An mHealth monitoring system for traditional birth attendant-led antenatal risk assessment in rural Guatemala

Automated image transcription for perinatal blood pressure monitoring using mobile health technology

This paper introduces a novel approach to address the challenges associated with transferring blood pressure (BP) data obtained from oscillometric devices used in self-measured BP monitoring systems to integrate this data into medical health records or a proxy database accessible by clinicians, particularly in low literacy populations. To this end, we developed an automated image transcription technique to effectively transcribe readings from BP devices, ultimately enhancing the accessibility and usability of BP data for monitoring and managing BP during pregnancy and the postpartum period, particularly in low-resource settings and low-literate populations. In the designed study, the photos of the BP devices were captured as part of perinatal mobile health (mHealth) monitoring programs, conducted in four studies across two countries. The Guatemala Set 1 and Guatemala Set 2 datasets include the data captured by a cohort of 49 lay midwives from 1697 and 584 pregnant women carrying singletons in the second and third trimesters in rural Guatemala during routine screening. Additionally, we designed an mHealth system in Georgia for postpartum women to monitor and report their BP at home with 23 and 49 African American participants contributing to the Georgia I3 and Georgia IMPROVE projects, respectively. We developed a deep learning-based model which operates in two steps: LCD localization using the You Only Look Once (YOLO) object detection model and digit recognition using a convolutional neural network-based model capable of recognizing multiple digits. We applied color correction and thresholding techniques to minimize the impact of reflection and artifacts. Three experiments were conducted based on the devices used for training the digit recognition model. Overall, our results demonstrate that the device-specific model with transfer learning and the device independent model outperformed the device-specific model without transfer learning. The mean absolute error (MAE) of image transcription on held-out test datasets using the device-independent digit recognition were 1.2 and 0.8 mmHg for systolic and diastolic BP in the Georgia IMPROVE and 0.9 and 0.5 mmHg in Guatemala Set 2 datasets. The MAE, far below the FDA recommendation of 5 mmHg, makes the proposed automatic image transcription model suitable for general use when used with appropriate low-error BP devices.

Mobil Monitoring Doppler Ultrasound (MoMDUS) study: protocol for a prospective, observational study investigating the use of artificial intelligence and low-cost Doppler ultrasound for the automated quantification of hypertension, pre-eclampsia and fetal growth restriction in rural Guatemala

INTRODUCTION

Undetected high-risk conditions in pregnancy are a leading cause of perinatal mortality in low-income and middle-income countries. A key contributor to adverse perinatal outcomes in these settings is limited access to high-quality screening and timely referral to care. Recently, a low-cost one-dimensional Doppler ultrasound (1-D DUS) device was developed that front-line workers in rural Guatemala used to collect quality maternal and fetal data. Further, we demonstrated with retrospective preliminary data that 1-D DUS signal could be processed using artificial intelligence and deep-learning algorithms to accurately estimate fetal gestational age, intrauterine growth and maternal blood pressure. This protocol describes a prospective observational pregnancy cohort study designed to prospectively evaluate these preliminary findings.

METHODS AND ANALYSIS

This is a prospective observational cohort study conducted in rural Guatemala. In this study, we will follow pregnant women (N =700) recruited prior to 18 6/7 weeks gestation until their delivery and early postpartum period. During pregnancy, trained nurses will collect data on prenatal risk factors and obstetrical care. Every 4 weeks, the research team will collect maternal weight, blood pressure and 1-D DUS recordings of fetal heart tones. Additionally, we will conduct three serial obstetric ultrasounds to evaluate for fetal growth restriction (FGR), and one postpartum visit to record maternal blood pressure and neonatal weight and length. We will compare the test characteristics (receiver operator curves) of 1-D DUS algorithms developed by deep-learning methods to two-dimensional fetal ultrasound survey and published clinical pre-eclampsia risk prediction algorithms for predicting FGR and pre-eclampsia, respectively.

ETHICS AND DISSEMINATION

Results of this study will be disseminated at scientific conferences and through peer-reviewed articles. Deidentified data sets will be made available through public repositories. The study has been approved by the institutional ethics committees of Maya Health Alliance and Emory University.

The Impact of the Use of e-Partogram on Maternal and Perinatal Outcomes: A Scoping Review

To overcome shortcomings of the paper partograph, enhance care during labor and delivery, improve record keeping, and help decision-making, several countries have focused on adopting low-cost digital applications. This scoping review highlights the usability and current status of the digital partogram in obstetric care. We conducted a thorough search involving the databases ScienceDirect, PubMed, and Google Scholar for relevant studies from inception till September 2023 by using the keywords "partograph", "electronic", and "obstetric" as well as the Boolean operators "AND" and "OR". Based on the selection criteria, 25 studies exploring the application of electronic partographs (e-partographs) in obstetric care were included in the review. The majority of the studies examined the efficiency and reported the effectiveness of e-partographs in comparison to paper partographs. The e-partograph has also demonstrated a clear benefit in that the healthcare providers filled out the data, and a reminder mechanism was placed, which might help determine whether the labor process was normal or needed more care. Moreover, an e-partograph was simple to adopt and use for obstetric caregivers and had the potential to save time. To sum up, digital partograph produces superior results to paper partograph. The use of an e-partograph can keep deliveries on track while lowering the need for cesarean sections and prolonged labor. The e-partograph provides essential benefits to its users and also provides a warning system with audible and visual cues that might be utilized to detect difficulties during delivery.

