Abstract
BACKGROUND: Much of the research on high-cost patients in healthcare has taken a static approach to studying what is actually a dynamic process. High-cost patients often utilize services across multiple sectors along care pathways, but due to the cross-sectional nature of many study designs, we lack a clear understanding of the temporal relationship between high-cost spending in community and acute care. Studying care trajectories for high cost patients with cardiovascular disease (CVD) can shed light on the dynamic interplay between community-based and acute care along the care continuum, and provide information about signals in community care that may indicate future elective and urgent hospitalizations. METHODS: Using linked health administrative data in Ontario, Canada, 74,683 incident cases with cardiovascular disease were identified between the years 2009 and 2011. Patients were followed for 36 months (total study duration 2009-2014) until the first urgent or elective admission to hospital for a heart-related condition. We used an extended Cox survival model with competing risks to study the relationship between high-cost spending in community care with two mutually exclusive outcomes: urgent or elective hospitalizations. RESULTS: Elective hospitalizations were most clearly signaled by a high-cost utilization of community-based specialist services in the month prior to hospital admission (hazard ratio 9.074, p < 0.0001), while urgent hospitalizations were signaled by high cost usage across all community-based sectors of care (from general practitioner & specialist visits, home care, laboratory services and emergency department (ED) usage). Urgent hospitalizations were most clearly signaled by high cost usage in ED in the month prior to hospital admission (hazard ratio 2.563, p < 0.0001). CONCLUSION: By studying the dynamic nature of patient care trajectories, we may use community-based spending patterns as signals in the system that can point to future and elective hospitalizations for CVD. These community-based spending signals may be useful for identifying opportunities for intervention along the care trajectory, particularly for urgent CVD patients for whom future hospitalizations are difficult to anticipate.