Paging Data Can Help Track Resident Workload Intensity

The hours residents work have been a matter of concern for decades. Several studies have suggested that resident overwork and fatigue leads to adverse outcomes for patients and trainees alike. The primary way to monitor and manage resident workload is to have residents log how long they are at work, known as “duty hours.” Residents are required to work fewer than 80 hours a week on average. But average hours worked presents an incomplete picture; what also matters is what happens during those hours, which can separately affect perceptions of workload and well-being.

Amit Kaushal, MD, PhD, clinical instructor of medicine, Palo Alto VA and adjunct professor of bioengineering, and co-authors Laurence Katznelson, MD, professor of neurosurgery and of endocrinology, and Robert Harrington, MD, professor of medicine and chairman of the Department of Medicine, have just published a study in npj Digital Medicine that leverages paging data to shed light on resident workload intensity.

The authors theorized that the digital data generated during the course of patient care can give clues to not just how long a resident worked but how intensely a resident worked. Secondary use of such data can be a novel source of information about resident workload. Paging is one such data stream.

Using paging data from the 2013-14 and 2014-15 academic years – over half a million pages, the largest study of paging data to date – the authors characterized paging patterns by service and year of training in a medicine residency program.

According to Kaushal, “An intern receives, on average, 2,924 pages over the course of a year, far more than either second- or third-year medical residents. In fact, over the two years under study, interns as a group fielded more pages than everyone else in the medical residency program combined.”

Yet there is considerable variation: for example, while all trainees covering the night shift report the same 12 duty hours each night, some are paged only 10 times over the course of their shift while others are paged more than 80 times. The authors propose that from a workload monitoring perspective, it doesn’t make sense to treat these as the same just because everyone works the same number of hours. And the data bears this out – shifts with higher paging burden were associated with other hospital-wide markers of heavy workload.

Given ongoing advances in digital technology and computational techniques, the authors suggest that their study provides a framework for using digital data to better monitor resident workload and prevent adverse events for residents and patients before they happen.

Kaushal concludes by pointing out additional technological sources for future study: “Paging is one example, but the medical record, smartphones, step counters, heart rate monitors – these all have the potential to provide rich insight about the intensity of a resident’s workload. Maybe in the future, real-time analysis of these data streams can move us from resident workload monitoring to resident workload management, where we can receive an alert and intervene before workload gets too far out of hand and puts patients and trainees at risk.”