2011 HSR&D National Meeting Abstract
1028 — How Much Intern Time Does a Newly Admitted Patient Take?
Fletcher KE (Milwaukee VAMC), Slagle J
(Vanderbilt University), Visotcky AM
(Medical College of Wisconsin), Schmidt J
(Milwaukee VAMC), Schapira MM
We conducted the following pilot to establish a method for collecting data on physician workload as it relates to individual patients. Our objectives were to:
1) describe the amount of time interns spend on various tasks during a period of getting new admissions; and
2) quantify the amount of time interns spent on individual patient admissions
We recruited internal medicine interns on the general medicine wards at the Milwaukee VAMC between May and September 2010. Research assistants (RAs) shadowed each intern while they were admitting patients from approximately 1PM to 5AM. The RAs continuously recorded the tasks that the intern performed, using task analysis software. For each intern, 1-2 new patients were also recruited. For these patients, the RAs specifically noted any work that the interns did on their behalf. We also abstracted demographic and comorbidity data from the patients’ medical records.
We present data for 16 interns and 18 patients. Mean intern age was 28.8 (SD 2.8); 56% were male. Mean patient age was 62 (SD 16); 94% were male. Mean length of the observation period was 14.7 h (SD 6.2). Overall, interns spent 12% of time on direct patient care. Computer work took the most time with a mean of 5.6 h (SD 1.4), explaining 39% of intern time. Teaching/learning accounted for 20 min (SD 21) or 2% of intern time. Interns slept 1.4 h (SD 1.4) on average, accounting for 9% of their time. Mean total time spent on each patient admission was 1.4 h (SD 0.6), accounting for 10% of the time. Interns spent a mean of only 8.5 min (SD 9.4) at the bedside of each new patient.
While admitting patients, interns spend the majority of their time on computer work. Direct patient care and education account for far less. A wide range of time is put toward the care of individual patients. This project demonstrates that it is feasible to measure patient-specific workload as experienced by interns.
The next step is to explore predictors of patient-specific workload at the time of admission so that work can be allocated among physicians to maximize patient safety.