The biomedical model, dominant in modern healthcare systems, is increasingly recognized as insufficient for addressing the full complexity of human health. Rooted in Enlightenment-era science and Cartesian dualism, it privileges linear causality, biological mechanisms, and predictive logic (Engel, 1977, 1992). This reductionist approach has delivered remarkable biomedical advances but continues to struggle with chronic illness, mental health, and the subjective dimensions of healing. As George Engel (1977) argued in his seminal critique, medicine must expand beyond its 17th-century worldview to include psychological, social, and existential dimensions.

Critiques of this uniplex model have emerged across decades and disciplines. Capra (1984) and Capra and Luisi (2014) emphasized the failure of mechanistic thinking to account for the dynamic, interdependent nature of life systems. Sturmberg and Martin (2013) assembled a comprehensive critique of the limitations of Evidence-Based Medicine (EBM), showing how it often fails to meet the needs of real-world clinical practice in complex adaptive systems. Sturmberg (2009) further critiques EBM as an overly narrow methodology that ignores context and subjective experience and becomes fixated on standardized interventions while neglecting the uniqueness of each patient’s journey.

The concept of health itself demands rethinking. As Sturmberg (2012) notes, the word “health” is derived from the Old English hal, meaning “whole.” Health, in this view, is not merely the absence of disease but the experience of wholeness. The WHO (1946) foundational definition of health as “a state of complete physical, mental and social well-being” also gestures toward this expansive view, though its operationalization often remains biomedical.

This narrowing of how we define and know health has far-reaching consequences for emerging technologies like machine learning (ML) and predictive artificial intelligence (AI). These tools often operate under the assumption that health can be fully understood through patterns in past data, treating it as a closed system—one that is fully observable, predictable, and governed by stable regularities. Proponents of “scientific wellness” argue that data-driven, personalized approaches can revolutionize prediction and prevention (Hood et al., 2004; Hood and Price, 2023). Yet despite their promise, these models often rely on the same reductionist assumptions. As we elaborate later in the paper, such Closed ML approaches (Birhane and Sumpter, 2022) tend to abstract away contextual nuance, relational dynamics, and lived experiences—and can be especially limited or even harmful when applied to individuals at the social margins, where complexity and structural inequities are most pronounced.

Taleb (2012) offers a sharp critique of the healthcare system’s emphasis on optimization and control, arguing that such approaches cultivate fragility in complex systems. In response, he introduces the concept of antifragility—the ability of a system to thrive and improve under conditions of variability, stress, and uncertainty—as a more suitable objective than mere predictability or stability. Taleb also revives the term iatrogenics (from Greek iatros, physician, and genesis, origin), highlighting harm from medical intervention. This aligns with Illich’s (1976) warning against overmedicalization and the erosion of self-care. Similarly, Vogt et al. (2016) caution that precision medicine may pathologize normal variation, leading to unnecessary treatments. In contrast, the MH framework promotes resilience by minimizing naïve intervention, emphasizing lifestyle and preventive care, and empowering individuals to take ownership of their well-being.

As Birhane (2021) argues, such models struggle with context, fail to account for interpersonal dynamics, and risk reinforcing inequalities. Birhane and Sumpter (2022) advocate for a shift toward “critical complexity,” which recognizes that real-world health systems are open, adaptive, and not fully predictable. Narayanan and Kapoor similarly warn that predictive AI can overfit historical patterns, falter when conditions change, and mislead decision-making by ignoring its own systemic impacts.

Moreover, health cannot be separated from meaning. Senturk et al. (2020), Birhane (2021), and Juarrero (1999) emphasize that in complex, open systems, understanding must go beyond causal explanation to include interpretive frameworks. Health is a deeply personal and subjective phenomenon shaped not only by biological mechanisms but also by symbols, relationships, and cultural narratives—layers that predictive logic alone cannot fully capture (Sturmberg, 2012).

In response to these limitations, we propose the Multiplex Health (MH) framework. MH is grounded in critical complexity theory and embraces a plural, integrative, and layered understanding of health. It synthesizes both explanatory and interpretive modes of knowing, incorporating diverse epistemologies, including spiritual and ethical traditions. MH affirms that health is emergent, relational, and value-laden, and that decision-making under uncertainty must integrate meaning-making, context, and ethics.

The MH framework is based on a philosophical worldview based on the Multiplexity framework proposed by Senturk et al. (2020) to address the complex realities of human health. In this section, we discuss multiplex ontology, epistemology, and methodology.

To illustrate the applied value of the MH framework, we examine its integration into Population Health Management (PHM)—a multidisciplinary field that unites clinical care, public health, informatics, and behavioral science. While PHM already emphasizes longitudinal health trajectories, upstream prevention, and system-wide coordination, MH offers a complementary ethical-epistemological lens that foregrounds pluralism, normative inclusivity, and responsiveness to cultural and spiritual dimensions—especially vital for ethically diverse, global contexts.

