22 Feb 2016

Gatekeepers at Work:
An Empirical Analysis of a Maternity Unit

Status

Accepted at Management Science.

Authors

Michael Freeman, Cambridge Judge Business School
Nicos Savva, London Business School
Stefan Scholtes, Cambridge Judge Business School

Abstract

We use a detailed operational and clinical dataset from a maternity hospital to investigate how workload affects decisions in gatekeeper-provider systems, where the servers act as gatekeepers to specialists but may also attempt to serve customers themselves, albeit with a probability of success that is decreasing in the complexity of the customer’s needs. We study the effect of workload during a service episode on gatekeepers’ service configuration decisions and the rate at which gatekeepers refer customers to a specialist. We find that gatekeeper-providers (midwives in our context) make substantial use of two levers to manage their workload (measured as patients per midwife): They ration resource-intensive discretionary services (epidural analgesia) for customers with non-complex service needs (mothers with spontaneous onset of labor) and, at the same time, increase the rate of specialist referral (physician-led delivery) for customers with complex needs (mothers with pharmacologically induced labor). The workload effect in the study unit is surprisingly large and comparable in size to those for leading clinical risk factors: When workload increases from two standard deviations below to two standard deviations above the mean, non-complex cases are 28.8% less likely to receive an epidural, leading to a cost reduction of 8.7%, while complex cases are 14.2% more likely to be referred for a physician-led delivery, leading to a cost increase of 2.6%. These observations are consistent with overtreatment at both high and low workload levels, albeit for different types of patients, and suggest that smoothing gatekeeper workload would reduce variability in customer service experience.

Keywords

Gatekeeper Systems; Workload Management; Health care: Hospitals; Service Operations; Econometrics

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Published online on 22 Feb 2016 Contact me by email Connect with me on LinkedIn

michael.3829freeman@inseakd.edu