Model Used To Explore Work Schedules’ Effect on Resident Physician Performance

A new AHRQ-funded study on the relationship between physician work schedules and performance found that mathematical modeling provides an approach that may help residency programs analyze and redesign work schedules. Using a mathematical model to explore the effects of circadian rhythms and length of time awake on performance and alertness, researchers simulated two traditional schedules and three novel schedules. Among resident physicians on novel work schedules (with shifts limited to 16 hours), the model predicted less poor performance and more alertness when compared with traditional work schedules (featuring shifts of more than 24 hours). Predicted times of worse performance and alertness were at night, which is also a time when supervision of trainees is lower. The study, “Applying Mathematical Models to Predict Resident Physician Performance and Alertness On Traditional and Novel Work Schedules,” and abstract appeared in the September issue of BMC Medical Education.

2016 Sep 13;16(1):239. doi: 10.1186/s12909-016-0751-9.

Applying mathematical models to predict resident physician performance and alertness on traditionaland novel work schedules.

Klerman EB1,2, Beckett SA3, Landrigan CP3,4,5.

Author information

Abstract

BACKGROUND:

In 2011 the U.S. Accreditation Council for Graduate Medical Education began limiting first year resident physicians (interns) to shifts of ≤16 consecutive hours. Controversy persists regarding the effectiveness of this policy for reducing errors and accidents while promoting education and patient care. Using a mathematical model of the effects of circadian rhythms and length of time awake on objective performance and subjective alertness, we quantitatively compared predictions for traditional intern schedulesto those that limit work to ≤ 16 consecutive hours.

METHODS:

We simulated two traditional schedules and three novel schedules using the mathematical model. The traditional scheduleshad extended duration work shifts (≥24 h) with overnight work shifts every second shift (including every third night, Q3) or every third shift (including every fourth night, Q4) night; the novel schedules had two different cross-cover (XC) night team schedules (XC-V1 and XC-V2) and a Rapid Cycle Rotation (RCR) schedule. Predicted objective performance and subjective alertness for each work shift were computed for each individual's schedule within a team and then combined for the team as a whole. Our primary outcome was the amount of time within a work shift during which a team's model-predicted objective performance and subjective alertness were lower than that expected after 16 or 24 h of continuous wake in an otherwise rested individual.

RESULTS:

The model predicted fewer hours with poor performance and alertness, especially during night-time work hours, for all three novel schedules than for either the traditional Q3 or Q4 schedules.

CONCLUSIONS:

Three proposed schedules that eliminate extended shifts may improve performance and alertness compared with traditional Q3 or Q4 schedules. Predicted times of worse performance and alertness were at night, which is also a time when supervision of trainees is lower. Mathematical modeling provides a quantitative comparison approach with potential to aid residency programs in schedule analysis and redesign.