Toward Prevention of Adverse Events Using Anticipatory Analytics

Abstract

Supplemental Digital Content is available in the text. Introduction: Electronic Medical Records provide new opportunities for studying the historical condition and dynamics of individual patients and populations to enable new insights that may lead to improved care and treatment. Diabetes is a prime target for new analyses as it is a chronic condition that affects 1 in 10 of the U.S. adult population and causes substantial disability and loss of life. Methods: We take typical physiological measures from 3 healthcare appointments of 1,711 diabetic patients and extract combined measures that capture the overall conditions of patients and the structure of the population. Further, we examined the dynamics of individual patients across appointments in this combined measure space and examined regions associated with variability in clinical measures. Results: Our results suggest that the dynamics of standard measures may aid evaluation of the risk of adverse events, and their utility should be tested in medical trials. Conclusions: Dynamic variability of vital signs and standard measures may reflect a loss of homeostasis, associated physiological instability, and potential for adverse events that can be estimated using the proposed method.