title: Using the Nippita classification system for women undergoing induction of labour in a large metropolitan maternity service: Bringing simplicity and certainty to an important quality improvement process.
abstract: In 2015 Nippita and colleagues developed a novel system to classify women undergoing induction of labour (IOL), which sought to overcome the problems of indication-based classification. We explored the utility and feasibility of this new system at Monash Health in Melbourne. We found overall induction rates of 24.7% compared with the New South Wales rates of 25.4% reported by Nippita et al. The classification system was easy to apply because it uses routinely and accurately collected data. There was no misinterpretation of the classification groups. The system provides a robust means for auditing IOLs and reviewing their appropriateness.
authors.affiliation: School of Nursing and Midwifery, Clayton Campus, Monash University, Melbourne, Victoria, Australia.
authors.forename: Mary A
authors.initials: MA
authors.lastname: Biro
authors.affiliation: School of Nursing and Midwifery, Clayton Campus, Monash University, Melbourne, Victoria, Australia.|Monash Health, Monash Medical Centre, Melbourne, Victoria, Australia.
authors.forename: Christine E
authors.initials: CE
authors.lastname: East
mesh_terms: D002585:Cesarean Section; D003430:Cross-Sectional Studies; D005260:Female; D005865:Gestational Age; D006771:Hospitals, Maternity; D006786:Hospitals, Urban; D006801:Humans; D007746:Labor Presentation; D007751:Labor, Induced / Q000145:classification*; D010298:Parity; D011247:Pregnancy; D058996:Quality Improvement
keywords: classification; induction of labour; quality improvement