A real-time computer model to assess resident work-hours scenarios.
OBJECTIVE
To accurately model residents' work hours and assess options to forthrightly meet Residency Review Committee-Internal Medicine (RRC-IM) requirements.


METHODS
The requirements limiting residents' work hours are clearly defined by the Accreditation Council for Graduate Medical Education (ACGME) and the RRC-IM: "When averaged over any four-week rotation or assignment, residents must not spend more than 80 hours per week in patient care duties."(1) The call for the profession to realistically address work-hours violations is of paramount importance.(2) Unfortunately, work hours are hard to calculate. We developed an electronic model of residents' work-hours scenarios using Microsoft Excel 97. This model allows the input of multiple parameters (i.e., call frequency, call position, days off, short-call, weeks per rotation, outpatient weeks, clinic day of the week, additional time due to clinic) and start and stop times for post-call, non-call, short-call, and weekend days. For each resident on a rotation, the model graphically demonstrates call schedules, plots clinic days, and portrays all possible and preferred days off. We tested the model for accuracy in several scenarios. For example, the model predicted average work hours of 85.1 hours per week for fourth-night-call rotations. This was compared with logs of actual work hours of 84.6 hours per week. Model accuracy for this scenario was 99.4% (95% CI 96.2%-100%). The model prospectively predicted work hours of 89.9 hours/week in the cardiac intensive care unit (CCU). Subsequent surveys found mean CCU work hours of 88, 1 hours per week. Model accuracy for this scenario was 98% (95% CI 93.2-100%). Thus validated, we then used the model to test proposed scenarios for complying with RRC-IM limits. The flexibility of the model allowed demonstration of the full range of work-hours scenarios in every rotation of our 36-month program. Demonstrations of status-quo work-hours scenarios were presented to faculty as well as real-time demonstrations of the feasibility, or unfeasibility, of their proposed solutions. The model clearly demonstrated that non-call (i.e., short-call) admissions without concomitant decreases in overnight call frequency resulted in substantial increases in total work hours. Attempts to "get the resident out" an hour or two earlier each day had negligible effects on total hours and were unrealistic paper solutions. For fourth-night-call rotations, the addition of a "golden weekend" (i.e., a fifth day off per month) was found to significantly reduce work hours. The electronic model allowed the development of creative schedules for previously third-night-call rotations that limit resident work hours without decreasing continuity of care by scheduling overnight call every sixth night alternating with sixth-night-short-call rotations.


CONCLUSIONS
Our electronic model is sufficiently robust to accurately estimate work hours on multiple and varied rotations. This model clearly demonstrates that it is very difficult to meet the RRC-IM work-hours limitations under standard fourth-night-call schedules with only four days off per month. We are successfully using our model to test proposed alternative scenarios, to overcome faculty misconceptions about resident work-hours "solutions," and to make changes to our call schedules that both are realistic for residents to accomplish and truly diminish total resident work hours toward the requirements of the RRC-IM.
Inpatient Internal Medicine, Mayo Clinic and Graduate School of Medicine, Rochester, MN 55902, USA. mcdonald.furman@mayo.edu
Furman S
FS
McDonald
Gautam
G
Ramakrishna
Henry J
HJ
Schultz
D003198:Computer Simulation / Q000706:statistics & numerical data; D003199:Computer Systems / Q000706:statistics & numerical data; D003266:Continuity of Patient Care; D004503:Education, Medical, Graduate; D006801:Humans; D007388:Internal Medicine; D007396:Internship and Residency / Q000706:statistics & numerical data; D010561:Personnel Staffing and Scheduling / Q000706:statistics & numerical data