What if health system evaluation could reflect people’s real experiences, not just abstract numbers? This question guided our exploration into creating better tools to understand and improve care in complex health systems.

Health systems worldwide are evolving rapidly, driven by demographic shifts, emerging technologies, and a growing emphasis on person-centred care. But our evaluation methods have lagged. Most still rely heavily on reductionist, utility-based indicators, narrow slices of reality that often fail to capture what truly matters to people and practitioners. In response, we set out to develop more nuanced, predictive, and meaningful approaches that reconnect data with lived experience.

Our video essay, "Health System Evaluation, Rewired: From Data Points to Human Lives," introduces a transdisciplinary shift in thinking. It proposes a reimagining of health system evaluation through multilevel, biographical, and participatory lenses, where human stories, care experiences, and dynamic system behaviours matter just as much as statistical outputs.

This work draws on several years of collaborative research across implementation science, social psychology, health informatics, and public health. We introduce innovations like PROLIFERATE and its advanced form, PROLIFERATE_AI, frameworks that enable co-design and predictive evaluation of innovations within complex adaptive systems. These tools help stakeholders ask: Does this work? For whom? Why? And how can it be improved in context?

We also embed these methods into broader theoretical models, such as the Caring Life Course Theory (CLCT) and Fundamental Care Modelling, which emphasise the importance of care relationships, sociotechnical contexts, and system-level interdependencies. By integrating biographical narratives and system intelligence into our models, we aim to provide a more holistic picture of care quality, adoption, and sustainability, especially in underserved or vulnerable populations.

The significance of this work extends beyond the realm of health informatics. It is about creating ethical, inclusive, and actionable knowledge that supports decision-making under uncertainty. In an age of AI and digital transformation, we cannot afford to treat health system data as neutral or sufficient on its own. Instead, we must ask: Whose data counts? Whose voices are heard? And how can we design systems that respond meaningfully to both?

This work is especially relevant to researchers, practitioners, policymakers, and community members working across domains such as:

• Public and digital health: where implementation success often depends on trust, usability, and adaptation to local needs.

• Computational and data sciences: seeking ways to embed ethical and participatory dimensions into predictive modelling and evaluation.

• Nursing and allied health: where care relationships and social determinants are key to improving outcomes.

• Health policy: aiming to move from performance metrics to system learning and human-centred accountability.

These approaches build on insights from transdisciplinary collaboration, knowledge translation, and systems thinking for wicked problems.

I hope this opinion and its accompanying video encourage conversation about what health systems are for, not just how they function. We also invite fellow community members to share their reflections, case studies, or tools for making evaluation more meaningful and equitable.

Let’s continue rewiring evaluation, from data points to human lives.

Read more: From Utility to Meaning