Hierarchical Attentive Network for Gestational Age Estimation in Low-Resource Settings

Assessing fetal development is essential to the provision of healthcare for both mothers and fetuses. In low- and middle-income countries, conditions that increase the risk of fetal growth restriction (FGR) are often more prevalent. In these regions, barriers to accessing healthcare and social services exacerbate fetal maternal health problems. One of these barriers is the lack of affordable diagnostic technologies. To address this issue, this work introduces an end-to-end algorithm applied to a low-cost, hand-held Doppler ultrasound device for estimating gestational age (GA), and by inference, FGR. The Doppler ultrasound signals used in this study were collected from 226 pregnancies (45 low birth weight at delivery) between 5 and 9 months GA by lay midwives in highland Guatemala. We designed a hierarchical deep sequence learning model with an attention mechanism to learn the normative dynamics of fetal cardiac activity in different stages of development. This resulted in a state-of-the-art GA estimation performance, with an average error of 0.79 months. This is close to the theoretical minimum for the given quantization level of one month. The model was then tested on Doppler recordings of the fetuses with low birth weight and the estimated GA was shown to be lower than the GA calculated from last menstruation. Thus, this could be interpreted as a potential sign of developmental retardation (or FGR) associated with low birth weight, and referral and intervention may be necessary.

Multi-omics and machine learning for the prevention and management of female reproductive health

Females typically carry most of the burden of reproduction in mammals. In humans, this burden is exacerbated further, as the evolutionary advantage of a large and complex human brain came at a great cost of women's reproductive health. Pregnancy thus became a highly demanding phase in a woman's life cycle both physically and emotionally and therefore needs monitoring to assure an optimal outcome. Moreover, an increasing societal trend towards reproductive complications partly due to the increasing maternal age and global obesity pandemic demands closer monitoring of female reproductive health. This review first provides an overview of female reproductive biology and further explores utilization of large-scale data analysis and -omics techniques (genomics, transcriptomics, proteomics, and metabolomics) towards diagnosis, prognosis, and management of female reproductive disorders. In addition, we explore machine learning approaches for predictive models towards prevention and management. Furthermore, mobile apps and wearable devices provide a promise of continuous monitoring of health. These complementary technologies can be combined towards monitoring female (fertility-related) health and detection of any early complications to provide intervention solutions. In summary, technological advances (e.g., omics and wearables) have shown a promise towards diagnosis, prognosis, and management of female reproductive disorders. Systematic integration of these technologies is needed urgently in female reproductive healthcare to be further implemented in the national healthcare systems for societal benefit.

Use of mHealth tools to register birth outcomes in low-income and middle-income countries: a scoping review

OBJECTIVE

Accurate reporting of birth outcomes in low-income and middle-income countries (LMICs) is essential. Mobile health (mHealth) tools have been proposed as a replacement for conventional paper-based registers. mHealth could provide timely data for individual facilities and health departments, as well as capture deliveries outside facilities. This scoping review evaluates which mHealth tools have been reported to birth outcomes in the delivering room in LMICs and documents their reported advantages and drawbacks.

DESIGN

A scoping review following Preferred Reporting Items for Systematic Reviews and Meta-Analyses and Joanna Briggs Institute guidelines for scoping reviews and the mHealth evidence reporting and assessment checklist for evaluating mHealth interventions.

DATA SOURCES

PubMed, CINAHL and Global Health were searched for records until 3 February 2022 with no earliest date limit.

ELIGIBILITY CRITERIA

Studies were included where healthcare workers used mHealth tools in LMICs to record birth outcomes. Exclusion criteria included mHealth not being used at the point of delivery, non-peer reviewed literature and studies not written in English.

DATA EXTRACTION AND SYNTHESIS

Two independent reviewers screened studies and extracted data. Common themes among studies were identified.

RESULTS

640 records were screened, 21 of which met the inclusion criteria, describing 15 different mHealth tools. We identified six themes: (1) digital tools for labour monitoring (8 studies); (2) digital data collection of specific birth outcomes (3 studies); (3) digital technologies used in community settings (6 studies); (4) attitudes of healthcare workers (10 studies); (5) paper versus electronic data collection (3 studies) and (6) infrastructure, interoperability and sustainability (8 studies).

CONCLUSION

Several mHealth technologies are reported to have the capability to record birth outcomes at delivery, but none were identified that were designed solely for that purpose. Use of digital delivery registers appears feasible and acceptable to healthcare workers, but definitive evaluations are lacking. Further assessment of the sustainability of technologies and their ability to integrate with existing health information systems is needed.

eHealth: A Survey of Architectures, Developments in mHealth, Security Concerns and Solutions

The ramifications of the COVID-19 pandemic have contributed in part to a recent upsurge in the study and development of eHealth systems. Although it is almost impossible to cover all aspects of eHealth in a single discussion, three critical areas have gained traction. These include the need for acceptable eHealth architectures, the development of mobile health (mHealth) technologies, and the need to address eHealth system security concerns. Existing survey articles lack a synthesis of the most recent advancements in the development of architectures, mHealth solutions, and innovative security measures, which are essential components of effective eHealth systems. Consequently, the present article aims at providing an encompassing survey of these three aspects towards the development of successful and efficient eHealth systems. Firstly, we discuss the most recent innovations in eHealth architectures, such as blockchain-, Internet of Things (IoT)-, and cloud-based architectures, focusing on their respective benefits and drawbacks while also providing an overview of how they might be implemented and used. Concerning mHealth and security, we focus on key developments in both areas while discussing other critical topics of importance for eHealth systems. We close with a discussion of the important research challenges and potential future directions as they pertain to architecture, mHealth, and security concerns. This survey gives a comprehensive overview, including the merits and limitations of several possible technologies for the development of eHealth systems. This endeavor offers researchers and developers a quick snapshot of the information necessary during the design and decision-making phases of the eHealth system development lifecycle. Furthermore, we conclude that building a unified architecture for eHealth systems would require combining several existing designs. It also points out that there are still a number of problems to be solved, so more research and investment are needed to develop and deploy functional eHealth systems.

