Case study 7: Addressing racial disparities in maternal morbidity
Global State of Patient Safety 2023
Country where the case study originated highlighted in red. Where relevant, additional countries where this programme has been implemented are highlighted in blue.
What is it?
The Alliance for Innovation on Maternal Health (AIM) is a national data-driven maternal safety and quality improvement initiative implemented in hospitals in the US state of Texas. An obstetric haemorrhage patient safety bundle was developed by AIM to establish a safer culture and environment from which care can be delivered, which was accompanied by work to reduce peripartum racial and ethnic disparities to eliminate inequalities in the care provided.
The bundle does not define how to manage care but suggests the actions that units and teams need to take, or the processes that should be in place. Key individuals including clinical and operational leaders attended learning sessions and performed a gap analysis for each bundle, to assess whether its elements were already implemented in the hospital. Following implementation of the intervention, the rate of severe maternal morbidity from haemorrhage (SMM-H) in Black women decreased from 45.5% to 31.6%.
Why was it developed?
Significant disparities persist in the safety of maternity care provided to Black and Hispanic women. In the US, Black women are 3-4 times more likely to die during childbirth and have twice the risk of severe maternal morbidity.
Maternal death is just the tip of iceberg, and many more women experience severe maternal morbidity, which commonly involves haemorrhage, a leading cause of death during delivery and the first six days following. A significant proportion of severe maternal morbidity and mortality events are preventable.
Upon examining the severe maternal morbidity data, it was disaggregated by race, ethnicity and language, and discovered that black patients had significantly higher morbidity from haemorrhage than anyone else in the hospital:
“When the CDC started sharing data…breaking it down by race, ethnicity, income and education, we recognised that the known disparities could not simply be attributed to underlying medical conditions. Before this, these disparities were thought to be related to higher underlying comorbid conditions in the black population, for example, diabetes or hypertension or obesity.
The CDCs data highlighted that black women were still at higher risk of death and complications independent of income, level of education, and comorbid conditions. It changed the conversation and helped us start to understand the contribution of implicit bias, discrimination, and racism as underlying root causes to these disparities.”
How can it be adopted?
While it is not necessary to have a specialised project or specific intervention to address disparities, it is important to examine the data by race, ethnicity and language, to share it with the relevant teams, and have open discussions about how to make improvements. Root causes and opportunities for improvement can be identified, whilst action should be taken to ensure some aspects of care are not made worse for certain groups.
Ongoing training and lifelong learning is needed around root causes. For example, implicit bias training is essential, but should be one part of an overall culture of creating a safe space to talk about such topics. Implementation should be an ongoing process to ensure all aspects of the intervention are sustained, and other issues highlighted in the outcome data are continually examined.
How can the measurement of patient safety be improved?
“There’s a new maternity care survey now that asks hospitals to voluntarily submit data, and hospitals are ranked [on their performance]. While we don’t necessarily agree that these metrics are the most important predictors in determining quality and patient safety, we do appreciate the fact that the survey is asking that we submit our data by race and ethnicity.
Additionally, some other regulatory bodies are introducing requirements around health equity. So, on the one hand, we took QI projects that we were already implementing and use disaggregated data to improve out outcomes. And now, on the other hand, we take data that we’re being asked to provide, and now we’re creating QI projects around them to figure out why do we have these disparities and how can we correct them?”
Key resources and contact details
Alliance for Innovation on Maternal Health (AIM)
Dr Christina Davidson: cmdavids@bcm.edu
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