A feasibility study of the Illuminate 360o approach for monitoring the implementation of a health system innovation
Bibliographic record
Abstract
Context: 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). Objectives: 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. Setting: Alberta, Canada from May 2022 to October 2023. Study Design and Analysis: 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. Results: 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). Conclusions: 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.054 | 0.085 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".