Indigenous Women's Perspectives on Contraception in Rural Guatemala

In rural, Indigenous Guatemala, women's healthcare is fragmented and inadequate. Our interdisciplinary, multinational research team aimed to 1) describe reproductive health in one rural Indigenous community; 2) explore contraceptive use; and 3) learn about and prioritize Indigenous Maya women's reproductive health beliefs and needs. Our study team conducted mixed-methods surveys with 62 women, led focus groups with 20 community health workers, and analyzed data using concurrent mixed methods analysis. We found that 51% of women surveyed reported current family planning, with 33% using a biomedical method. We found high mean fertility, 6.9 live births per woman aged 40-49 (national average 4.7), with significant socioeconomic variation. We also found that poverty correlated with total fertility, while education inversely correlated. Our research found that contraceptive use had a strong association with access to healthcare and with women's reported sexual autonomy (which we instrumentalized based on women's answers to the question "can you refuse to have sex with your husband?"). Many women we spoke to feared contraception, specifically concerned it could cause cancer. Overall, Guatemalan Indigenous women expressed unease seeking reproductive healthcare within health systems that have historically and currently excluded and mistreated Indigenous communities. Our research documented unexplored influences on contraceptive use, including the relationship between sexual autonomy and contraception and widespread concern of cancer with contraceptive use. We conclude, moving forward, that we and other researchers should continue to collaborate with communities to improve Indigenous women's reproductive healthcare.

Why women choose to to seek facility-level obstetrical care in rural Guatemala: A qualitative study

OBJECTIVE

The majority of indigenous Guatemalan women give birth at home with traditional birth attendants (TBAs), and maternal mortality rates are high (Ministerio de Salud, 2017). Our objective was to better understand decision-making around whether to remain in the home or to seek facility-level care for obstetric complications.

METHODS

This study was a qualitative analysis using semi-structured interviews in a Maya population in the Western Highlands of Guatemala who received prenatal care between April 2017 and December 2018. We used qualitative interviews with women who were identified as medically high-risk and needing facility-level care, offered assistance with acquiring such care, and yet declined this option. Women interviewed were connected to a primary care organization called Maya Health Alliance, through care with TBAs involved in a program utilizing a smartphone-based decision support application to identify maternal and neonatal complications of pregnancy. Interviews were analyzed using Dedoose (www.dedoose.com). Deductive and inductive analysis was performed.

RESULTS

Barriers to care included a disagreement between the respondent and TBA about indications for facility care, fear of hospital care, concerns about the quality of hospital care, logistical obstacles, and lack of control; and they were more often described by respondents who had previous healthcare experiences. Therapeutic misalignment occurred more with conditions perceived to be less severe. Participants described a balancing of fears and apprehensions against concerns of low quality and disrespectful maternity care, and in the setting of emergent conditions, disregarded barriers that were often described as inhibiting non-urgent obstetric care.

CONCLUSIONS

The decision to engage in medical care in this population of Maya women involves a weighing of the perception of seriousness of the medical complication against fears of facility level care and concerns of a poor quality of care.

CNN-Based LCD Transcription of Blood Pressure From a Mobile Phone Camera

Routine blood pressure (BP) measurement in pregnancy is commonly performed using automated oscillometric devices. Since no wireless oscillometric BP device has been validated in preeclamptic populations, a simple approach for capturing readings from such devices is needed, especially in low-resource settings where transmission of BP data from the field to central locations is an important mechanism for triage. To this end, a total of 8192 BP readings were captured from the Liquid Crystal Display (LCD) screen of a standard Omron M7 self-inflating BP cuff using a cellphone camera. A cohort of 49 lay midwives captured these data from 1697 pregnant women carrying singletons between 6 weeks and 40 weeks gestational age in rural Guatemala during routine screening. Images exhibited a wide variability in their appearance due to variations in orientation and parallax; environmental factors such as lighting, shadows; and image acquisition factors such as motion blur and problems with focus. Images were independently labeled for readability and quality by three annotators (BP range: 34-203 mm Hg) and disagreements were resolved. Methods to preprocess and automatically segment the LCD images into diastolic BP, systolic BP and heart rate using a contour-based technique were developed. A deep convolutional neural network was then trained to convert the LCD images into numerical values using a multi-digit recognition approach. On readable low- and high-quality images, this proposed approach achieved a 91% classification accuracy and mean absolute error of 3.19 mm Hg for systolic BP and 91% accuracy and mean absolute error of 0.94 mm Hg for diastolic BP. These error values are within the FDA guidelines for BP monitoring when poor quality images are excluded. The performance of the proposed approach was shown to be greatly superior to state-of-the-art open-source tools (Tesseract and the Google Vision API). The algorithm was developed such that it could be deployed on a phone and work without connectivity to a network.

