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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
Author(s) -
Biro Mary A.,
East Christine E.
Publication year - 2017
Publication title -
australian and new zealand journal of obstetrics and gynaecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.734
H-Index - 65
eISSN - 1479-828X
pISSN - 0004-8666
DOI - 10.1111/ajo.12598
Subject(s) - certainty , audit , metropolitan area , process (computing) , service (business) , computer science , quality (philosophy) , simplicity , operations management , medicine , business , engineering , mathematics , marketing , accounting , philosophy , epistemology , pathology , operating system , geometry
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 IOL s and reviewing their appropriateness.