Objective: To introduce, describe, and demonstrate the emergence and testing of an evaluation method that combines different logics for co-designing, measuring, and optimizing innovations and solutions within complex adaptive health systems.

Method: We describe the development and preliminary testing of a framework to evaluate new ways of using and implementing knowledge (innovations) and technological solutions to solve problems via co-design methods and measurable approaches such as data science. The framework is called PROLIFERATE; it is initially located within the ecological logic: complexity science, by investigating the evolving and emergent properties of systems, but also embraces the mechanistic logic of implementation science (IS) (i.e., getting evidence-based interventions into practice); and the social logic, as the study of individuals, groups, and organizations. Integral to this logic mixture is measuring person-centered parameters (i.e., comprehension, emotional responses, barriers, motivations, and optimization strategies) concerning any evaluated matter across the micro, meso, and macro levels of systems. We embrace the principles of Nilsen's taxonomy to demonstrate its adaptability by comparing and encompassing the normalization process theory, the 2 × 2 conceptual map of influence on behaviors, and PROLIFERATE.

Results: Snapshots of ongoing research in different healthcare settings within Australia are offered to demonstrate how PROLIFERATE can be used for co-designing innovations, tracking their optimization process, and evaluating their impacts. The exemplification involves the evaluation of Health2Go (the design and implementation of an innovative procedure: interdisciplinary learning within an allied health service—community-based) and RAPIDx_AI (an artificial intelligence randomized clinical trial being tested to improve the cardiac care of patients within emergency departments—tertiary care).

Conclusion: PROLIFERATE is one of the first frameworks to combine ecological, mechanistic, and social logic models to co-design, track, and evaluate complex interventions while operationalizing an innovative complexity science approach: the knowledge translation complexity network model (KT-cnm). It adds a novel perspective to the importance of stakeholders’ agency in the system by considering their sociodemographic characteristics and experiences within different healthcare settings (e.g., procedural innovations such as “interdisciplinary learning” for Health2Go, and tech-enabled solutions such as RAPIDx_AI). Its structured facilitation processes engage stakeholders in dynamic and productive ways while measuring and optimizing innovation within the complexities of health systems.