Towards deep phenotyping pregnancy: a systematic review on artificial intelligence and machine learning methods to improve pregnancy outcomes

Objective

Development of novel informatics methods focused on improving pregnancy outcomes remains an active area of research. The purpose of this study is to systematically review the ways that artificial intelligence (AI) and machine learning (ML), including deep learning (DL), methodologies can inform patient care during pregnancy and improve outcomes.

Materials and methods

We searched English articles on EMBASE, PubMed and SCOPUS. Search terms included ML, AI, pregnancy and informatics. We included research articles and book chapters, excluding conference papers, editorials and notes.

Results

We identified 127 distinct studies from our queries that were relevant to our topic and included in the review. We found that supervised learning methods were more popular (n = 69) than unsupervised methods (n = 9). Popular methods included support vector machines (n = 30), artificial neural networks (n = 22), regression analysis (n = 17) and random forests (n = 16). Methods such as DL are beginning to gain traction (n = 13). Common areas within the pregnancy domain where AI and ML methods were used the most include prenatal care (e.g. fetal anomalies, placental functioning) (n = 73); perinatal care, birth and delivery (n = 20); and preterm birth (n = 13). Efforts to translate AI into clinical care include clinical decision support systems (n = 24) and mobile health applications (n = 9).

Conclusions

Overall, we found that ML and AI methods are being employed to optimize pregnancy outcomes, including modern DL methods (n = 13). Future research should focus on less-studied pregnancy domain areas, including postnatal and postpartum care (n = 2). Also, more work on clinical adoption of AI methods and the ethical implications of such adoption is needed.

A review of fetal cardiac monitoring, with a focus on low- and middle-income countries

There is limited evidence regarding the utility of fetal monitoring during pregnancy, particularly during labor and delivery. Developed countries rely on consensus 'best practices' of obstetrics and gynecology professional societies to guide their protocols and policies. Protocols are often driven by the desire to be as safe as possible and avoid litigation, regardless of the cost of downstream treatment. In high-resource settings, there may be a justification for this approach. In low-resource settings, in particular, interventions can be costly and lead to adverse outcomes in subsequent pregnancies. Therefore, it is essential to consider the evidence and cost of different fetal monitoring approaches, particularly in the context of treatment and care in low-to-middle income countries. This article reviews the standard methods used for fetal monitoring, with particular emphasis on fetal cardiac assessment, which is a reliable indicator of fetal well-being. An overview of fetal monitoring practices in low-to-middle income counties, including perinatal care access challenges, is also presented. Finally, an overview of how mobile technology may help reduce barriers to perinatal care access in low-resource settings is provided.

Leveraging Technology to Improve Diabetes Care in Pregnancy

Pregnant women with diabetes are at higher risk of adverse outcomes. Prevention of such outcomes depends on strict glycemic control, which is difficult to achieve and maintain. A variety of technologies exist to aid in diabetes management for nonpregnant patients. However, adapting such tools to meet the demands of pregnancy presents multiple challenges. This article reviews the key attributes digital technologies must offer to best support diabetes management during pregnancy, as well as some digital tools developed specifically to meet this need. Despite the opportunities digital health tools present to improve the care of people with diabetes, in the absence of robust data and large research studies, the ability to apply such technologies to diabetes in pregnancy will remain imperfect.

A Proxy for Detecting IUGR Based on Gestational Age Estimation in a Guatemalan Rural Population

In-utero progress of fetal development is normally assessed through manual measurements taken from ultrasound images, requiring relatively expensive equipment and well-trained personnel. Such monitoring is therefore unavailable in low- and middle-income countries (LMICs), where most of the perinatal mortality and morbidity exists. The work presented here attempts to identify a proxy for IUGR, which is a significant contributor to perinatal death in LMICs, by determining gestational age (GA) from data derived from simple-to-use, low-cost one-dimensional Doppler ultrasound (1D-DUS) and blood pressure devices. A total of 114 paired 1D-DUS recordings and maternal blood pressure recordings were selected, based on previously described signal quality measures. The average length of 1D-DUS recording was 10.43 ± 1.41 min. The min/median/max systolic and diastolic maternal blood pressures were 79/102/121 and 50.5/63.5/78.5 mmHg, respectively. GA was estimated using features derived from the 1D-DUS and maternal blood pressure using a support vector regression (SVR) approach and GA based on the last menstrual period as a reference target. A total of 50 trials of 5-fold cross-validation were performed for feature selection. The final SVR model was retrained on the training data and then tested on a held-out set comprising 28 normal weight and 25 low birth weight (LBW) newborns. The mean absolute GA error with respect to the last menstrual period was found to be 0.72 and 1.01 months for the normal and LBW newborns, respectively. The mean error in the GA estimate was shown to be negatively correlated with the birth weight. Thus, if the estimated GA is lower than the (remembered) GA calculated from last menstruation, then this could be interpreted as a potential sign of IUGR associated with LBW, and referral and intervention may be necessary. The assessment system may, therefore, have an immediate impact if coupled with suitable intervention, such as nutritional supplementation. However, a prospective clinical trial is required to show the efficacy of such a metric in the detection of IUGR and the impact of the intervention.

Estimating birth weight from observed postnatal weights in a Guatemalan highland community

OBJECTIVE

Low birth weight is one of the leading contributors to global perinatal deaths. Detecting this problem close to birth enables the initiation of early intervention, thus reducing the long-term impact on the fetus. However, in low-and middle-income countries, sometimes newborns are weighted days or months after birth, thus challenging the identification of low birth weight. This study aims to estimate birth weight from observed postnatal weights recorded in a Guatemalan highland community.

