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Updated: Jun 17, 2026

A Novel Method for Involving Women of Color at High Risk for Preterm Birth in Research Priority Setting
Published on: January 12, 2018
Improving Maternal Health Equity and Outcomes Through the Development of a Clinician-Informed Algorithm: A
Jena Wallander Gemkow1, Eve Walter1, Nivedita Mohanty2
1AllianceChicago, Chicago, Illinois (Jena Wallander Gemkow, Dr. Walter, Ta-Yun Yang).
This study developed an algorithm to identify high-risk pregnant patients in federally qualified health centers (FQHCs). While improving postpartum visit prediction, the algorithm showed limited accuracy in predicting adverse maternal health outcomes.
Area of Science:
- Maternal Health
- Health Informatics
- Population Health Management
Background:
- Adverse maternal health outcomes are increasing postpartum.
- Outpatient settings can address postpartum complications.
- Federally Qualified Health Centers (FQHCs) serve vulnerable populations.
Purpose of the Study:
- Develop and test an algorithm for a population health tool.
- Identify high-risk prenatal patients within FQHCs.
- Improve identification of patients needing postpartum care.
Main Methods:
- Human-centered design for tool development.
- Focus groups and interviews with FQHC clinicians.
- Predictive modeling using electronic health record (EHR) data from 18 FQHCs.
Main Results:
- Algorithm improved postpartum visit recall by 45% (96% accuracy).
- Adverse outcome recall increased by 16%, but prediction accuracy was 42%.
- User interviews indicated the tool's utility in identifying high-risk patients.
Conclusions:
- Understanding EHR data and clinician involvement are crucial for intervention development.
- Future research should integrate diverse data sources for comprehensive risk assessment.
- Optimizing care for vulnerable maternal populations requires robust tools and data.
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