Background/Objectives: Women’s health has historically lagged behind other medical specialties in transformative innovation, despite significant technological advances in adjacent fields. In this collection of papers, we examine the current state of innovation in women’s health and maternal–fetal medicine, identify barriers to transformation, and propose strategies for accelerating breakthrough developments. This paper presents an overview of multiple forces and their often-competing relationships that influence the environment in which advances in multiple areas of healthcare have had to navigate to enter mainstream practice. An understanding of these forces is essential to explain why some new technologies are readily deployed into clinical practice while others take many years to be adopted. Understanding the entire “echo-system” around any specific technology provides a much fuller understanding of how any individual advance can make its way into actual utilization. Methods: We synthesized current literature on innovation in women’s health, analyzing technological advances in artificial intelligence, precision medicine, non-invasive diagnostics, and surgical robotics. We examined patterns of innovation adoption and barriers to implementation across multiple domains. Results: Several key areas presented in this paper and the following show promise for transformative change: artificial intelligence (AI)-driven diagnostics achieving expert-level performance in prenatal screening, precision medicine approaches transforming genetic disease management, and non-invasive monitoring technologies revolutionizing maternal–fetal care. However, systemic barriers including regulatory complexity, liability concerns, and institutional inertia continue to limit widespread adoption of numerous breakthrough technologies. Conclusions: The convergence of multiple technological advances, particularly artificial intelligence and precision medicine, positions women’s health for unprecedented transformation. Success requires fostering innovation-ready environments, embracing systems-awareness approaches, and maintaining focus on human-centered care while leveraging technological capabilities with continual feedback and course corrections.

Purpose: The United States has the highest maternal and neonatal mortality rates among the 45 high-income countries demonstrating enormous discrepancies between white and black mothers and infants. These outcome discrepancies have worsened over the last decade despite the availability of insurance coverage from the Affordable Care Act (ACA) to over 40 million previously uninsured Americans. We have compared differences in each state government’s acceptance of Medicaid expansion, overall insurance coverage, and ACA marketplace uptake. We next investigated how states implemented new ACA-enabled paradigms allowing the creation of very large monopolies for health care using Accountable Care Organizations (ACOs), in exchange for accepting Medicaid expansion to increase care for indigent patients. ACO’s were granted exemptions from Stark and Sherman Anti-trust prohibitions and permitted rewarding for “in house” referrals and could punish physicians for “system leakage.” However, many states did not expand Medicaid benefits, although the federal government would be paying 90% of costs. Nevertheless, they retained those regulatory waivers, actually exacerbating inequalities. Methods: Using publicly available, de-identified databases such as the US Census and CDC, we compared closures of hospital and L&D units, maternal mortality ratios (MMRs), neonatal mortality rates (NMRs), occurrence of maternity or health care deserts (DSRTs), and under-resourced (URS) areas across all states and DC. After considering several approaches for standardization including population, area, population density, healthcare deserts, proximity, and hospital beds, we selected population and square mileage as the primary denominators to compare 10 pairings of reasonably comparably sized states by their percentages of patients living in DSRTs reflecting lower rates of overall insurance coverage, Medicaid expansion, life expectancy, and multiple health morbidity rates. We evaluated ratios of DSRTs, MMR, and NMR within pairs. Results: Nationwide, there have been more than 100 hospital and 300 L & D closures since ACA. These have occurred disproportionately in rural areas, usually as large ACOs were allowed to just shut “under-performing” hospitals within their systems. Overall, US MMR for white patients is 26.6 and for black patients 69.9. NMR is 4.4 and 10.38 respectively. In NY, MMR is 14.1 and 55.6 respectively, but in GA white patients are 59.7 and black patients 95.6. (i.e. white GA ≈black NY). Overall health status declined for people of color and, when exposed to challenges like DSRTs and UNR, such stresses have disproportionately larger impacts for a lower health status group compared to a higher one. TX has DSRTs affecting 24.4% of its population vs CA at 0.09%. Dividing TX by CA gives a ratio of 271, MMR ratio of 2.69, NMR ratio of 1.28. FL/NY: DSRTs 1.71, MMR 1.18, NMR 2.37; for middle sized states: OH/PA: DSRTs 191, MMR 1.44, NMR 1.17; ; GA/IL: DSRTs 1.91, MMR 1.89, NMR 1.00; LA/OR: DSRTs 1.78, MMR 2.41, NMR 1.44, IN/MA: DSRTs 440, MMR 2.1, NMR 1.43; MO/IL: DSRTs 1.42 MMR 1.28, 1.25; TN/VA: DSRTs 1.14, MMR1.39, NMR 1.01; MS/MN: DSRTs 4.93, MMR 3.44, NMR 1.66. For smaller states: WV/ME: DSRTs 2116.5, MMR 25.1, NMR 1.0. Most differences are p<.001 (not shown). Conclusions: Theoretically, ACA coverage should have universally improved actual access to care. However, some medical metrics such as maternal-child care have significantly worsened. Not only have MMR and NMR increased, but the disparities between white and black patients have widened (not shown). Our data suggest these have been partly due to ACA/ACO sanctioned hospital and L & D closures. While health system margins increased, more DSRTs and URSs appeared, particularly impacting care for both poor white and black patients in rural areas compared to urban areas with the latter having more available care providers. Since the 1980s, the societal and political power of physicians relative to hospitals, insurance companies, and governmental authorities has significantly declined as have health care performance metrics. DSRTs is not a perfect parameter for defining risks for poor care risks, but it does identify a serious problem in how the American health care industry has changed from a professionally driven model to a big business model. Health care delivery employing big business rather than medical ethical and performance standards has fundamentally failed. As an example, mandating in-house referrals, imposed by corporate management may be cost-efficient in the short run but can deny patients access to better care that might be available in other hospitals or medical offices that can improve outcomes and ultimately better economics for patients, governments, and private insurance carriers. US medicine has reached a crossroad, and decisions must be made. As with other sanctioned monopolies like utilities, the medical enterprise representing 17% of US Gross National Product has an obligation to serve areas with low financial performance as well as those that are more lucrative. In business, unfettered monopolies lead to higher prices and lower quality goods. We now see that the business practices of healthcare monopolies have worsened maternal child health and also lead to inequalities of care.