APPROACH

With 918 newborns recorded in postpartum visits at a Guatemalan highland community, we fitted traditional infant weight models (Count's and Reeds models). The model that fitted the observed data best was selected based on typical newborn weight patterns reported in the medical literature and previous longitudinal studies. Then, estimated birth weights were determined using the weight gain percentage derived from the fitted weight curve.

MAIN RESULTS

The best model for both genders was the Reeds2 model, with a mean square error of 0.30 kg2 and 0.23 kg2 for male and female newborns, respectively. The fitted weight curves exhibited similar behavior to those reported in the literature, with a maximum weight loss around three to five days after birth, and birth weight recovery, on average, by day ten. Moreover, the estimated birth weight was consistent with the 2015 Guatemalan National Survey, no having a statistically significant difference between the estimated birth weight and the reported survey birth weights (two-sided Wilcoxon rank-sum test; [Formula: see text]).

SIGNIFICANCE

By estimating birth weight at an opportune time, several days after birth, it may be possible to identify low birth weight more accurately, thus providing timely treatment when is required.

Working with lay midwives to improve the detection of neonatal complications in rural Guatemala

Background

Globally most neonatal deaths occur within the first week of life and in low-income and middle-income countries. Strengthening health system linkages for frontline providers—such as lay midwives providing home-based obstetrical care—may improve neonatal outcomes in these settings. Here, we conducted a quality improvement study to increase the detection of neonatal complications by lay midwives in rural Guatemala, thereby increasing referrals to a higher level of care.

Methods

A quality improvement team in Guatemala reviewed drivers of neonatal health services provided by lay midwives. Improvement interventions included training on neonatal warning signs, optimised mobile health technology to standardise assessments and financial incentives for providers. The primary quality outcome was the rate of neonatal referral to a higher level of care.

Results

From September 2017 to September 2018, participating midwives attended 869 home deliveries and referred 80 neonates to a higher level of care. A proportion control chart, using the preintervention period from January to September 2017 as the baseline, showed an increase in the referral rate of all births from 1.5% to 9.9%. Special cause was obtained in January 2018 and sustained except for May 2018. The proportion of neonates receiving assessments by midwives in the first week of life increased to >90%. A trend toward an increasing number of days between neonatal deaths did not attain special cause.

Conclusions

Structured improvement interventions, including mobile health decision support and financial incentives, significantly increased the detection of neonatal complications and referral of neonates to higher levels of care by lay midwives operating in rural home-based settings in Guatemala. The results show the value of improving the integration of lay midwives and other first responders into neonatal systems of care in low-resource settings.

Mobile phone apps for clinical decision support in pregnancy: a scoping review

Background

The use of digital technology in healthcare has been found to be useful for data collection, provision of health information and communications. Despite increasing use of medical mobile phone applications (apps), by both clinicians and patients, there appears to be a paucity of peer-reviewed publications evaluating their use, particularly in pregnancy. This scoping review explored the use of mobile phone apps for clinical decision support in pregnancy. Specific objectives were to: 1. determine the current landscape of mobile phone app use for clinical decision support in pregnancy; 2. identify perceived benefits and potential hazards of use and 3. identify facilitators and barriers to implementation of these apps into clinical practice.

Methods

Papers eligible for inclusion were primary research or reports on the development and evaluation of apps for use by clinicians for decision support in pregnancy, published in peer-reviewed journals. Research databases included Medline, Embase, PsychoInfo, the Cochrane Database of Systematic Reviews and the online digital health journals JMIR mHealth and uHealth. Charting and thematic analysis was undertaken using NVivo qualitative data management software and the Framework approach.

Results

After screening for eligibility, 13 papers were identified, mainly reporting early stage development of the mobile app, and feasibility or acceptability studies designed to inform further development. Thematic analysis revealed four main themes across the included papers: 1. acceptability and satisfaction; 2. ease of use and portability; 3. multi-functionality and 4. the importance of user involvement in development and evaluation.

Conclusions

This review highlights the benefits of mobile apps for clinical decision support in pregnancy and potential barriers to implementation, but reveals a lack of rigorous reporting of evaluation of their use and data security. This situation may change, however, following the issue of FDA and MHRA guidelines and implementation of UK government and other international strategies. Overall, the findings suggest that ease of use, portability and multi-functionality make mobile apps for clinical decision support in pregnancy useful and acceptable tools for clinicians.

Obstetric care navigation: results of a quality improvement project to provide accompaniment to women for facility-based maternity care in rural Guatemala

BACKGROUND

Many maternal and perinatal deaths in low-resource settings are preventable. Inadequate access to timely, quality care in maternity facilities drives poor outcomes, especially where women deliver at home with traditional birth attendants (TBA). Yet few solutions exist to support TBA-initiated referrals or address reasons patients frequently refuse facility care, such as disrespectful and abusive treatment. We hypothesised that deploying accompaniers-obstetric care navigators (OCN)-trained to provide integrated patient support would facilitate referrals from TBAs to public hospitals.

METHODS

This project built on an existing collaboration with 41 TBAs who serve indigenous Maya villages in Guatemala's Western Highlands, which provided baseline data for comparison. When TBAs detected pregnancy complications, families were offered OCN referral support. Implementation was guided by bimonthly meetings of the interdisciplinary quality improvement team where the OCN role was iteratively tailored. The primary process outcomes were referral volume, proportion of births receiving facility referral, and referral success rate, which were analysed using statistical process control methods.

