Multimorbidity clustering of the emergency department patient flow: Impact analysis of new unscheduled care clinics

Abstract

Background In France, the number of emergency department (ED) admissions doubled between 1996 and 2016. To cope with the resulting crowding situation, redirecting patients to new healthcare services was considered a viable solution which would spread demand more evenly across available healthcare delivery points and render care more efficient. The objective of this study was to analyze the impact of opening new on-demand care services based on variations in patient flow at a large hospital emergency department. Methods We performed a before-and-after study investigating the use of unscheduled care services in the Aube region in eastern France, that focused on ED attendance at Troyes Hospital. A hierarchical clustering based on co-occurrence of diagnoses was applied which divided the population into different multimorbidity profiles. Temporal trends of the resultant clusters were also studied empirically and using regression models. A multivariate logistic regression model was constructed to adjust the periodic effect for appropriate confounders and therefore confirm its presence. Results In total, 120,722 visits to the ED were recorded over a 24-month period (2018–2019) and 16 clusters were identified, accounting for 94.76% of all visits. There was a decrease of 56.77 visits per week in seven specific clusters and an increase of use of unscheduled health care services by 328.12 visits per week. Conclusions Using an innovative and reliable methodology to evaluate changes in patient flow through the ED, these findings may help inform public health policy experts on the implementation of unscheduled care services to ease pressure on hospital EDs.

Phenotypic Clusters and Multimorbidity in Hypermobile Ehlers-Danlos Syndrome

“…Nonhierarchical k-means cluster analysis was conducted to identify distinct patient subgroups as reported previously. 14 , 15 K-means clustering was conducted to find similar groupings among the patients with hEDS enrolled in our study. This approach is a multivariate statistical technique to identify similarities and differences among numeric data.…”

Non-Emergency Department (ED) Interventions to Reduce ED Utilization: a Scoping Review

“…The second most commonly reported intervention was health service capacity increases in non-ED settings (14%). This included the opening of additional primary care [72,74] or specialist clinics [70,71,73,75,76,82,83,97], free primary health clinics [68,79], increasing primary care hours [77,78,81] or changing the types of hospital services offered [80] or speed of access to some clinical services [69]. This was closely followed by studies that examined the effect of educational strategies (13%).…”