Delay in the induction of labour process: a retrospective cohort study and computer simulation of maternity unit workload

Abstract

ObjectivesDelay in the induction of labour (IOL) process is associated with poor patient experience and adverse perinatal outcome. Our objective was to identify factors associated with delay in the IOL process and develop interventions to reduce delay.Design and settingsWe performed a retrospective cohort study of maternity unit workload in a large UK district general hospital. Electronic hospital records were used to quantify delay in the IOL process and linear regression analysis was performed to assess significant associations between delay and potential causative factors. A novel computer maternity unit simulation model, MUMSIM (Maternity Unit Management SIMulation), was developed using real-world data and interventions were tested to identify those associated with a reduction in delay.ParticipantsAll women giving birth at Stoke Mandeville Hospital, Buckinghamshire National Health Service (NHS) Trust in 2018 (n=4932).Primary outcome measureDelay in the IOL process of more than 12 hours.ResultsThe retrospective analysis of real-world maternity unit workload showed 30% of women had IOL and of these, 33% were delayed >12 hours with 20% delayed >24 hours, 10% delayed >48 hours and 1.3% delayed >72 hours. Delay was significantly associated with the total number of labouring women (p=0.008) and the number of booked IOL (p=0.009) but not emergency IOL, spontaneously labouring women or staffing shortfall. The MUMSIM computer simulation predicted that changing from slow release 24-hour prostaglandin to 6-hour prostaglandin for primiparous women would reduce delay by 4% (p<0.0001) and that additional staffing interventions could significantly reduce delay up to 17.9% (p<0.0001).ConclusionsPlanned obstetric workload of booked IOL is associated with delay rather than the unpredictable workload of women in spontaneous labour or emergency IOL. We present a novel maternity unit computer simulation model, MUMSIM, which allows prediction of the impact of interventions to reduce delay.