RESULTS

Over the 12-month pilot, TBAs attended 847 births. The median referral volume rose from 14 to 27.5, meeting criteria for special cause variation, without a decline in success rate. The proportion of births receiving facility-level care increased from 24±6% to 62±20% after OCN implementation. Hypertensive disorders of pregnancy and prolonged labour were the most common referral indications. The OCN role evolved to include a number of tasks, such as expediting emergency transportation and providing doula-like labour support.

CONCLUSIONS

OCN accompaniment increased the proportion of births under TBA care that received facility-level obstetric care. Results from this of obstetric care navigation suggest it is a feasible, patient-centred intervention to improve maternity care.

mHealth and big-data integration: promises for healthcare system in India

BACKGROUND

The use of mobile devices in health (mobile health/mHealth) coupled with related technologies promises to transform global health delivery by creating new delivery models that can be integrated with existing health services. These delivery models could facilitate healthcare delivery into rural areas where there is limited access to high-quality access care. Mobile technologies, Internet of Things and 5G connectivity may hold the key to supporting increased velocity, variety and volume of healthcare data.

OBJECTIVE

The purpose of this study is to identify and analyse challenges related to the current status of India's healthcare system-with a specific focus on mHealth and big-data analytics technologies. To address these challenges, a framework is proposed for integrating the generated mHealth big-data and applying the results in India's healthcare.

METHOD

A critical review was conducted using electronic sources between December 2018 and February 2019, limited to English language articles and reports published from 2010 onwards.

MAIN OUTCOME

This paper describes trending relationships in mHealth with big-data as well as the accessibility of national opportunities when specific barriers and constraints are overcome. The paper concentrates on the healthcare delivery problems faced by rural and low-income communities in India to illustrate more general aspects and identify key issues. A model is proposed that utilises generated data from mHealth devices for big-data analysis that could result in providing insights into the India population health status. The insights could be important for public health planning by the government towards reaching the Universal Health Coverage.

CONCLUSION

Biomedical, behavioural and lifestyle data from individuals may enable customised and improved healthcare services to be delivered. The analysis of data from mHealth devices can reveal new knowledge to effectively and efficiently support national healthcare demands in less developed nations, without fully accessible healthcare systems.

An open source autocorrelation-based method for fetal heart rate estimation from one-dimensional Doppler ultrasound

OBJECTIVE

Open research on fetal heart rate (FHR) estimation is relatively rare, and evidence for the utility of metrics derived from Doppler ultrasound devices has historically remained hidden in the proprietary documentation of commercial entities, thereby inhibiting its assessment and improvement. Nevertheless, recent studies have attempted to improve FHR estimation; however, these methods were developed and tested using datasets composed of few subjects and are therefore unlikely to be generalizable on a population level. The work presented here introduces a reproducible and generalizable autocorrelation (AC)-based method for FHR estimation from one-dimensional Doppler ultrasound (1D-DUS) signals.

APPROACH

Simultaneous fetal electrocardiogram (fECG) and 1D-DUS signals generated by a hand-held Doppler transducer in a fixed position were captured by trained healthcare workers in a European hospital. The fECG QRS complexes were identified using a previously published fECG extraction algorithm and were then over-read to ensure accuracy. An AC-based method to estimate FHR was then developed on this data, using a total of 721 1D-DUS segments, each 3.75 s long, and parameters were tuned with Bayesian optimization. The trained FHR estimator was tested on two additional (independent) hand-annotated Doppler-only datasets recorded with the same device but on different populations: one composed of 3938 segments (from 99 fetuses) acquired in rural Guatemala, and another composed of 894 segments (from 17 fetuses) recorded in a hospital in the UK.

MAIN RESULTS

The proposed AC-based method was able to estimate FHR within 10% of the reference FHR values 96% of the time, with an accuracy of 97% for manually identified good quality segments in both of the independent test sets.

SIGNIFICANCE

This is the first work to publish open source code for FHR estimation from 1D-DUS data. The method was shown to satisfy estimations within 10% of the reference FHR values and it therefore defines a minimum accuracy for the field to match or surpass. Our work establishes a basis from which future methods can be developed to more accurately estimate FHR variability for assessing fetal wellbeing from 1D-DUS signals.

The Promise of Mobile Technologies for the Health Care System in the Developing World: A Systematic Review

Evolution of mobile technologies and their rapid penetration into people's daily lives, especially in the developing countries, have highlighted mobile health, or m-health, as a promising solution to improve health outcomes. Several studies have been conducted that characterize the impact of m-health solutions in resource-limited settings and assess their potential to improve health care. The aim of this review is twofold: 1) to present an overview of the background and significance of m-health and 2) to summarize and discuss the existing evidence for the effectiveness of m-health in the developing world. A systematic search in the literature was performed in Pubmed, Scopus, as well as reference lists, and a broad sample of 98 relevant articles was identified, which were then categorized into five wider m-health categories. Although statistically significant conclusions cannot be drawn since the majority of studies relied on small-scale trials and limited assessment of long-term effects, this review provides a systematic and extensive analysis of the advantages, disadvantages, and challenges of m-health in developing countries in an attempt to determine future research directions of m-health interventions.