Background/Objectives: Electronic fetal monitoring (EFM) has been used for intrapartum fetal surveillance for over 50 years. Despite numerous trials comparing EFM with standard fetal heart rate (FHR) auscultation, it remains contentious whether continuous monitoring with standard interpretation has reliably improved perinatal outcomes, specifically lower rates of perinatal morbidity and mortality. This review examines previous attempts to improve fetal monitoring and presents future directions for novel intrapartum fetal surveillance systems. Methods: We conducted a chronological review of EFM developments, including ancillary methods such as fetal ECG analysis, automated systems for FHR analysis, and artificial intelligence applications. We analyzed the evolution from visual interpretation to intelligent systems and evaluated the performance of various automated monitoring platforms. Results: Various ancillary methods developed to improve EFM accuracy for predicting fetal compromise have shown limited success. Only a limited number of studies demonstrated that adding fetal ECG analysis to visual FHR pattern interpretation resulted in better fetal outcomes. Automated systems for FHR analysis have not consistently enhanced intrapartum fetal surveillance. However, novel approaches such as the Fetal Reserve Index (FRI) show promise by incorporating clinical risk factors with traditional FHR patterns to provide higher-level risk assessment and prognosis. Conclusions: The shortcomings of visual interpretation of FHR patterns persist despite technological advances. Future intelligent intrapartum surveillance systems must combine conventional fetal monitoring with comprehensive risk assessment that incorporates maternal, fetal, and obstetric factors. The integration of artificial intelligence with contextualized metrics like the FRI represents the most promising direction for improving intrapartum fetal surveillance and clinical outcomes.

Sixty years ago, the purpose of introducing electronic fetal heart rate monitoring (EFM) was to reduce the incidence of intrapartum stillbirth. However, by the early 1980s, with falling stillbirth rates, fetal blood sampling had been widely abandoned, as many considered that EFM was sufficient on its own. Unfortunately, while the sensitivity of EFM for the detection of potential fetal compromise is high, specificity is low, and there is a high false positive rate which has been associated with a rising cesarean section rate. The authors suggest that EFM is considered and analyzed as a classic screening test and not a diagnostic test. Furthermore, it requires contextualization with other risk factors to achieve improved performance. A new proposed metric, the Fetal Reserve Index, takes into account additional risk factors and has demonstrated significantly improved performance metrics. It is going through the phases of further development, evaluation, and wider clinical implementation.

