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

PLOS Digit Health. 2024 May 23;3(5):e0000511. doi: 10.1371/journal.pdig.0000511. eCollection 2024 May.

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.

Publication types

  • Review

Grants and funding

LK is supported by a VENI fellowship from the Netherlands Organisation for Health Research and Development ZonMw (grant number 2020 – 09150161910128). HSD is supported by the National Institute of Health [grant number R00HL153795]. MDR is supported by Cincinnati Children’s Medical Center Research Foundation start-up funding. CS is the Robert L.McNeil Jr. Endowed Fellow in Translational Medicine and Therapeutics. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.