Cardiotocography and beyond: a review of one-dimensional Doppler ultrasound application in fetal monitoring

One-dimensional Doppler ultrasound (1D-DUS) provides a low-cost and simple method for acquiring a rich signal for use in cardiovascular screening. However, despite the use of 1D-DUS in cardiotocography (CTG) for decades, there are still challenges that limit the effectiveness of its users in reducing fetal and neonatal morbidities and mortalities. This is partly due to the noisy, transient, complex and nonstationary nature of the 1D-DUS signals. Current challenges also include lack of efficient signal quality metrics, insufficient signal processing techniques for extraction of fetal heart rate and other vital parameters with adequate temporal resolution, and lack of appropriate clinical decision support for CTG and Doppler interpretation. Moreover, the almost complete lack of open research in both hardware and software in this field, as well as commercial pressures to market the much more expensive and difficult to use Doppler imaging devices, has hampered innovation. This paper reviews the basics of fetal cardiac function, 1D-DUS signal generation and processing, its application in fetal monitoring and assessment of fetal development and wellbeing. It also provides recommendations for future development of signal processing and modeling approaches, to improve the application of 1D-DUS in fetal monitoring, as well as the need for annotated open databases.

mHealth intervention to improve the continuum of maternal and perinatal care in rural Guatemala: a pragmatic, randomized controlled feasibility trial

Background/objective

Guatemala’s indigenous Maya population has one of the highest perinatal and maternal mortality rates in Latin America. In this population most births are delivered at home by traditional birth attendants (TBAs), who have limited support and linkages to public hospitals. The goal of this study was to characterize the detection of maternal and perinatal complications and rates of facility-level referral by TBAs, and to evaluate the impact of a mHealth decision support system on these rates.

Methods

A pragmatic one-year feasibility trial of an mHealth decisions support system was conducted in rural Maya communities in collaboration with TBAs. TBAs were individually randomized in an unblinded fashion to either early-access or later-access to the mHealth system. TBAs in the early-access arm used the mHealth system throughout the study. TBAs in the later-access arm provided usual care until crossing over uni-directionally to the mHealth system at the study midpoint. The primary study outcome was the monthly rate of referral to facility-level care, adjusted for birth volume.

Results

Forty-four TBAs were randomized, 23 to the early-access arm and 21 to the later-access arm. Outcomes were analyzed for 799 pregnancies (early-access 425, later-access 374). Monthly referral rates to facility-level care were significantly higher among the early-access arm (median 33 referrals per 100 births, IQR 22–58) compared to the later-access arm (median 20 per 100, IQR 0–30) (p = 0.03). At the study midpoint, the later-access arm began using the mHealth platform and its referral rates increased (median 34 referrals per 100 births, IQR 5–50) with no significant difference from the early-access arm (p = 0.58). Rates of complications were similar in both arms, except for hypertensive disorders of pregnancy, which were significantly higher among TBAs in the early-access arm (RR 3.3, 95% CI 1.10–9.86).

Conclusions

Referral rates were higher when TBAs had access to the mHealth platform. The introduction of mHealth supportive technologies for TBAs is feasible and can improve detection of complications and timely referral to facility-care within challenging healthcare delivery contexts.

Trial registration

Clinicaltrials.gov NCT02348840.

Obstetric care navigation: a new approach to promote respectful maternity care and overcome barriers to safe motherhood

Background

Disrespectful and abusive maternity care is a common and pervasive problem that disproportionately impacts marginalized women. By making mothers less likely to agree to facility-based delivery, it contributes to the unacceptably high rates of maternal mortality in low- and middle-income countries. Few programmatic approaches have been proposed to address disrespectful and abusive maternity care.

Obstetric care navigation

Care navigation was pioneered by the field of oncology to improve health outcomes of vulnerable populations and promote patient autonomy by providing linkages across a fragmented care continuum. Here we describe the novel application of the care navigation model to emergency obstetric referrals to hospitals for complicated home births in rural Guatemala. Care navigators offer women accompaniment and labor support intended to improve the care experience—for both patients and providers—and to decrease opposition to hospital-level obstetric care. Specific roles include deflecting mistreatment from hospital staff, improving provider communication through language and cultural interpretation, advocating for patients’ right to informed consent, and protecting patients' dignity during the birthing process. Care navigators are specifically chosen and trained to gain the trust and respect of patients, traditional midwives, and biomedical providers. We describe an ongoing obstetric care navigator pilot program employing rapid-cycle quality improvement methods to quickly identify implementation successes and failures. This approach empowers frontline health workers to problem solve in real time and ensures the program is highly adaptable to local needs.

Conclusion

Care navigation is a promising strategy to overcome the “humanistic barrier” to hospital delivery by mitigating disrespectful and abusive care. It offers a demand-side approach to undignified obstetric care that empowers the communities most impacted by the problem to lead the response. Results from an ongoing pilot program of obstetric care navigation will provide valuable feedback from patients on the impact of this approach and implementation lessons to facilitate replication in other settings.

Doppler-based fetal heart rate analysis markers for the detection of early intrauterine growth restriction

INTRODUCTION

One indicator for fetal risk of mortality is intrauterine growth restriction (IUGR). Whether markers reflecting the impact of growth restriction on the cardiovascular system, computed from a Doppler-derived heart rate signal, would be suitable for its detection antenatally was studied.

