BACKGROUND: Medical error is a major cause of preventable morbidity and mortality. Resident fatigue is likely to be a significant contributor. OBJECTIVES: We calculated and compared predicted fatigue impairment in surgical residents on varying schedules by using the validated Sleep, Activity, Fatigue, and Task Effectiveness model and Fatigue Avoidance Scheduling Tool; we identified specific times of day and rotations during which residents were most affected, instituted countermeasures, and measured the predicted response. METHODS: We compared 4 scheduling patterns: day shift, trauma shift, night shift, and prework hour restriction Q3 call (or every-third-night call). The dependent variables were mean daily effectiveness while at work and the percentage of time residents worked with critical fatigue impairment (defined as an effectiveness score of less than 70 correlated with an increased risk for error and a blood alcohol content of 0.08). Fatigue countermeasures (ie, a 30-minute nap, eliminating 24-hour shifts) were applied to rotations with significant impairment to determine impairment plasticity. RESULTS: CALCULATED MEAN EFFECTIVENESS SCORES AND PERCENTAGE OF TIME SPENT IMPAIRED AT WORK WERE AS FOLLOWS: day shift, 90.3, 0%; trauma shift, 82.0, 7.5%; prework hour restriction Q3 call shift, 80.7, 23%; and night shift, 68.0, 50% (P < .001). Fatigue optimization countermeasures for night shift rotation improved mean daily effectiveness to 87.1 with only 1.9% of time working while impaired (P < .001). CONCLUSIONS: There is a significant potential for fatigue impairment in residents, with work schedule a significant factor. Once targeted, fatigue impairment may be minimized with specific countermeasures. Fatigue optimization tools provide data for targeted scheduling interventions, which reduce fatigue and may mitigate medical error.
BACKGROUND: Medical error is a major cause of preventable morbidity and mortality. Resident fatigue is likely to be a significant contributor. OBJECTIVES: We calculated and compared predicted fatigue impairment in surgical residents on varying schedules by using the validated Sleep, Activity, Fatigue, and Task Effectiveness model and Fatigue Avoidance Scheduling Tool; we identified specific times of day and rotations during which residents were most affected, instituted countermeasures, and measured the predicted response. METHODS: We compared 4 scheduling patterns: day shift, trauma shift, night shift, and prework hour restriction Q3 call (or every-third-night call). The dependent variables were mean daily effectiveness while at work and the percentage of time residents worked with critical fatigue impairment (defined as an effectiveness score of less than 70 correlated with an increased risk for error and a blood alcohol content of 0.08). Fatigue countermeasures (ie, a 30-minute nap, eliminating 24-hour shifts) were applied to rotations with significant impairment to determine impairment plasticity. RESULTS: CALCULATED MEAN EFFECTIVENESS SCORES AND PERCENTAGE OF TIME SPENT IMPAIRED AT WORK WERE AS FOLLOWS: day shift, 90.3, 0%; trauma shift, 82.0, 7.5%; prework hour restriction Q3 call shift, 80.7, 23%; and night shift, 68.0, 50% (P < .001). Fatigue optimization countermeasures for night shift rotation improved mean daily effectiveness to 87.1 with only 1.9% of time working while impaired (P < .001). CONCLUSIONS: There is a significant potential for fatigue impairment in residents, with work schedule a significant factor. Once targeted, fatigue impairment may be minimized with specific countermeasures. Fatigue optimization tools provide data for targeted scheduling interventions, which reduce fatigue and may mitigate medical error.
Authors: Teryl K Nuckols; Jay Bhattacharya; Dianne Miller Wolman; Cheryl Ulmer; José J Escarce Journal: N Engl J Med Date: 2009-05-21 Impact factor: 91.245
Authors: Michael Littner; Clete A Kushida; W McDowell Anderson; Dennis Bailey; Richard B Berry; David G Davila; Max Hirshkowitz; Sheldon Kapen; Milton Kramer; Daniel Loube; Merrill Wise; Stephen F Johnson Journal: Sleep Date: 2003-05-01 Impact factor: 5.849
Authors: Steven R Hursh; Daniel P Redmond; Michael L Johnson; David R Thorne; Gregory Belenky; Thomas J Balkin; William F Storm; James C Miller; Douglas R Eddy Journal: Aviat Space Environ Med Date: 2004-03
Authors: Gina Luciano; Lydia Hambour; Paul Luciano; Eric Holmboe; Sudeep Aulakh; Simon Fleming; Michael Rosenblum Journal: J Gen Intern Med Date: 2020-05-29 Impact factor: 5.128