Publications on artificial intelligence (AI) applications have dramatically increased for most medical specialties, including obstetrics. Here, we review the most recent pertinent publications on AI programs in obstetrics, describe trends in AI applications for specific obstetric problems, and assess AI's possible effects on obstetric care. Searches were performed in PubMed (MeSH), MEDLINE, Ovid, ClinicalTrials.gov , Google Scholar, and Web of Science using a combination of keywords and text words related to "obstetrics," "pregnancy," "artificial intelligence," "machine learning," "deep learning," and "neural networks," for articles published between June 1, 2019, and May 31, 2024. A total of 1,768 articles met at least one search criterion. After eliminating reviews, duplicates, retractions, inactive research protocols, unspecified AI programs, and non-English-language articles, 207 publications remained for further review. Most studies were conducted outside of the United States, were published in nonobstetric journals, and focused on risk prediction. Study population sizes ranged widely from 10 to 953,909, and model performance abilities also varied widely. Evidence quality was assessed by the description of model construction, predictive accuracy, and whether validation had been performed. Most studies had patient groups differing considerably from U.S. populations, rendering their generalizability to U.S. patients uncertain. Artificial intelligence ultrasound applications focused on imaging issues are those most likely to influence current obstetric care. Other promising AI models include early risk screening for spontaneous preterm birth, preeclampsia, and gestational diabetes mellitus. The rate at which AI studies are being performed virtually guarantees that numerous applications will eventually be introduced into future U.S. obstetric practice. Very few of the models have been deployed in obstetric practice, and more high-quality studies are needed with high predictive accuracy and generalizability. Assuming these conditions are met, there will be an urgent need to educate medical students, postgraduate trainees and practicing physicians to understand how to effectively and safely implement this technology.

Objective: Intrauterine resuscitation (IR) may be employed during labor to reduce emergency deliveries with concerns for fetal wellbeing emanating mostly from increased uterine contraction frequency and/or intensity. However, there is no standard definition of what constitutes IR, and how its impact is assessed. Here, we have created two measures of relative IR effectiveness, determined over a two-hour time frame after Pitocin was first initiated, and asked how fetal risk severity at the time of its initiation impacted IR effectiveness and the clinical decisions made. Methods: We analyzed 118 patients receiving Pitocin who underwent IR at least once during labor. Retrospectively, we assessed risk levels using our Fetal Reserve Index version 2 (FRI v2) scores that were calculated in 20 min timeframes. FRIv2 scores include various maternal, obstetric, and fetal risk factors, uterine contraction frequency, and FHR baseline rate, variability, accelerations, and decelerations. We define 3 IR scenarios to assess relative IR effectiveness. (1) No reduction in PIT infusion rates (PITSAME), (2) decreased PIT infusion rates (DPIT), or (3) PIT turned off (PIT OFF). Maternal repositioning and oxygen administration are nearly universal across all types and, therefore, are not considered in groupings. We then created two measures of IR effectiveness by classifying changes in FRI v2 scores over six 20 min windows coincident with and following IR use as (1) “Improvement” (improvement relative to the FRIv2 score at IR initiation) and (2) “Stabilization” (no further decrease in FRI score relative to the FRIv2 score in the sixth 20 min epoch after IR initiation). We evaluated the relative effectiveness of the three PIT options, and to test whether the level of fetal risk at the time of IR initiation affected its short-term effectiveness, FRI v2 risk scores were assigned to one of three groups (Green [1.00–0.625]; Yellow [0.50–0.25]; Red 0.25–0.0]). Higher scores indicate lower risk. Statistical analysis was performed with ANOVA and t- tests. Results: Overall, the first and/or the only initiation of IR resulted in improvement in 71% of cases and stabilization in 78% of cases. The remaining 22% were failures, meaning that the FRIv2 score in the 6th 20 min period was lower than the score at the time of initiation. There were modest, but not statistically significant, differences in effectiveness (improvement or stabilization) by type of IR. There was a trend toward lower IR effectiveness of PIT OFF during IR initiation when compared to PIT continuation or decreased groups. Conclusions: IR initiation or type did not vary significantly by retrospectively calculated levels of fetal risk, showing that wide variation in clinician practices, not necessarily correlated with what we believe actual risk was, determine how IR was used. The FRI provides contextualization of FHR elements by adding maternal, fetal, and obstetric risk factors, and increased uterine activity enables a more rigorous and reproducible approach to analysis of emerging fetal compromise and IR effectiveness. As practice has shifted from the over-aggressiveness of PIT use to now premature discontinuations with any tracing variation, we need better metrics. FRIv2 further improves its physiologic underpinnings. Thus, we propose a new approach to the overall assessment of IR practice.

