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Vital signs monitoring is a fundamental component of ensuring the health and safety of women and newborns during pregnancy, labor, and childbirth. This monitoring is often the first step in early detection of pregnancy abnormalities, providing an opportunity for prompt, effective intervention to prevent maternal and neonatal morbidity and mortality. Contemporary pregnancy monitoring systems require numerous devices wired to large base units; at least five separate devices with distinct user interfaces are commonly used to detect uterine contractility, maternal blood oxygenation, temperature, heart rate, blood pressure, and fetal heart rate. Current monitoring technologies are expensive and complex with implementation challenges in low-resource settings where maternal morbidity and mortality is the greatest. We present an integrated monitoring platform leveraging advanced flexible electronics, wireless connectivity, and compatibility with a wide range of low-cost mobile devices. Three flexible, soft, and low-profile sensors offer comprehensive vital signs monitoring for both women and fetuses with time-synchronized operation, including advanced parameters such as continuous cuffless blood pressure, electrohysterography-derived uterine monitoring, and automated body position classification. Successful field trials of pregnant women between 25 and 41 wk of gestation in both high-resource settings (n = 91) and low-resource settings (n = 485) demonstrate the systems performance, usability, and safety.

Key Points: Question: Can novice clinicians accurately estimate gestational age using a low-cost, battery-powered ultrasonography probe with integrated artificial intelligence (AI) image interpretation? Findings: This prospective study enrolled 400 pregnant individuals with due dates confirmed by first-trimester ultrasonography. At follow-up visits randomly assigned throughout gestation, novice clinicians using an AI-enabled device estimated gestational age as accurately as credentialed sonographers using traditional ultrasonography devices (difference, 0.2 days). Meaning: Obstetrical care in low-resource settings may benefit from reliable gestational age assessment using AI integration with point-of-care ultrasonography. Importance: Accurate assessment of gestational age (GA) is essential to good pregnancy care but often requires ultrasonography, which may not be available in low-resource settings. This study developed a deep learning artificial intelligence (AI) model to estimate GA from blind ultrasonography sweeps and incorporated it into the software of a low-cost, battery-powered device. Objective: To evaluate GA estimation accuracy of an AI-enabled ultrasonography tool when used by novice users with no prior training in sonography. Design, Setting, and Participants: This prospective diagnostic accuracy study enrolled 400 individuals with viable, single, nonanomalous, first-trimester pregnancies in Lusaka, Zambia, and Chapel Hill, North Carolina. Credentialed sonographers established the "ground truth" GA via transvaginal crown-rump length measurement. At random follow-up visits throughout gestation, including a primary evaluation window from 14 0/7 weeks' to 27 6/7 weeks' gestation, novice users obtained blind sweeps of the maternal abdomen using the AI-enabled device (index test) and credentialed sonographers performed fetal biometry with a high-specification machine (study standard). Main Outcomes and Measures: The primary outcome was the mean absolute error (MAE) of the index test and study standard, which was calculated by comparing each method's estimate to the previously established GA and considered equivalent if the difference fell within a prespecified margin of ±2 days. Results: In the primary evaluation window, the AI-enabled device met criteria for equivalence to the study standard, with an MAE (SE) of 3.2 (0.1) days vs 3.0 (0.1) days (difference, 0.2 days [95% CI, −0.1 to 0.5]). Additionally, the percentage of assessments within 7 days of the ground truth GA was comparable (90.7% for the index test vs 92.5% for the study standard). Performance was consistent in prespecified subgroups, including the Zambia and North Carolina cohorts and those with high body mass index. Conclusions and Relevance: Between 14 and 27 weeks' gestation, novice users with no prior training in ultrasonography estimated GA as accurately with the low-cost, point-of-care AI tool as credentialed sonographers performing standard biometry on high-specification machines. These findings have immediate implications for obstetrical care in low-resource settings, advancing the World Health Organization goal of ultrasonography estimation of GA for all pregnant people. Trial Registration: ClinicalTrials.gov Identifier: NCT05433519 This prospective diagnostic study examines the accuracy of an AI-enabled ultrasonography tool in estimating gestational age when used by those with no prior training in sonography. [ABSTRACT FROM AUTHOR]

