A feasibility study of the Illuminate 360o approach for monitoring the implementation of a health system innovation

<h3>Context:</h3> Health system innovations aimed at improving healthcare access are complex interventions that can have unintended consequences; hence the need for near real-time evaluation approaches which capture evolving contexts and impacts. This need became apparent with the initiation of the Alberta Surgical Initiative’s Facilitated Access to Specialized Treatment (FAST). <h3>Objectives:</h3> We explored how to design a system to provide near real-time feedback on context and impacts to inform health system innovations; and how to operationalize data collection across diverse patient populations and providers. <h3>Setting:</h3> Alberta, Canada from May 2022 to October 2023. <h3>Study Design and Analysis:</h3> We used a mixed-method complexity-informed methodology. We codesigned two online data collection instruments with patients and providers to run concurrently using the Cognitive Edge SenseMaker® tool. We utilized theoretical constructs of salutogenesis – manageability, meaningfulness, and coherence; and resilience. We employed diverse strategies to solicit respondent participation in clinics, provider networks, community and, social media. Participants provided short reflections about their surgical journey or referral experiences, along with elaborations in quantitative categories. We used web analytics to observe the effects of different recruitment strategies. We examined types of patient and provider experiences, impacts and context with the FAST innovation by filtering the linked qualitative and quantitative responses. <h3>Results:</h3> We generated a diagrammatic representation of the Illuminate 360o approach for monitoring and adapting complex health system innovations. Examples of the types of information that can be collected by this approach included micro-narratives about patient and provider experiences which are linked with quantitative elaborations through multiple choice questions (categorical data), dyads (continuous data), and ternary plots (compositional data). <h3>Conclusions:</h3> This study is the first of its kind in applying complexity methodology to the pressing challenges of generating near real-time information to support the roll-out and ongoing optimization of a complex intervention in healthcare. There is inherent value in strategies to collect ongoing information to understand evolving contexts and impacts of complex interventions in order to adapt them to desired end-state outcomes and generate timely insights to inform healthcare improvement.