Noninvasive Prenatal Testing (NIPT) and prenatal ultrasound can identify disorders of sexual differentiation (DSD), although discrepancies between genetic and phenotypic data can complicate diagnoses. This study explores phenotype-genotype discordance within the DSD context, focusing on methodological concerns and biological explanations. Advances in prenatal screening technologies, including cell-free DNA (cfDNA) testing and ultrasound examinations, have improved DSD detection rates. Analysis of a case featuring a 46, XY DSD due to an NR5A1 gene mutation illustrates the importance of integrating cfDNA testing with ultrasound, which enhances detection and early management. The findings underscore the necessity for a multidisciplinary approach in diagnosis and treatment planning, facilitating timely interventions that reduce psychological distress for families. The study recommends refining diagnostic algorithms that combine cfDNA testing and ultrasound to correlate genetic insights with clinical observations, thus improving patient outcomes through informed decision-making and comprehensive care strategies.

Introduction: The Affordable Care Act was intended principally to increase healthcare insurance coverage to uninsured Americans. Ostensibly, 40 million people obtained coverage. However, American maternal mortality has significantly worsened. The overall health curve in conservative leaning states is shifted downward in comparison to liberal ones including COVID mortality, maternal mortality, neonatal mortality, life expectancy, heart disease, pulmonary, and diabetic deaths, obesity, smoking, suicides, and alcohol related auto deaths. Here, we focus on insurance coverage issues related to those outcomes and market alterations produced by Affordable Care Act (ACA) legislation and regulatory actions that may or may not have had unintended consequences. Methods: Using authoritative national public databases, we analyzed 18 health status metrics in the context of Managed Care Organizations market consolidation and Accountable Care Organizations penetration fueled by the Medicaid expansion. We ranked states from best statistics to worst incorporating 10 measures of health access including: hospital beds, patients without examination for over a year, and incidence of maternity care deserts. Results: There is considerable variation in the USA for both healthcare status and access. Our data show these are highly correlated (r(2) = 0.47, p < 0.01). States with the best outcomes have the best access. States with highest healthcare metrics and healthcare access were all traditional liberal "blue states" with greater infrastructure and insurance coverage. Conclusions: Hospital and clinic realignments created under the ACA appear to have worsened the healthcare of women and children in the USA, particularly for patients of color. Healthcare status and access to services are highly correlated. States allocating more resources for healthcare have better outcomes. Monopoly exemptions under ACA are temporally and statistically correlated with worsening of maternal outcomes across all geographic regions where control and consolidation was permitted or encouraged. Maternity care deserts have significantly disadvantaged women of color and working-class families in rural and urban zones. Further exacerbating the problem is the reduction of physician independence both within and outside of hospital systems.

We are developing a non-invasive neonatal monitoring device to continue monitoring of the fetus during the first 30-60 minutes postpartum. We monitor critical physiological parameters such as oxygenation, heart rate, and skin pH. These are crucial for the early identification of potential health issues in newborns, including hypoxia and acidosis which are forerunners of neonatal encephalopathy and cerebral palsy. An ESP32 microcontroller and MAX30102 sensor are integrated within a wearable, sock form factor. We address limitations of traditional invasive neonatal monitors by providing continuous, real-time health assessment through seamless wireless connectivity, including Bluetooth compatibility. The project not only focuses on the technological development of the device but also emphasizes the importance of a non-invasive monitoring design for comfort and precise diagnostics for informed healthcare interventions. This project underscores the critical role of early detection and intervention for neonatal compromise, to potentially transform neonatal health monitoring and improve outcomes for newborn infants worldwide.

