Case Study B

Antimicrobial Resistance (AMR) Forecasting in Uganda and Germany and Integration into healthcare systems

Prof. Jens Rolff, Maren Herzog, Prof. Charlotte Kloft, Sara Salehian (Freie Universität Berlin, Germany)

Dr. Miriam Stegemann, Dr. Anne Ritter, Sophie Becke, PD Dr. Ulrich Kertzscher (Charité Universitätsmedizin Berlin, Germany)

Prof. Sabine Ammon, Nils Neuhaus, Dr. Ina Peters, Prof. Dirk Oberschmidt, Christian Vehmann (Technische Universität Berlin, Germany)

Dr. Francis Williams Ojara (Gulu University, Uganda)

Rationale

The primary goal of this case study (CS) is to test and refine forecasting technologies within AMS frameworks in two distinct healthcare settings - Germany and Uganda. These countries serve as proxies for high- and low-income settings, respectively, and provide a comparative landscape to assess how forecasting technologies can be implemented and scaled across diverse healthcare systems. In both settings, we will explore experimental, computational and social scientific methods to predict the emergence of resistance, with a particular focus on evaluating treatment regimens and optimizing antimicrobial use to prevent resistance. By testing the forecasting device in parallel in Uganda and Germany, we aim to understand how factors like healthcare infrastructure, antibiotic availability, and microbiological capabilities influence the integration and effectiveness of these tools in AMS programs.

Main Research Questions

1
What are the optimal pathways for integrating forecasting technologies into Antimicrobial Stewardship (AMS) programs across diverse healthcare system contexts?
2
Which features must a stakeholder-driven redesign of the device include to foster wide utilization while preventing undesired uses and consequences?
3
Which concrete differences occur in the application of the device in the two chosen national contexts (Uganda & Germany), and how can they be accounted for in the integration framework and design?

Methods

Technological prototype testing and refinement in laboratory environments using the novel forecasting tool EvolChip • Comparative analysis betweed Uganda & Germany
Systematic review of AMS protocols, workflows and best practices
Semi-structured qualitative interviews with key stakeholders
Participatory workshops wihtin living lab

Expected Outcomes

Framework for the integration of forecasting technologies into AMS programs in diverse healthcare system contexts.
Tailored AMS training and educational resources.
Guidelines for scaling and transferring the technology to other healthcare systems.
Refined EvolChip prototype shaped by real-world user-centered testing.

Work Packages

Technology Integration Framework For Forecasting AMR In AMS

Ethical And Context-sensitive Design Of Evolchip

Comparative Technology Evaluation to Ensure Applicability in Uganda and Germany