Predicting future staffing needs at teaching hospitals: use of an analytical program with multiple variables.
OBJECTIVE
To develop a model to predict future staffing for the surgery service at a teaching hospital.


METHODS
Tertiary hospital.


METHODS
A computer model with potential future variables was constructed. Some of the variables were distribution of resident staff, fellows, and physician extenders; salary/wages; work hours; educational value of rotations; work units, inpatient wards, and clinics; future volume growth; and efficiency savings. Outcomes Number of staff to be hired, staffing expense, and educational impact.


RESULTS
On a busy general surgery service, we estimated the impact of changes in resident work hours, service growth, and workflow efficiency in the next 5 years. Projecting a reduction in resident duty hours to 60 hours per week will require the hiring of 10 physician assistants at a cost of $1 134 000, a cost that is increased by $441 000 when hiring hospitalists instead. Implementing a day of didactic and simulator time (10 hours) will further increase the costs by $568 000. A 10% improvement in the efficiency of floor care, as might be gained by advanced information technology capability or by regionalization of patients, can mitigate these expenses by as much as 21%. On the other hand, a modest annual growth of 2% will increase the costs by $715 000 to $2 417 000.


CONCLUSIONS
To simply replace residents with alternative providers requires large amounts of human and fiscal capital. The potential for simple efficiencies to mitigate some of this expense suggests that traditional patterns of care in teaching hospitals will have to change in response to educational mandates.
Mitchell
Christine C
CC
Department of Surgery, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA.
Ashley
Stanley W
SW
Zinner
Michael J
MJ
Moore
Francis D
FD
D019985:Benchmarking; D003198:Computer Simulation; D013502:General Surgery; D006301:Health Services Needs and Demand / Q000639:trends*; D006784:Hospitals, Teaching; D006801:Humans; D008505:Medical Staff, Hospital / Q000600:supply & distribution* / Q000639:trends; D014481:United States; D000078329:Workforce; D016526:Workload