Inherent in healthcare policy/medical practice changes are 2 components: 1. Does it improve care, & 2. How much does it cost? Serious attention is finally being paid to medical ramifications of Social Determinants of Healthcare, racism, and resultant inequitable care. This study focuses on the second question: What are the short and long-term financial consequences (FC) of disparate care? Cesarean delivery rates (CDR) by race/ethnic group, have higher levels of morbidity and mortality and serve as a surrogate for quality of care. Using a GDP-based statistical dollar value metric of each life, we calculate long-term FC of disparate care. We compare representative states having generally perceived high and low spending on obstetrical care infrastructures and posed the following question: if CDR for Black patients were reduced to that of white patients, what FC would there be for short-term and long-term care. Long term costs are calculated as (additional hospital stay days X avg. cost per day) + (additional avg. recovery days X avg. daily GDP contribution). Most states show considerably higher CDR and morbidity/mortality for Black patients vs. white. Reducing Black CDR to white levels, short-term US savings would be $263M. Maternal and neonatal mortality rates (MMR and NMR) for Blacks are also higher [excess cost for Black MMR = $224M]. States with higher maternal and neonatal MR have significant impact on lifetime economic productivity. Nationally, the annual cost of excess MMR & NMR is $3.147 Billion from increased costs, reduced incomes, and tax revenues. In CA economic reductions are $471 per delivery; in GA it’s $1665. In addition to general societal deleterious effects of disparities, the perception of “saving” money by not spending for better care is wrong. Long term State FC show no short-term savings and increased long term expenses. Ignoring (momentarily) the commonly articulated quality of care reasons for fixing the provision of care, it costs the US $ 3.634 Billion per year that could be better allocated.

Georgia has a high rate of severe maternal morbidity and mortality when compared to the rest of the United States1–6. Evidence gained from the Georgia Maternal Mortality Review Committee identified areas of focus for high yield clinical initiatives for improvement in maternal health outcomes2,7,8. Cardiovascular disease, including cardiomyopathy, coronary conditions, and pre-eclampsia/eclampsia, is the most common cause of pregnancy-related death in non-Hispanic, Black women in Georgia9–11. Development of a Cardio-Obstetrics program is an initiative to advance health equity by decreasing cardiovascular morbidity and mortality. This report describes the following: (1) state-level advocacy for improving maternal health outcomes with funding gained through the legislative process and partnership with a governmental agency; (2) Cardio-Obstetrics program development based on evidence gained from the maternal mortality review process; and (3) implementation of a Cardio-Obstetrics service, beginning with a focused approach for capacity building and understanding barriers to care.

Early detection of intrapartum risks enables timely interventions to prevent or mitigate adverse labor outcomes such as cerebral palsy. However, accurate automated systems to support clinical decision-making during delivery are currently lacking. To address this gap, we propose Artificial Intelligence for Modeling and Explaining Neonatal Health (AIMEN), a deep learning framework that predicts adverse labor outcomes from maternal, fetal, obstetrical, and intrapartum factors while providing interpretable reasoning behind its predictions. AIMEN reveals how specific modifications to input variables could alter predicted outcomes, enhancing clinical insight. To address class imbalance and limited sample size, AIMEN employs Conditional Tabular GAN (CTGAN) for data augmentation. This process includes synthetic data generation, and we investigate in detail properties such as relaxing feature bounds for a subset of training points to explore slightly out-of-range physiological values, and applying silhouette-score-based filtering to increase the separability of synthetic samples. AIMEN uses an ensemble of fully connected neural networks for classification and outperforms state-of-the-art models such as XGBoost, TabNet, DANet, and LightGBM, achieving an average F1 score of 0.784 in predicting high-risk deliveries. Moreover, AIMEN generates counterfactual explanations that identify actionable changes involving only two to three attributes on average. Resources: https://github.com/ab9mamun/AIMEN.

The original goal of electronic fetal monitoring was to reduce stillbirths. It worked. Then the mission expanded to reducing neurologic impairment including cerebral palsy. Despite 50 years' experience, the data have been contradictory, and even the key opinion leaders of EFM admit it an only detect about half the problems. Concomitantly, the cesarean delivery rate which has greater complications and costs has increased about 6-fold. Here we review multiple generations of antenatal testing schemes having increasing sophistication but still not too much improvement in outcomes and our re-engineered approach to intrapartum fetal monitoring for which we morph from the subjective Category system which has poor statistical performance metrics to a new approach we call the "Fetal Reserve Index." The FRI breaks down the tracing into 4 quantifiable components (fetal heart rate, variability, accelerations, and decelerations) and then formally adds to the analysis the presence of increased uterine activity, and maternal, fetal, and obstetrical risk factors. In version 1.0, all parameters are weighted equally. We have shown improved and earlier identification of fetal risk earlier in the pathophysiology allowing less abrupt and dramatic interventions. We have further shown the early postpartum period to be one of commonly unrecognized risks, and we envision a continuum of assessment from antepartum through intrapartum and postpartum for optimal results.