Product details

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Abstract

This supplement is to accompany the case. The newly appointed director of Surgical Business Analytics has been charged with the task of improving predictions of surgical case volume at Vanderbilt University Medical Center in Nashville, Tennessee. He is provided with 48 weeks of elective surgery schedule data that give the number of surgeries booked on specific dates prior to the surgery day and the actual number performed. Variation in daily operating room volumes is a major problem because of the mismatch in timing between when the staff schedules are made and when the final demand is known (usually the day before). This uncertainty creates staffing challenges for all support and ancillary services, including nurses, orderlies, anesthesiologists, the recovery room, pathology, radiology and the sterile cart centre. Can he develop a method to improve surgical case volume prediction that is actionable in a managerially useful time frame? How can he engage and approach stakeholders who may often consider 'analytics,' 'data analysis' and 'computations' as a black hole?
Location:
Size:
Large
Other setting(s):
2012

About

Abstract

This supplement is to accompany the case. The newly appointed director of Surgical Business Analytics has been charged with the task of improving predictions of surgical case volume at Vanderbilt University Medical Center in Nashville, Tennessee. He is provided with 48 weeks of elective surgery schedule data that give the number of surgeries booked on specific dates prior to the surgery day and the actual number performed. Variation in daily operating room volumes is a major problem because of the mismatch in timing between when the staff schedules are made and when the final demand is known (usually the day before). This uncertainty creates staffing challenges for all support and ancillary services, including nurses, orderlies, anesthesiologists, the recovery room, pathology, radiology and the sterile cart centre. Can he develop a method to improve surgical case volume prediction that is actionable in a managerially useful time frame? How can he engage and approach stakeholders who may often consider 'analytics,' 'data analysis' and 'computations' as a black hole?

Settings

Location:
Size:
Large
Other setting(s):
2012

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