This May, the Health and Human Services (HHS) Office of Women’s Health released a new maternal prenatal risk index called the m-PRI. Developed by researchers with funding from the Federal Health and Human Services (HHS) Office of Women’s Health, it quantifies risk based on existing maternal conditions. The m-PRI is a composite, mortality-informed risk scoring tool used to identify pregnant patients at high risk for severe maternal morbidity and mortality during labor and delivery or in the immediate days postpartum. The published research in The Lancet demonstrates that the m-PRI significantly outperforms the Obstetric Comorbidity Scoring System (OCSS) in predicting maternal mortality risk.
What the m-PRI Does
- Identifies Vulnerability: The index calculates a single composite risk score by summing the weighted values of a patient’s pre-existing conditions and demographic factors.
- It can be used to Improve Outcomes: If implemented effectively, it can help clinicians spot at-risk mothers early in pregnancy, ensuring timely intervention and preventing severe complications during delivery.
Below is a mapping of the 28 risk conditions identified by the researchers across their clinical domains, along with their relative scoring categories within the HHS Office on Women’s Health model.
| Clinical Domain | The 28 Evaluated Risk Conditions | Framework Severity Tier & Statistical Impact |
| Placental & Obstetric(Highest Acute Risk) | • Placenta Accreta Spectrum (PAS)• Severe Preeclampsia / Eclampsia• Placental Abruption / Previa• Prior Cesarean / Major Uterine Surgery• Multiple Gestation (Twins/Triplets)• History of Stillbirth / Fetal Death | Tier 4: Critical Risk (β > 2.5) Placenta accreta spectrum conditions carry the highest ordinal statistical weights due to their extreme correlation with massive blood transfusions and surgical complications. |
| Cardiovascular & Vascular(High Core Mortality) | • Pulmonary Hypertension• Heart Failure / Cardiomyopathy• Acquired Cardiac Disease• Congenital Heart Defects• Specified Complex Arrhythmias• Vascular Disease / Chronic Hypertension | Tier 3: High-Impact (β: 1.6 to 2.5)Pulmonary hypertension and heart failure represent the leading drivers of indirect maternal death, requiring severe care escalations. |
| Renal & Metabolic(Chronic Progression) | • Chronic Renal (Kidney) Disease• Pre-existing Diabetes (Type 1 or 2)• Morbid Obesity (BMI \(\ge \) 40)• Gestational Diabetes• Thyroid / Metabolic Disorders | Tier 2 to 3: Moderate to High (β: 1.0 to 1.8)Chronic renal failure scores closely to Tier 3, while pre-pregnancy diabetes and severe obesity layer heavily into cumulative metrics. |
| Systemic & Neurological(Systemic Complicators) | • Active Malignancy (Cancer)• Epilepsy / Complex Seizure Disorders• Autoimmune / Rheumatoid Diseases• Chronic Pulmonary Disease / Asthma• Active Chronic Infections (HIV/Hep) | Tier 1 to 2: Low to Moderate (β: 0.5 to 1.2)These conditions expand the tool’s precision-recall area over older models (like the OCSS) by identifying systemic vulnerabilities. |
| Behavioral & Demographics(Baseline Modifiers) | • Substance Use Disorder (SUD)• Severe Alcohol Use Disorder• Major Depression / Anxiety Disorders• Bipolar Disorder / Severe Mental Illness• Advanced Maternal Age (35+)• High-Risk Socioeconomic Demographics | Tier 1: Low-Impact Additive (β: 0.1 to 0.5)Mental health, behavioral conditions, and age represent foundational risk modifiers that increment the cumulative baseline score. |
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Background About Implementation
Hospital health systems in the study plan to integrate the m-PRI into Electronic Health Record (EHR) platforms such as Epic and Oracle’s Cerner, using automated background algorithms that scan existing patient charts during the initial prenatal intake. This ensures that clinicians receive a clear risk score without needing to perform manual data entry. [1, 2]
To seamlessly implement this without causing alert fatigue or disrupting existing physician workflows, participating health systems are focusing on several key technical and procedural strategies:
1. Automated Chart Scraping and Background Calculation
The index is designed to pull raw data directly from existing EHR fields. [1]
- Dynamic Extractions: The algorithm will automatically review the patient’s past medical history, active problem lists, demographic data, and intake questionnaires.
- Zero Manual Input: By compiling the 28 composite risk factors behind the scenes, the EHR generates a baseline score before the OB/GYN even steps into the exam room. [1]
2. Point-of-Care Clinical Decision Support (CDS)
Instead of burying the score in a deep sub-menu, health systems plan to embed the m-PRI directly into standard obstetric workflows.
- Visual Banners: High-risk scores will appear as a color-coded visual indicator or banner on the patient’s main pregnancy dashboard.
- Actionable Prompting: Rather than a simple pop-up warning, the EHR will offer immediate, actionable steps tailored to the score, such as prompting the clinician to order early gestational diabetes screening or schedule a Maternal-Fetal Medicine (MFM) consult.
3. Integrated Care Routing and Referrals
Health systems are leveraging the m-PRI to automate complex scheduling and administrative processes.
- Facility Matching: If a patient’s score crosses a specific severity threshold, the EHR can flag the chart to ensure delivery is planned at a facility with the appropriate Level III or IV maternal care capabilities.
- Standardized Order Sets: An elevated score can trigger an automated “high-risk prenatal package” within the EHR, standardizing the frequency of third-trimester ultrasounds or remote blood pressure monitoring orders. [1]
4. Cross-Platform Interoperability
Because patients frequently switch providers or transfer facilities during pregnancy, data portability is crucial.
- FHIR Standards: Health networks are building these tools using Fast Healthcare Interoperability Resources (FHIR) API frameworks.
- Seamless Transfers: This allows the m-PRI score and its underlying data components to transfer securely between community clinics, independent OB/GYN practices, and major regional hospital systems. [1, 2]
5. Population Health Surveillance
At an administrative level, health systems plan to aggregate m-PRI data across their entire patient population. [1]
- Resource Allocation: Executive teams can use collective data to track high-risk geographic trends (by zip code) and better allocate specialized midwives, maternal clinics, and community health resources where they are most urgently needed.
The Policy Center will continue to monitor progress with mPRI implementation and is urging the Office of Women’s Health and its research partners to support similar analysis and framework development for postpartum maternal mortality and morbidity.