This cross-sectional study of 3212 pregnant women assessed the field performance, acceptability, and feasibility of two dual HIV/syphilis rapid diagnostic tests, the Chembio DPP HIV-syphilis Assay and the SD Bioline HIV/syphilis Duo in antenatal clinics. Sensitivity and specificity for HIV and syphilis were calculated compared to the rapid Determine HIV-1/2 with Uni-Gold to confirm positive results for HIV and the Treponema pallidum particle agglutination assay for syphilis. RPR titers ≥1:4 were used to define active syphilis detection. Acceptability and feasibility were assessed using self-reported questionnaires. For Chembio, the HIV sensitivity was 90.6% (95%CI = 87.4, 93.0) and specificity was 97.2% (95%CI = 96.2, 97.8); syphilis sensitivity was 68.6% (95%CI = 61.9, 74.6) and specificity was 98.5% (95%CI = 97.8, 98.9). For SD Bioline, HIV sensitivity was 89.4% (95%CI = 86.1, 92.0) and specificity was 96.3% (95%CI = 95.3, 97.1); syphilis sensitivity was 66.2% (95%CI = 59.4, 72.4) and specificity was 97.2% (95%CI = 96.4, 97.9). Using the reference for active syphilis, syphilis sensitivity was 84.7% (95%CI = 76.1, 90.6) for Chembio and 81.6% (95%CI = 72.7, 88.1) for SD Bioline. Both rapid diagnostic tests were assessed as highly acceptable and feasible. In a field setting, the performance of both rapid diagnostic tests was comparable to other published field evaluations and each was rated highly acceptable and feasible. These findings can be used to guide further research and proposed scale up in antenatal clinic settings. [ABSTRACT FROM AUTHOR]

Background: Well-functioning health systems need to utilize data at all levels, from the provider, to local and national-level decision makers, in order to make evidence-based and needed adjustments to improve the quality of care provided. Over the last 7 years, the Doris Duke Charitable Foundation's African Health Initiative funded health systems strengthening projects at the facility, district, and/or provincial level to improve population health. Increasing data-driven decision making was a common strategy in Mozambique, Rwanda and Zambia. This paper describes the similar and divergent approaches to increase data-driven quality of care improvements (QI) and implementation challenge and opportunities encountered in these three countries.Methods: Eight semi-structured in-depth interviews (IDIs) were administered to program staff working in each country. IDIs for this paper included principal investigators of each project, key program implementers (medically-trained support staff, data managers and statisticians, and country directors), as well as Ministry of Health counterparts. IDI data were collected through field notes; interviews were not audio recorded. Data were analyzed using thematic analysis but no systematic coding was conducted. IDIs were supplemented through donor report abstractions, a structured questionnaire, one-on-one phone calls, and email exchanges with country program leaders to clarify and expand on key themes emerging from IDIs.Results: Project successes ranged from over 450 collaborative action-plans developed, implemented, and evaluated in Mozambique, to an increase from <10% to >80% of basic clinical protocols followed in intervention facilities in rural Zambia, and a shift from a lack of awareness of health data among health system staff to collaborative ownership of data and using data to drive change in Rwanda.Conclusion: Based on common successes across the country experiences, we recommend future data-driven QI interventions begin with data quality assessments to promote that rapid health system improvement is possible, ensure confidence in available data, serve as the first step in data-driven targeted improvements, and improve staff data analysis and visualization skills. Explicit Ministry of Health collaborative engagement can ensure performance review is collaborative and internally-driven rather than viewed as an external "audit." [ABSTRACT FROM AUTHOR]

Background: Mobile health (m-health) utilizes widespread access to mobile phone technologies to expand health services. Community health workers (CHWs) provide first-level contact with health facilities; combining CHW efforts with m-health may be an avenue for improving primary care services. As part of a primary care improvement project, a pilot CHW program was developed using a mobile phone-based application for outreach, referral, and follow-up between the clinic and community in rural Zambia., Materials and Methods: The program was implemented at six primary care sites. Computers were installed at clinics for data entry, and data were transmitted to central servers. In the field, using a mobile phone to send data and receive follow-up requests, CHWs conducted household health surveillance visits, referred individuals to clinic, and followed up clinic patients., Results: From January to April 2011, 24 CHWs surveyed 6,197 households with 33,304 inhabitants. Of 15,539 clinic visits, 1,173 (8%) had a follow-up visit indicated and transmitted via a mobile phone to designated CHWs. CHWs performed one or more follow-ups on 74% (n=871) of active requests and obtained outcomes on 63% (n=741). From all community visits combined, CHWs referred 840 individuals to a clinic., Conclusions: CHWs completed all planned aspects of surveillance and outreach, demonstrating feasibility. Components of this pilot project may aid clinical care in rural settings and have potential for epidemiologic and health system applications. Thus, m-health has the potential to improve service outreach, guide activities, and facilitate data collection in Zambia.