ArticleBehavioral sciences (Basel, Switzerland)2026
Development, Implementation, and Refinement of a Mental Health Consultation-Liaison Model in the Neonatal Intensive Care Unit: A Learning Health System Approach.
Article in Behavioral sciences (Basel, Switzerland), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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6 authors.
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Abstract
Proactive mental health consultation-liaison (C-L) services in neonatal intensive care units (NICUs) are increasingly recognized as essential to infant health and family functioning, yet there is limited guidance on how to develop sustainable and effective programs in real-world clinical settings. This paper describes the development, implementation, and iterative refinement of a NICU mental health C-L program using a learning health system (LHS) framework. Grounded in a theory-driven model that conceptualizes the infant as the identified patient, the program was designed to target proximal mechanisms influencing infant regulation, parent-infant interaction, and medical course. Consistent with LHS principles, program elements were introduced incrementally and refined through embedded data collection, rapid feedback loops, and multidisciplinary team learning. Data sources emphasized feasibility, reach, implementation processes, and acceptability rather than hypothesis-driven outcome testing. Over six years, this approach supported the expansion of multiple integrated service components, including dyadic intervention services, parent mental health screening and referral pathways, unit-wide family engagement initiatives, and neurobehavioral care practices. Rather than presenting a fixed protocol, this paper offers a replicable framework for developing NICU mental health C-L services that balances developmental theory, implementation realities, and sustainability. The LHS approach provides a pragmatic roadmap for NICUs seeking to build or refine mental health services in contexts characterized by clinical complexity, evolving evidence, and system-level constraints.
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