Summary: | <h4>Objectives</h4>Most indicators proposed for assessing quality of care in obstetrics are process indicators and do not directly measure health effects, and cannot always be identified from routinely available databases. Our objective was to propose a set of indicators to assess the quality of hospital obstetric care from maternal morbidity outcomes identifiable in permanent hospital discharge databases.<h4>Methods</h4>Various maternal morbidity outcomes potentially reflecting quality of obstetric care were first selected from a systematic literature review. Then a three-round Delphi consensus survey was conducted online from 11/2016 through 02/2017 among a French panel of 37 expert obstetricians, anesthetists-critical-care specialists, midwives, quality-of-care researchers, and user representatives. For a given maternal outcome, several definitions could be proposed and the indicator (i.e. corresponding rate) could be applied to all women or restricted to specific subgroup(s).<h4>Results</h4>Of the 49 experts invited to participate, 37 agreed. The response rate was 92% in the second round and 97% in the third. Finally, a set of 13 indicators was selected to assess the quality of hospital obstetric care: rates of uterine rupture, postpartum hemorrhage, transfusion incident, severe perineal lacerations, episiotomy, cesarean, cesarean under general anesthesia, post-cesarean site infection, anesthesia-related complications, postpartum pulmonary embolism, maternal readmission and maternal mortality. Six were considered in specific subgroups, with, for example, the postpartum hemorrhage rate assessed among all women and also among women at low risk of PPH.<h4>Implications</h4>This Delphi process enabled us to define consensually a set of indicators to assess the quality of hospital obstetrics care from routine hospital data, based on maternal morbidity outcomes. Considering 6 of them in specific subgroups of women is especially interesting. These indicators, identifiable through codes used in international classifications, will be useful to monitor quality of care over time and across settings.
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