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Learn how AI can help to address racial and ethnic maternal health disparities and anticipate and avoid adverse outcomes. Discover how AI can proactively identify women at the highest risk for obstetric adverse outcomes, surface their specific risk factors, and help to develop tailored interventions. See how AI can help mitigate implicit biases in care delivery, measure and track disparities over time, and pinpoint gaps in the maternal care continuum.

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Medical Claims

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EHR data with comprehensive patient histories of vital signs and symptoms, problem lists and chief complaints, tests results, diagnoses and procedures, and prescriptions.

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thousands of auto-generated, clinically relevant contributing factors

Elira Fraiser
37-Year-Old Female
Risk of preeclampsia-related preterm birth in the next 3 months
Risk Score Percentile
Impact on Risk  |  Contributing Factor
+26% | # of Prenatal Care Visits (6M)
+24% | Rise in Blood Pressure (mmHg)
130/80 to 145/90
+17% | Diagnosis of Diabetes (12M)
+15% | Eligible for Full Medicaid Benefits

Why It Matters

The 2020 U.S. maternal mortality rate ranked last among all industrialized countries, with 17.4 deaths per 100,000 pregnancies.¹ For every maternal death measured, 100 women also experience severe obstetric morbidity.² This is a potentially life-threatening outcome of labor and delivery, often resulting in significant health consequences. More than 60,000 women suffer from severe maternal morbidity annually, and the rate of maternal morbidity increased by 36% from 2008 to 2018.²𝄒³ Adverse obstetric outcomes also present a substantial financial burden. Severe maternal morbidity has been associated with an increase in maternity-related costs of 111% in commercially-insured populations and 175% in populations covered by Medicaid.⁴ Such increases are impactful; there were 3.75 million births in 2019 and Medicaid paid for approximately 50% of them.⁵𝄒⁶

Maternal health data evinces immense racial and ethnic disparities. On average, Black women are three to four times more likely to die a pregnancy-related death compared to White women, and they are up to 12 times more likely in some cities.² In addition to a dramatically increased mortality rate, Non-Hispanic Black women have the highest rates for the majority of CDC severe morbidity indicators. Black women also have elevated rates of pregnancy-induced and chronic hypertension, asthma, placental disorders, preexisting diabetes, and blood disorders. Minority women are also less likely to have chronic conditions adequately managed prior to pregnancy, and they are more likely to experience complications due to these conditions.² 

Reducing potentially preventable maternal morbidity and mortality hinges on reducing the racial and ethnic disparities. In a study of maternal deaths 46% of black deaths were considered potentially preventable compared to 33% of white deaths.² In addition to minorities having higher likelihood of chronic conditions during pregnancy, health outcome disparities are correlated with a plethora of socioeconomic-related factors, such as hospital quality. 75% of Black deliveries occur in a quarter of U.S. hospitals, but just 18% of White deliveries occur in the same hospitals.² On average, these hospitals have higher risk-adjusted maternal morbidity rates. Similarly, a national survey of women’s childbearing experiences found that roughly 25% of respondents experienced discrimination during hospitalization, and Black and Hispanic women were nearly three times as likely to indicate concerns with their treatment due to race, ethnicity, and cultural background.⁷

AI Presents an Opportunity

Healthcare organizations (HCOs) can leverage AI to help address racial and ethnic maternal health disparities and improve obstetric outcomes. Predictive analytics can proactively identify women at the highest risk for obstetric adverse events, surface their specific risk factors, and help to develop tailored interventions. Further, it can mitigate implicit biases in care delivery, provide a solution to measure disparities across race and ethnicity over time, and help to identify specific gaps in the maternal care continuum for populations and individual patients. 

Armed with AI-driven insights, HCOs can proactively implement proven interventions, such as enrolling patients in midwifery-led care in birth centers. Birth center care and a focus on enhanced prenatal care were evaluated extensively through CMMI’s Strong Start For Mothers and Newborns Initiative. The program retained standard components of birth center care, but it specifically focused on longer midwifery visits and stronger relationships, personalized education, resource referrals, and psychosocial needs assessments. This produced significantly better outcomes while substantially reducing racial disparities. Strong Start found that Black, Hispanic, and White women enrolled in birth center care experienced a 5–6% reduction in preterm births.⁸ Black women also had a 5% reduction in low birth weight and experienced cesarean at a 15.1% rate—less than half the national rate of 35.9%.⁵ Birth center care is also associated with greater savings. The estimated Medicaid savings from prevented cesareans and reduction in preterm births per 10,000 births totaled approximately $28.5 million.⁵

Did You Know...

