted across organizations while rules and regulations constrain access and data processing. In this paper we describe a federated approach that can significantly reduce the eff orts required to overcome these obstacles. First, we describe a standard conceptual workflow for optimizing clinical pathways, including all steps and involved stakeholders. This is followed by a translation of the workflow into a real-world scenario with an associated proof of principle to demonstrate how the scenario can be implemented on top of a federated framework. We present the most important results and conclude with an overview of the benefits for each of the stakeholders. Our most important outcomes are: the federated approach off ers significant benefits for all relevant stakeholders and has little downsides. A policy-driven framework with embedded policy enforce ment is crucial for successful adoption of a federated approach. Integration of safe statistics and synthetic data generation in a federated framework is straightforward and off ers additional benefits, especially when setting up healthcare consortia. This solution is almost ready to be adopted by healthcare organizations as part of their regular operations.

Original languageEnglish
Pages (from-to)147-158
Number of pages12
JournalJusletter IT
Volume2025
DOIs
Publication statusPublished - 20 Feb 2025

Keywords

  • automated policy management
  • federated learning
  • healthcare
  • safe statistics
  • synthetic data

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