Hospitals

Healthcare systems operate in safety-critical environments where rapid adaptation and sustained functioning must occur simultaneously, yet existing safety frameworks tend to conceptualise agility and resilience as separate, sequential, or retrospective capabilities. This conceptual separation limits understanding of how safety is enacted during disruption, when healthcare workers and organisations must respond in real time without temporal or structural buffers. This paper introduces agilience as an emerging conceptual construct that captures the concurrent enactment of agility (rapid adaptation) and resilience (sustained functioning, recovery, and learning) under conditions of uncertainty. Drawing on safety science, resilience engineering, organisational theory, and comparative industry literature, this conceptual commentary clarifies how agilience extends existing Safety-I and Safety-II paradigms by addressing the temporal gap between prevention-focused and learning-focused approaches. Agilience is positioned as both an explanatory lens and an aspirational organisational state, highlighting the alignment required between individual adaptive capability and organisational structures to support safe, sustainable care delivery. The paper outlines the defining features, boundaries, and system conditions under which agilience becomes visible, and illustrates its relevance through healthcare examples. By articulating agilience as a distinct conceptual contribution, this work provides a foundation for future empirical investigation, measurement development, and application in healthcare safety management.

Health systems face pressure to strengthen resilience against supply chain disruptions while maintaining cost-effective service delivery. This mixed-methods study describes a pilot project that integrated 3D printing services into a Canadian provincial health authority. Quantitative data were derived from internal clinical engineering work orders, where a scenario-based economic analysis compared original equipment manufacturer (OEM) procurement with modelled 3D-printed parts. Using conservative assumptions, selected non-electronic structural parts were assigned a fixed unit cost. Qualitative data were collected from two focus groups with clinical engineers and other end-users. Results from an exploratory scenario-based economic analysis suggest that substituting selected structurally simple clinical engineering parts with 3D-printed alternatives would be associated with modelled cost impacts ranging from a 67.4% net increase (OEM prices halved and 3D-printing costs doubled) to a 69.6% cost reduction (OEM prices increased by 10% and 3D-printing costs decreased by 20%). Demand changes affected absolute savings but not the percent difference (58.1% under ±50% quantity changes), and a pessimistic procurement scenario (OEM prices decreased by 30% and 3D-printing costs increased by 50%) reduced savings to 10.3%. Focus groups highlighted perceived benefits and implementation challenges associated with integrating additive manufacturing. Implementation was facilitated through an outsourcing model, which was perceived to shift certain responsibilities and risk-management functions to the vendor. Long-term adoption will require clearer communication and targeted education. This pilot study suggests that, under constrained regulatory scope and scenario-based assumptions, additive manufacturing may contribute to supply chain resilience and may be associated with modelled cost advantages for selected low-risk components.

Effective communication in critical care units, such as the Cardiovascular Intensive Care Unit (CVICU), is vital for patient safety; however, clinical notes from multiple professionals are often lengthy and complex. This study evaluated the Mistral large language model for summarizing Cardiovascular Intensive Care Unit progress notes using the Illness severity, Patient summary, Action list, Situation awareness and contingency planning, and Synthesis by receiver (I-PASS) framework, a standardized mnemonic for patient handoffs in healthcare. A total of 385 patients were included in the cohort, and all the progress notes associated with each patient were combined into a single document and summarized by the model. The readability was assessed using multiple metrics, including Flesch Reading Ease, Flesch-Kincaid Grade Level, Gunning-Fog Index, Simple Measure of Gobbledygook Index (SMOG), Automated Readability Index, and Dale-Chall Score. The readability metrics showed that the summaries generated with the Mistral Large Language Model (LLM) were much more difficult to read than the original notes, requiring a higher reading level. In a small clinician review, junior residents rated the summaries overall more favorably than senior residents, who often identified missing clinical details. Although Mistral condensed the documentation, this reduced readability and some loss of context may limit its usefulness for clinical handoffs. As a preliminary study with a small clinician-reviewed sample, these findings are descriptive and will require validation in larger clinical settings.