Computationally derived transition points across phases of clinical care

Abstract

Abstract The objective of this study is to use statistical techniques for the identification of transition points along the life course, aiming to identify fundamental changes in patient multimorbidity burden across phases of clinical care. This retrospective cohort analysis utilized 5.2 million patient encounters from 2013 to 2022, collected from a large academic institution and its affiliated hospitals. Structured information was systematically gathered for each encounter and three methodologies - clustering analysis, False Nearest Neighbor, and transitivity analysis - were employed to pinpoint transitions in patients’ clinical phase. Clustering analysis identified transition points at age 2, 17, 41, and 66, FNN at 4.27, 5.83, 5.85, 14.12, 20.62, 24.30, 25.10, 29.08, 33.12, 35.7, 38.69, 55.66, 70.03, and transitivity analysis at 7.27, 23.58, 29.04, 35.00, 61.29, 67.03, 77.11. Clustering analysis identified transition points that align with the current clinical gestalt of pediatric, adult, and geriatric phases of care. Notably, over half of the transition points identified by FNN and transitivity analysis were between ages 20 and 40, a population that is traditionally considered to be clinically homogeneous. Few transition points were identified between ages 3 and 17. Despite large social and developmental transition at those ages, the burden of multimorbidities may be consistent across the age range. Transition points derived through unsupervised machine learning approaches identify changes in the clinical phase that align with true differences in underlying multimorbidity burden. These transitions may be different from conventional pediatric and geriatric phases, which are often influenced by policy rather than clinical changes.