Richard H Epstein1, Franklin Dexter2, Edward A Maratea3. 1. Department of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami, Miller School of Medicine, 1400 NW 12th Avenue, Suite 3075, Miami, FL 33136, United States of America. Electronic address: repstein@med.miami.edu. 2. Department of Anesthesia, University of Iowa, 200 Hawkins Drive, Iowa City, IA 52242, United States of America. Electronic address: Franklin-Dexter@UIowa.edu. 3. Department of Anesthesiology, Perioperative Medicine and Pain Management, University of Miami, Miller School of Medicine, 1400 NW 12th Avenue, Suite 3075, Miami, FL 33136, United States of America. Electronic address: emaratea@med.miami.edu.
Abstract
STUDY OBJECTIVE: To estimate the prevalence of unscheduled absences in a cohort of certified registered nurse anesthetists (CRNAs) over a 3-year period, for purposes of critiquing statistical review of individual providers relative to potential identification of patterns of such absences. DESIGN: Retrospective, observational study. SETTING: University hospital. SUBJECTS: 99 CRNAs performing clinical assignments in the operating rooms. INTERVENTIONS: None. MEASUREMENTS: CRNA daily clinical assignments and unscheduled absences were retrieved from the department's staff assignment software package. Data were extracted and analyzed to estimate the prevalence of unscheduled absences by CRNAs by day of the week, and whether each absence occurred on the workday before or after either a holiday or a personal vacation. A statistical power analysis was performed to determine the number of workdays of data required to identify outlier personnel above the 95th percentile among all CRNAs while controlling for a family-wise error rate of 5%. MAIN RESULTS: The overall incidence of unscheduled absences pooled by days was 1.7%, with small differences among days of the week, and before or after vacations. A year of data would be required to detect outliers for unscheduled absences exceeding the 95% upper confidence limit among all CRNAs. Attempting to identify patterns of absences being on specific days of the week or as related to holidays and vacations would require multiple years of data. CONCLUSIONS: OR managers can detect CRNAs with excessive numbers of unscheduled absences, but at least a year of data is required. Detecting apparent "patterns" of absences would require multiple years of data and is thus impractical.
STUDY OBJECTIVE: To estimate the prevalence of unscheduled absences in a cohort of certified registered nurse anesthetists (CRNAs) over a 3-year period, for purposes of critiquing statistical review of individual providers relative to potential identification of patterns of such absences. DESIGN: Retrospective, observational study. SETTING: University hospital. SUBJECTS: 99 CRNAs performing clinical assignments in the operating rooms. INTERVENTIONS: None. MEASUREMENTS: CRNA daily clinical assignments and unscheduled absences were retrieved from the department's staff assignment software package. Data were extracted and analyzed to estimate the prevalence of unscheduled absences by CRNAs by day of the week, and whether each absence occurred on the workday before or after either a holiday or a personal vacation. A statistical power analysis was performed to determine the number of workdays of data required to identify outlier personnel above the 95th percentile among all CRNAs while controlling for a family-wise error rate of 5%. MAIN RESULTS: The overall incidence of unscheduled absences pooled by days was 1.7%, with small differences among days of the week, and before or after vacations. A year of data would be required to detect outliers for unscheduled absences exceeding the 95% upper confidence limit among all CRNAs. Attempting to identify patterns of absences being on specific days of the week or as related to holidays and vacations would require multiple years of data. CONCLUSIONS: OR managers can detect CRNAs with excessive numbers of unscheduled absences, but at least a year of data is required. Detecting apparent "patterns" of absences would require multiple years of data and is thus impractical.