Using routinely collected clinical data for circadian medicine: A review of opportunities and challenges

Abstract

A wealth of data is available from electronic health records (EHR) that are collected as part of routine clinical care in hospitals worldwide. These rich, longitudinal data offer an attractive object of study for the field of circadian medicine, which aims to translate knowledge of circadian rhythms to improve patient health. This narrative review aims to discuss opportunities for EHR in studies of circadian medicine, highlight the methodological challenges, and provide recommendations for using these data to advance the field. In the existing literature, we find that data collected in real-world clinical settings have the potential to shed light on key questions in circadian medicine, including how 24-hour rhythms in clinical features are associated with-or even predictive of-health outcomes, whether the effect of medication or other clinical activities depend on time of day, and how circadian rhythms in physiology may influence clinical reference ranges or sampling protocols. However, optimal use of EHR to advance circadian medicine requires careful consideration of the limitations and sources of bias that are inherent to these data sources. In particular, time of day influences almost every interaction between a patient and the healthcare system, creating operational 24-hour patterns in the data that have little or nothing to do with biology. Addressing these challenges could help to expand the evidence base for the use of EHR in the field of circadian medicine.

Fig 1. Overview of opportunities for the use of EHR for circadian medicine.

Fig 2. Considering causes in EHR studies of biological rhythms.

(A) An individual’s occupation may independently “cause” both the time of vaccination (scheduling/availability) and the risk of COVID-19 infection (exposure). A way to think about cause in these diagrams is that the variable being pointed to “listens to” the variable pointing to it. (B) Twenty-four-hour rhythms in hospital operation based on data from Ruben and colleagues [65] and Caraballo and colleagues [68]. (C) Proposed causal model for the effect of time of day of lab test (or drug administration) on outcome. This model assumes that health status is a confounder because it independently influences both the time of lab test (or drug administration) and the outcome of interest. (D) Given the causal model for lab results in panel C, we propose that the probability of an abnormal result at a particular time is inversely related to the probability of testing at that time. Theoretical curves (gray color) reflect different scaling, i.e., proposed relationships between the probability of testing and probability of obtaining an abnormal result. Ground truth is unknown.