Research Programs

Health systems are structurally organized around the moment of encounter. Care occurs in discrete episodes - scheduled appointments, emergency presentations, inpatient admissions - with negligible visibility into what transpires between them. Deterioration accumulates undetected until it reaches a threshold that demands acute intervention. The system, in effect, operates blind between contacts, deferring action until cost and acuity are already elevated.

Netiv’s Ambient Health Systems program begins from a different premise: that the structural conditions of episodic care are not inevitable but engineered and can therefore be re-engineered. The program applies Netiv’s Landscape Engineering methodology to identify and reshape the system dynamics that generate late intervention, fragmented coordination, and chronic over-reliance on acute infrastructure. Rather than optimizing individual care pathways, we are mapping the attractor states that keep health systems reactive, and engineering the conditions under which earlier, distributed, and anticipatory care can stably emerge.

The program spans home, long-term care, and community environments, and is built on three technical pillars: continuous sensing technologies that extend system perception beyond clinical walls; AI-enabled signal detection that translates ambient data into actionable intelligence; and system simulation through Netiv’s Mycelia and Noesis, which models how local perturbations propagate across care networks. Together, these pillars support the prototyping of care environments where monitoring, response, and coordination are continuous properties of the system rather than episodic acts within it.

The program is structured as a multi-sector consortium convened for structured inquiry, iterative learning, and real-world implementation.

Ambient Health Systems

Sensing, Adaptation, and Continuous Care at the Edges of the System

    • Health system operators and integrated care networks

    • AI, data, and sensing technology partners

    • Clinical leads across primary, community, and long-term care

    • Community organizations and patient partners

    • Health policy and regulatory stakeholders

    • Industry innovators in ambient and point-of-care technologies

Leadership and Governance in Complex Adaptive Health Systems

Reconceiving Leadership for Systems That Are Dynamic, Distributed,
and Augmented

The scientific consensus that health systems are complex adaptive systems has not yet translated into a corresponding transformation in how those systems are led. Governance models remain largely anchored in command-and-control assumptions: centralized authority, predictable cause-and-effect, and leadership confined to formal hierarchical roles. The result is a structural mismatch - systems that evolve continuously, governed by frameworks designed for environments that do not.

This program is a structured research initiative to reconceive leadership as a system-level property rather than an individual attribute. In complex adaptive systems, leadership does not reside exclusively in formal authority; it emerges wherever individuals or collectives have the capacity and orientation to influence the system toward shared value. The critical question is what conditions enable that emergence and what organizational, cultural, and structural features suppress it.

The program investigates the combinations of skills and capacities required across individuals, teams, and organizational levels to sustain attunement to system signals: from frontline operators with proximate knowledge of patients and communities, to integrative leaders who detect patterns across the system, to executive actors positioned to challenge prevailing assumptions when they have ceased to fit reality. A particular focus is placed on the boundaries between these levels - where signal loss, amplification, and distortion most commonly occur.

A second dimension of the program addresses the governance implications of AI-enabled sensing architectures as they are embedded in health system operations. As systems acquire the capacity for continuous self-monitoring, leaders will increasingly contend with intelligence that may challenge, complicate, or contradict their experiential judgment. The program explores how leadership adapts, or fails to adapt, to operating alongside system-level intelligence, and what new governance structures are required as a result.

The program is deeply integrated with Netiv’s Ambient Health Systems program and is structured as a multi-sector research consortium.

    • Health system executives and senior leadership teams

    • Academic partners in organizational theory, complexity science, and health systems

    • Leadership development and executive education organizations

    • AI and technology collaborators

    • Patient and community partners

Intelligent ÉTMIS for Complex Adaptive Health Systems

Reimagining Health Technology Assessment as a Continuous, Adaptive, and Intelligent Function

In Québec, ÉTMIS - the evaluation of health technologies and modes of intervention - is a cornerstone function of the health system, responsible for ensuring that decisions about technologies, treatments, and care models are grounded in rigorous evidence. The design logic of ÉTMIS, however, was developed for a different operating environment: one characterized by relatively discrete innovations, stable implementation contexts, and the expectation that evidence generated in controlled conditions would adequately predict real-world performance. These assumptions no longer hold.

Contemporary evaluations face a structural problem. Assessments are typically static and retrospective: designed to yield a recommendation at a point in time, in an environment that continues to evolve around them. They are poorly equipped to account for nonlinear dynamics, context variability, and the cascading interdependencies that define complex adaptive health systems. And they are not calibrated for the new generation of health technologies - continuous, embedded, and adaptive - whose value is not a fixed quantity to be measured but a dynamic property that emerges through interaction with the system that hosts them.

The diagnostic shift now underway illustrates the stakes clearly. The transition from episodic, laboratory-based testing to continuous, point-of-care sensing embedded in patient environments produces technologies whose performance cannot be assessed once and filed. Their accuracy, safety, utility, and equity implications evolve as care contexts change, as populations shift, and as the systems around them adapt. A single-cycle evaluation captures a snapshot of a moving target.

This program asks how health technology assessment can be reconstituted as a continuous learning function: one that monitors evidence as it accumulates in real-world conditions, detects how impacts evolve over time, and informs decisions iteratively rather than at a single evaluation point. Drawing on complexity science, AI-enabled evidence synthesis, and Netiv’s value framework, the program is developing evaluation architectures that can be embedded within care delivery itself, transforming ÉTMIS from a periodic reporting mechanism into an ongoing epistemic infrastructure for the health system.

    • Health technology assessment (HTA) bodies and ÉTMIS units

    • Health system decision-makers and program directors

    • AI, data, and evidence synthesis platform partners

    • Industry innovators in diagnostics, therapeutics, and care technologies

    • Academic researchers in health economics, complexity science,
      and evaluation methodology