As Valles (2020) argues, Population Health science emerged to counter the limitations of biomedical individualism by emphasizing the social embeddedness of health and the need for intersectoral collaboration. PHM operationalizes this through AI-enabled risk stratification, EHR-integrated care pathways, and lifestyle-oriented interventions (Nash et al., 2021; Steenkamer et al., 2016). Yet while PHM provides the infrastructure for systemic, data-driven interventions, MH deepens its conceptual and moral scope—supporting the inclusion of multiple worldviews, ethical traditions, and non-biomedical indicators of well-being.

Building on this, Henry (2025a, 2025b) proposes a comprehensive model for embedding the Human Phenotype Ontology within Population Health Management, proposing federated infrastructures such as Higher Expert Medical Science Safety (HEMSS), predictive pre-eXams, and AI-powered biological modelling as cornerstones for ethically robust, equitable care delivery. While Henry’s approach ensures structural readiness and policy alignment, the MH framework adds layers of interpretive nuance: integrating community-derived narratives, cultural-spiritual insights, and pluralistic moral frameworks (e.g., BioFiqh, virtue ethics) making PHM not just technologically capable, but deeply humane and contextually responsive.

In the adolescent mental health use case, this means that predictive alerts generated through HPO–Biological Modelling can be enriched with relational assessments of spiritual distress, family norms, or communal meaning-making—ensuring that risk stratification is not only precise but also person-centered. This synthesis maintains infrastructural integrity while elevating ethical resilience and global cultural responsiveness.

The MH framework offers a principled enhancement to PHM by introducing six interlocking contributions. It expands health beyond the physical to include psychological, social, and spiritual dimensions (multiplex ontology), and integrates ethical, narrative, and experiential knowledge alongside clinical data (multiplex epistemology). It incorporates diverse modeling approaches—mechanistic, statistical, symbolic, and normative—to reflect the full complexity of health systems (pluralistic modeling). MH embraces contextual variability and emergent phenomena (critical complexity), balances big data with clinical insight and moral reasoning (triangulated science), and brings culturally grounded ethical frameworks such as BioFiqh and virtue ethics to bear on issues like adolescent autonomy and care (comparative multiplex ethics). While a full operational model lies beyond this paper’s scope, these principles sketch how MH can help PHM evolve into a more ethically resilient, culturally sensitive, and holistically grounded paradigm.

As a concrete example, consider an adolescent mental health initiative embedded within a PHM framework that uses HPO, predictive AI, and federated learning to identify early markers of depression and anxiety. While such technologies offer scalable risk stratification, they often overlook interpretive depth and cultural attunement. For instance, an adolescent flagged as high risk may be experiencing spiritual distress or family-based moral tension that is invisible to algorithmic models. Without broader frameworks, interventions may become overly biomedical and insufficiently person-centered.

This is where MH becomes indispensable, PHM already advances a life course orientation by tracking health trajectories across time and contexts; MH can further enhance this by embedding those trajectories within dynamic social, ethical, and spiritual worlds. Where PHM and Life Course Theory (LCT) emphasize developmental timing and social determinants, MH brings in the pluralistic meanings that individuals and communities assign to those life transitions. A genomic risk flag for adolescent depression, for instance, may indicate more than biochemical vulnerability—it may reflect spiritual despondency, strained familial ethics, or cultural tensions.

MH enables these nuances to surface by integrating narrative insight, value-laden judgment, and community-based reasoning alongside data analytics. In doing so, it supports a more contextual and relational understanding of health that aligns closely with LCT’s emphasis on time, place, and cumulative experience. Rather than imposing uniform interventions, MH fosters adaptive, culturally resonant responses that respect autonomy while honoring communal norms. This synthesis of predictive modeling with moral meaning-making allows PHM to evolve into a more humane, just, and globally relevant practice.

The MH framework offers more than a philosophical realignment—it proposes a rigorous, pluralistic, and actionable scientific paradigm grounded in critical complexity, multi-layered normativity, and open science. In contrast to dominant health frameworks built on predictive modeling, linear causality, and narrow epistemologies, MH fosters a shift toward understanding, context, and meaningful relational engagement. MH presents an example of a pluralistic open science that does not reject of scientific rigor, but reimagines what rigorous science would mean when we assume multiplex ontology and epistemology, which is indispensable for the study of living systems like human health.

