A collaborative learning health system agent‐based model: Computational and face validity

INTRODUCTION: Improving the healthcare system is a major public health challenge. Collaborative learning health systems (CLHS) ‐ network organizations that allow all healthcare stakeholders to collaborate at scale ‐ are a promising response. However, we know little about CLHS mechanisms of actions, nor how to optimize CLHS performance. Agent‐based models (ABM) have been used to study a variety of complex systems. We translate the conceptual underpinnings of a CLHS to a computational model and demonstrate initial computational and face validity. METHODS: CLHSs are organized to allow stakeholders (patients and families, clinicians, researchers) to collaborate, at scale, in the production and distribution of information, knowledge, and know‐how for improvement. We build up a CLHS ABM from a population of patient‐ and doctor‐agents, assign them characteristics, and set them into interaction, resulting in engagement, information, and knowledge to facilitate optimal treatment selection. To assess computational and face validity, we vary a single parameter ‐ the degree to which patients influence other patients ‐ and trace its effects on patient engagement, shared knowledge, and outcomes. RESULTS: The CLHS ABM, developed in Python and using the open‐source modeling framework Mesa, is delivered as a web application. The model is simulated on a cloud server and the user interface is a web browser using Python and Plotly Dash. Holding all other parameters steady, when patient influence increases, the overall patient population activation increases, leading to an increase in shared knowledge, and higher median patient outcomes. CONCLUSIONS: We present the first theoretically‐derived computational model of CLHSs, demonstrating initial computational and face validity. These preliminary results suggest that modeling CLHSs using an ABM is feasible and potentially valid. A well‐developed and validated computational model of the health system may have profound effects on understanding mechanisms of action, potential intervention targets, and ultimately translation to improved outcomes.