Enabling decision-making and innovation in learning health systems through simulation modelling

TL;DR

The paper explores how simulation modeling can help make better decisions and reduce risks when transforming healthcare systems using digital technologies.

Contribution

The paper introduces the novel application of simulation modeling to support decision-making and innovation in Learning Health Systems.

Findings

Simulation modeling can support informed decision-making in Learning Health Systems.

Simulation modeling can reduce risks associated with transforming complex healthcare systems.

There is limited attention given to simulation modeling in the context of Learning Health Systems despite its benefits in other industries.

Abstract

Canadian healthcare systems require profound transformations to enhance patient experience, improve population health, reduce costs, and improve the work life of healthcare providers. Learning Health Systems (LHSs) are an approach for undertaking this transformation in an effective, efficient, and sustainable manner with digital technologies as a key enabler for change. However, the successful implementation of a LHS brings with it challenging and potentially risky changes to clinical practices and operations. Simulation modelling is an advanced analytics technique particularly well-suited for informing decision-making and planning prior to and during the transformation of complex systems such as LHSs. Yet, despite the use and demonstrated benefits of simulation modelling in many different industries including healthcare, its application in the context of LHSs has received limited attention. In this article, we discuss how simulation modelling can be leveraged to support better-informed, lower-risk decisions and innovation in LHSs.

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