As Juarrero (1999) and Morin (2008) argue, living systems are not reducible to mechanistic parts but are dynamic, non-linear, and emergent. Health systems, in particular, are shaped by feedback loops, cultural beliefs, and individual experiences. The overreliance on closed, predictive logic can result in active harm, especially to the people who are already disadvantaged in the society as Birhane (2021) demonstrates. Closed ML systems that operate on static datasets and fixed models are ill-equipped to handle the context-sensitive and evolving realities of health, especially when these tools exacerbate inequalities among already marginalized populations.

The closed worldview of predictive optimization seeks to eliminate uncertainty, but in doing so it erodes human agency, pluralism, and ethical reflection. The MH framework, by contrast, advances a both-and approach that embraces uncertainty and integrates predictive science with interpretive understanding. This paradigm resonates with E. F. Schumacher’s call for “wisdom-oriented science”—a science that does not just explain and predict, but also nurtures meaning, responsibility, and the common good. MH follows this call by integrating semiotics, spirituality, and ethics directly into the scientific method and provides a conceptual foundation for achieving this integration.

MH also grounds healing as becoming whole, returning to the etymological roots of health from the Old English hal—meaning wholeness. As Sturmberg (2012) notes, “wholeness is experiential,” and only the patient can truly judge if it has been achieved. The WHO (1946) definition of health—emphasizing complete physical, mental, and social well-being—recognizes this multidimensionality but has rarely been fully operationalized in modern systems (Sturmberg, 2012). MH takes this challenge seriously, integrating spiritual flourishing, virtue ethics, and moral reasoning into the pursuit of health outcomes.

MH’s inclusion of Islamic virtue ethics such as tazkiyah offers a normative anchor for behavioral change strategies within PHM. These concepts are not merely theological abstractions—they provide culturally resonant motivations for practices like healthy eating, sleep hygiene, and emotional self-regulation. By drawing on virtues like sabr (patience), hikmah (wisdom), and tawakkul (trust in God), MH reinforces PHM’s emphasis on preventive care and mental well-being while ensuring that behavioral interventions are rooted in indigenous moral vocabularies. In doing so, MH makes behavioral science in PHM both more effective and more ethically grounded.

The MH framework also strengthens PHM by foregrounding community (ummah) as both an ontological reality and a practical structure for care. For instance, rather than viewing mental health as an individual burden, MH encourages shared responsibility for well-being through culturally meaningful communal spaces—mosques, local health councils, neighborhood support networks. This communal logic can inform PHM interventions such as peer mentoring, communal fasting support, or collective parenting education. In contexts where individualism dominates biomedical assumptions, MH offers a corrective grounded in relational ethics and mutual accountability.

Finally, MH’s integration of BioFiqh enables PHM systems to navigate the fine balance between predictive analytics and respect for personal agency. Through its ethical scaffolding—Qaṭʿiyyāt (definitive rulings), Ẓanniyyāt (probabilistic judgments), and Mubāḥāt (permissibles)—BioFiqh guides PHM in deploying nudges and predictive tools in a way that values religious rituals and population dignity. For example, in the case of mental health, overreach can quickly become coercive. MH ensures that ethical deliberation is not an afterthought but built into the epistemic architecture of PHM interventions.

At the civilizational level, MH advances a vision of open science as a precondition for an open civilization. Senturk (2011, 2025) and Birhane (2021) argue that true openness in science requires more than data transparency—it requires openness to multiple ways of knowing, to relational thinking, and to the coexistence of different normative systems. This is especially critical in plural societies where health must be negotiated across traditions. As Qadir and Senturk (2024) note, the emergence of Multiplex AI Humanities is part of this broader shift—a movement toward systems of knowledge that are ethical, interpretive, and human-centered.

Ultimately, MH is better science precisely because it acknowledges the complexity, diversity, and meaning-laden nature of human lives and health. It integrates what is measurable with what is meaningful; it values prediction, but not at the expense of understanding. It provides a model of care and knowledge that is as rigorous as it is relational, as inclusive as it is integrative. In doing so, it opens a path not only to better health outcomes but also to a wiser, more just, and more pluralistic scientific and civilizational future.

The Multiplex Health (MH) framework introduces a normative and conceptual scaffolding that is urgently needed in the evolving landscape of healthcare. Beyond its philosophical contributions, MH offers actionable pathways for advancing Population Health Management (PHM), particularly in addressing mental health and behavioral challenges. The case study presented in this paper demonstrates how MH can inform a more context-aware and ethically robust approach to PHM—one that respects individual agency, cultural diversity, and the layered nature of human well-being. In sum, the MH framework as applied to PHM exemplifies how a complexity- and normativity-informed paradigm can elevate public health from a system of management to a system of meaning. This integrative vision holds promise for building a more holistic, open, and pluralistic healthcare system capable of addressing the moral and practical challenges of our time.