Detecting and Redirecting Critical Transitions in High-Need, High-Cost Patient Trajectories: An Instability–Plasticity Theory for Longitudinal Care

Journal article

Background: Patients described as high-need, high-cost (HNHC) represent a subset of individuals with complex multimorbidity whose healthcare trajectories are characterised by recurrent instability and intensive use of acute care services. Concepts such as trajectory disruption, resilience, and complex adaptive behaviour are widely discussed in health systems research, yet linking these ideas to longitudinal patient care remains limited. The PaJR (Patient Journey Record) relational system was designed using principles from complex adaptive systems theory, enabling longitudinal observation of patient trajectories in real-world care. Objective: This study develops a middle-range theory grounded in longitudinal relational monitoring data. Methods: Two datasets (MonashWatch and Irish cohorts) provide empirical grounding through descriptive analysis of signal clustering, distribution, and multi-domain patterns. Monitoring calls capture structured patient-reported signals across multiple domains, including illness, medication, healthcare utilisation, social support, environmental factors, and self-care. Results: Results demonstrate long-tail signal distributions, temporal clustering, and multi-domain instability preceding admission. Alerts frequently occurred in clusters across consecutive monitoring calls 88% of alert calls were part of a consecutive alert sequence, with approximately 64% of alert calls occurring immediately after a previous alert. Alerts were also commonly multi-domain, with approximately 64% involving disturbances across more than one domain simultaneously. Conclusions: Longitudinal relational monitoring reveals instability patterns in patient journeys that are not visible in episodic health-system data. Recognising these instability phases may enable earlier, more adaptive responses for patients with complex healthcare needs and provides empirical grounding for emerging theories of healthcare trajectories within complex adaptive systems. Although grounded in relational monitoring data, the instability–plasticity framework may extend to inform interpretation across physiological and connected health monitoring systems.

Authors: Martin, CM; Campbell, D; Stockman, K; Henderson, I

• Publication status: Published
• Peer review status: Peer reviewed
• Publisher: MDPI
• Journal: Systems
• Volume: 14
• Issue: 6
• Pages: 610
• Article number: 610
• Publication date: 2026-05-26
• Acceptance date: 2026-05-20
• DOI: 10.3390/systems14060610
• EISSN: 2079-8954
• ISSN: 2079-8954
• Language: English
• Keywords: patient journey; high-need high-cost patients; resilience; instability; multimorbidity; patient trajectories; complex adaptive systems; telehealth monitoring; relational monitoring