  • 46% of maternal deaths for Black women are considered potentially preventable²
  • 3-4x higher likelihood of maternal mortality for Black women compared to White women²
  • More than 60,000 women suffer from maternal morbidity annually in the U.S.²
  • Roughly 50% of births in 2019 were paid for by Medicaid⁵
Citations & Footnotes

1. Declerq, Eugene, Zephyrin, Laurie. “Maternal Mortality in the United States: A Primer.” The Commonwealth Fund, 16 Dec. 2020, https://doi.org/10.26099/ta1q-mw24. Accessed 22 Oct. 2021.

2. Howell, Elizabeth. “Reducing Disparities in Severe Maternal Morbidity and Mortality.” Clinical Obstetrics and Gynecology, vol. 61, no. 2, Jan. 2018, pp. 387–399, doi: 10.1097/GRF.0000000000000349. Accessed 22 Oct.

3. Premier. “Bundle of Joy: Maternal & Infant Health Trends.” Premier, 2020, pp. 1–14, https://explore.premierinc.com/MaternalHealthTrends/landing-page-copy-67V7-863BH.html?. Accessed 22 Oct. 2021. 

4. Black M., Christopher, Vesco K. Kimberly, et al. “Costs of Severe Maternal Morbidity in U.S. Commercially Insured and Medicaid Populations: An Updated Analysis.” Women’s Health Reports, vol. 2, no. 1, 27 Sep. 2021, pp. 443–451. http://doi.org/10.1089/whr.2021.0026. Accessed 20 Oct. 2021.

5. Alliman, Jill, et al. “Strong Start in Birth Centers: Socio‐Demographic Characteristics, Care Processes, and Outcomes for Mothers and Newborns.” Birth, vol. 46, no. 2, 17 May 2019, pp. 234–243, 10.1111/birt.12433. Accessed 20 Oct. 2021.

6. CDC. “National Vital Statistics System.” Centers for Disease Control and Prevention, last reviewed 27 Sep. 2021. https://www.cdc.gov/nchs/nvss/births.htm. Accessed 22 Oct. 2021.

7. Declerq, Eugene, Sakala, Carol, et al. “Listening to Mothers III Pregnancy and Birth.” Childbirth Connection, May 2013, pp. 1–75. https://www.nationalpartnership.org/our-work/resources/health-care/maternity/listening-to-mothers-iii-pregnancy-and-birth-2013.pdf. Accessed 20 Oct. 2021. 

8. Alliman, Jill, Bauer, Kate. “Next Steps for Transforming Maternity Care: What Strong Start Birth Center Outcomes Tell Us.” Journal of Midwifery & Women’s Health, vol. 65, no. 4, 11 Apr. 2020, pp. 462–465, doi:10.1111/jmwh.13084.

ClosedLoop is an exciting and important partner in our strategy to develop timely predictive insights through operationalized AI solutions that will drive member engagement and better health outcomes.

Pat Wang
President & CEO, Healthfirst

Our partnership with ClosedLoop adds the depth of AI to more accurately identify those patients who are most likely to benefit from our primary care interventions as well to evaluate these interventions and make more relevant and timely modifications to improve their effectiveness.

David Klebonis
Chief Operating Officer, PBACO

We are extremely impressed with the predictive modeling capabilities the ClosedLoop platform has delivered. The ClosedLoop team has exceeded all defined goals and benchmarks set to date and we anticipate a substantial return on our investment as these predictions are operationally deployed.

Cheryl Lulias
President & Executive Director, MHN

We’re able to store and operationalize analytics directly from ClosedLoop. That’s driving real value—it’s accelerated the implementation of key insights into clinical workflows and it allows us to more easily account for all of the different factors that influence intervention decisions.

Christer Johnson
Chief Analytics Officer, Healthfirst

At SWHR, our patient-centered network is committed to innovative care models that are value-based, high-quality, and data-driven. Because of our partnership with ClosedLoop, we’re better able to identify and act on the most impactful opportunities for our physician partners to improve patient results and reduce unnecessary costs.

Dr. Jason Fish
Chief Medical Officer & SVP, Southwestern Health Resources

We compared the ClosedLoop predicted spend to the spend predicted by a leading rules-based risk prediction engine and found that 75% of the time ClosedLoop was closer, and in most cases significantly closer, to the actual spend.

Phillip Bruns
Chief Technology Officer, CareATC

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