Health system innovations aimed at improving healthcare access are complex with potential for unintended consequences. There is a need for real-time evaluation which captures evolving contexts and impacts. This study presents experiences—both successes and failures—at extending the sensemaking methodology from our previous work into a large scale health ecosystem for monitoring, evaluation and learning in such contexts. Between May 2022 and October 2023, we explored the feasibility of using a sensemaking methodological approach to provide near real-time feedback on a large-scale health system innovation. We designed two complexity-informed data collection instruments using the Cognitive Edge SenseMaker® tool, and attempted to: 1) operationalize them across diverse patient populations and providers; and 2) engage with stakeholders in collaborative sensemaking of the insights from the data. Between May 2022 and October 2023, test data yielded 221 patient and 104 provider entries regarding surgical referral experiences. Attempts to scale the data collection instruments within formal healthcare channels to reach a diverse representation of patients and providers were unsuccessful. We pivoted to capture information on the impacts of our enacted data collection strategies. From these efforts, we generated a diagrammatic representation of the sensemaking methodological approach, namely the Illuminate 360°. This approach consists of an iterative cycle: data collection, analysis, monitoring/reflection, decision-making, and action planning. Central to this is the integration of regular sensemaking sessions with relevant parties, which facilitates clarity on the phenomenon of interest and insights for action. While scaling up the methodology through formal channels proved unfeasible within the project’s parameters, our findings offer critical insights into building situational awareness for complex healthcare interventions. We demonstrate that the Illuminate 360° approach, when grounded in implementation science frameworks and theories, can generate the timely, co-created data necessary to inform healthcare improvement and support data-driven decision-making.
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Ofosu et al. (2026) studied this question.
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