Optimal number of beds and occupancy to minimize staffing costs in an obstetrical unit?
OBJECTIVE
We describe how the science of analyzing patient arrival and discharge data can be used to determine the optimal number of staffed OB beds to minimize labour costs.


METHODS
The number of staffed beds represents a balance between having as few staffed beds as possible to care properly for parturients vs having enough capacity to assure available staff for new admissions. The times of admission and discharge of patients from the OB unit can be used to calculate an average census. From this average census, and the properties of the Poisson distribution, the optimal number of staffed beds can be estimated. This calculation requires specification of the risk of having all in-house and on-call staff caring for patients, such that additional staff are unavailable should another parturient arrive. As an example, patient admission and discharge times were obtained for 777 successive patients cared for at an obstetrical unit. The numbers of patients present in the OB unit each two-hour period were calculated and analyzed statistically.


RESULTS
There was variation in the average census among hours of the day and days of the week. Poisson distributions fit the data for each of four periods throughout the week. Simply benchmarking the current average occupancy and comparing it to a desired occupancy would have been inadequate as this neglected consideration of the risk of being unable to appropriately care for an additional patient.


CONCLUSIONS
The optimal number of beds and occupancy of an OB unit to minimize staffing costs can be determined using straightforward statistical methods.
Dexter
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Department of Anesthesia, University of Iowa, Iowa City 52242, USA. franklin-dexter@uiowa.edu
Macario
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A
D000773:Anesthesia, Obstetrical / Q000191:economics*; D002170:Canada; D003365:Costs and Cost Analysis; D003696:Delivery Rooms / Q000191:economics*; D010561:Personnel Staffing and Scheduling / Q000191:economics*; D016012:Poisson Distribution; D018570:Risk Assessment; D000078329:Workforce