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Background Maternal mortality in the United States is a public health crisis and national emergency. Missed or delayed recognition of preventable life-threatening symptoms and untimely treatment of preventable high-risk medical conditions have been cited as key contributors to the nation’s worsening mortality rates. Effective strategies are urgently needed to address this maternal health crisis, particularly for Black birthing populations. Morbidity and Mortality Assessment: Lifting Outcomes Via Education (MAMA LOVE) is a web-based platform that focuses on the identification of maternal morbidity and mortality risk factors. Objective The purpose of this paper is to present the conceptualization, development, heuristics, and utility evaluation of the web-based maternal mortality risk assessment and educational tool MAMA LOVE. Methods A user-centered design approach was used to gain feedback from clinical experts and potential end users to ensure that the tool would be effective among groups most at risk for maternal morbidity and mortality. A heuristic evaluation was conducted to evaluate usability and need within the current market. Algorithms describing key clinical, mental health, and social conditions were designed using digital canvas software (Miro) and incorporated into the final wireframes of the revised prototype. The completed version of MAMA LOVE was designed in Figma and built with the SurveyJS platform. Results The creation of the MAMA LOVE tool followed three distinct phases: (1) the content development and creation of an initial prototype; (2) the feedback gathering and usability assessment of the prototype; and (3) the design, development, and testing of the final tool. The tool determines the corresponding course of action using the algorithm developed by the authors. A total of 38 issues were found in the heuristic evaluation of the web tool’s initial prototype. Conclusions Maternal morbidity and mortality is a public health crisis needing immediate effective interventions. In the current market, there are few digital resources available that focus specifically on the identification of dangerous symptoms and risk factors. MAMA LOVE is a tool that can address that need by increasing knowledge and providing resources and information that can be shared with health care professionals.

BACKGROUND Maternal mortality in the United States is a public health crisis and national emergency. Missed or delayed recognition of preventable life-threatening symptoms and untimely treatment of preventable high-risk medical conditions have been cited as key contributors to the nation’s worsening mortality rates. The development of effective strategies are urgently needed to address this maternal health crisis, particularly among Black birthing populations. MAMA LOVE (Morbidity and Mortality Assessment: Lifting Outcomes Via Education) is a web-based platform that focuses on the identification of maternal morbidity and mortality risk factors. OBJECTIVE The purpose of this paper is to present the conceptualization, development, heuristics, and utility evaluation of the web-based maternal mortality risk assessment and educational tool, MAMA LOVE. METHODS Algorithms describing key clinical, mental health, and social conditions were designed utilizing digital canvas software (Miro) and incorporated into the final wireframes of the initial prototype. A user-centered design approach was used to gain feedback from potential end users to ensure that the tool would be effective among groups most at risk for maternal morbidity and mortality. A market analysis and heuristic evaluation were conducted to evaluate useability and need within the current market. RESULTS MAMA LOVE was designed in Figma and built with the SurveyJS platform. The tool determines the corresponding course of action using the algorithm developed by the authors. 38 issues were found in the heuristic evaluation of the web tool's initial prototype. Market analysis identified 5 web-based applications with a maternal health disparity focus, however MAMA love was found to be unique due to the feature related to provision of resources based on user-centered information. CONCLUSIONS Maternal morbidity and mortality is a public health crisis needing immediate effective interventions. MAMA LOVE is a tool that can address that need by increasing knowledge, providing resources, and providing information that can be shared with healthcare professionals. CLINICALTRIAL n/a

Background Racial disparities exist in maternal morbidity and mortality, with most of these events occurring in healthy pregnant people. A known driver of these outcomes is unplanned cesarean birth. Less understood is to what extent maternal presenting race/ethnicity is associated with unplanned cesarean birth in healthy laboring people, and if there are differences by race/ethnicity in intrapartum decision-making prior to cesarean birth. Methods This secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-Be (nuMoM2b) dataset involved nulliparas with no significant health complications at pregnancy onset who had a trial of labor at ≥ 37 weeks with a singleton, non-anomalous fetus in cephalic presentation (N = 5,095). Logistic regression models were used to examine associations between participant-identified presenting race/ethnicity and unplanned cesarean birth. Participant-identified presenting race/ethnicity was used to capture the influence of racism on participant’s healthcare experiences. Results Unplanned cesarean birth occurred in 19.6% of labors. Rates were significantly higher among Black- (24.1%) and Hispanic- (24.7%) compared to white-presenting participants (17.4%). In adjusted models, white participants had 0.57 (97.5% CI [0.45–0.73], p < 0.001) lower odds of unplanned cesarean birth compared to Black-presenting participants, while Hispanic-presenting had similar odds as Black-presenting people. The primary indication for cesarean birth among Black- and Hispanic- compared to white-presenting people was non-reassuring fetal heart rate in the setting of spontaneous labor onset. Conclusions Among healthy nulliparas with a trial of labor, white-presenting compared to Black or Hispanic-presenting race/ethnicity was associated with decreased odds of unplanned cesarean birth, even after adjustment for pertinent clinical factors. Future research and interventions should consider how healthcare providers’ perception of maternal race/ethnicity may bias care decisions, leading to increased use of surgical birth in low-risk laboring people and racial disparities in birth outcomes.