MATERIAL AND METHODS

We used a cardiotocography archive of 1163 IUGR cases and 1163 healthy controls, matched for gestation and gender. We assessed the discriminative power of short-term variability and long-term variability of the fetal heart rate, computed over episodes of high and low variation aiming to separate growth-restricted fetuses from controls. Metrics characterizing the sleep state distribution within a trace were also considered for inclusion into an IUGR detection model.

RESULTS

Significant differences in the risk markers comparing growth-restricted with healthy fetuses were found. When used in a logistic regression classifier, their performance for identifying IUGR was considerably superior before 34 weeks of gestation. Long-term variability in active sleep was superior to short-term variability [area under the receiver operator curve (AUC) of 72% compared with 71%]. Most predictive was the number of minutes in high variation per hour (AUC of 75%). A multivariate IUGR prediction model improved the AUC to 76%.

CONCLUSION

We suggest that heart rate variability markers together with surrogate information on sleep states can contribute to the detection of early-onset IUGR.

Template-based Quality Assessment of the Doppler Ultrasound Signal for Fetal Monitoring

One dimensional Doppler Ultrasound (DUS) is a low cost method for fetal auscultation. However, accuracy of any metrics derived from the DUS signals depends on their quality, which relies heavily on operator skills. In low resource settings, where skill levels are sparse, it is important for the device to provide real time signal quality feedback to allow the re-recording of data. Retrospectively, signal quality assessment can help remove low quality recordings when processing large amounts of data. To this end, we proposed a novel template-based method, to assess DUS signal quality. Data used in this study were collected from 17 pregnant women using a low-cost transducer connected to a smart phone. Recordings were split into 1990 segments of 3.75 s duration, and hand labeled for quality by three independent annotators. The proposed template-based method uses Empirical Mode Decomposition (EMD) to allow detection of the fetal heart beats and segmentation into short, time-aligned temporal windows. Templates were derived for each 15 s window of the recordings. The DUS signal quality index (SQI) was calculated by correlating the segments in each window with the corresponding running template using four different pre-processing steps: (i) no additional preprocessing, (ii) linear resampling of each beat, (iii) dynamic time warping (DTW) of each beat and (iv) weighted DTW of each beat. The template-based SQIs were combined with additional features based on sample entropy and power spectral density. To assess the performance of the method, the dataset was split into training and test subsets. The training set was used to obtain the best combination of features for predicting the DUS quality using cross validation, and the test set was used to estimate the classification accuracy using bootstrap resampling. A median out of sample classification accuracy on the test set of 85.8% was found using three features; template-based SQI, sample entropy and the relative power in the 160 to 660 Hz range. The results suggest that the new automated method can reliably assess the DUS quality, thereby helping users to consistently record DUS signals with acceptable quality for fetal monitoring.

Monitoring fetal maturation-objectives, techniques and indices of autonomic function

Monitoring the fetal behavior does not only have implications for acute care but also for identifying developmental disturbances that burden the entire later life. The concept, of 'fetal programming', also known as 'developmental origins of adult disease hypothesis', e.g. applies for cardiovascular, metabolic, hyperkinetic, cognitive disorders. Since the autonomic nervous system is involved in all of those systems, cardiac autonomic control may provide relevant functional diagnostic and prognostic information. The fetal heart rate patterns (HRP) are one of the few functional signals in the prenatal period that relate to autonomic control and, therefore, is predestinated for its evaluation. The development of sensitive markers of fetal maturation and its disturbances requires the consideration of physiological fundamentals, recording technology and HRP parameters of autonomic control. Based on the ESGCO2016 special session on monitoring the fetal maturation we herein report the most recent results on: (i) functional fetal autonomic brain age score (fABAS), Recurrence Quantitative Analysis and Binary Symbolic Dynamics of complex HRP resolve specific maturation periods, (ii) magnetocardiography (MCG) based fABAS was validated for cardiotocography (CTG), (iii) 30 min recordings are sufficient for obtaining episodes of high variability, important for intrauterine growth restriction (IUGR) detection in handheld Doppler, (iv) novel parameters from PRSA to identify Intra IUGR fetuses, (v) evaluation of fetal electrocardiographic (ECG) recordings, (vi) correlation between maternal and fetal HRV is disturbed in pre-eclampsia. The reported novel developments significantly extend the possibilities for the established CTG methodology. Novel HRP indices improve the accuracy of assessment due to their more appropriate consideration of complex autonomic processes across the recording technologies (CTG, handheld Doppler, MCG, ECG). The ultimate objective is their dissemination into routine practice and studies of fetal developmental disturbances with implications for programming of adult diseases.

Agile Development of a Smartphone App for Perinatal Monitoring in a Resource-Constrained Setting

Technology provides the potential to empower frontline healthcare workers with low levels of training and literacy, particularly in low- and middle-income countries. An obvious platform for achieving this aim is the smartphone, a low cost, almost ubiquitous device with good supply chain infrastructure and a general cultural acceptance for its use. In particular, the smartphone offers the opportunity to provide augmented or procedural information through active audiovisual aids to illiterate or untrained users, as described in this article. In this article, the process of refinement and iterative design of a smartphone application prototype to support perinatal surveillance in rural Guatemala for indigenous Maya lay midwives with low levels of literacy and technology exposure is described. Following on from a pilot to investigate the feasibility of this system, a two-year project to develop a robust in-field system was initiated, culminating in a randomized controlled trial of the system, which is ongoing. The development required an agile approach, with the development team working both remotely and in country to identify and solve key technical and cultural issues in close collaboration with the midwife end-users. This article describes this process and intermediate results. The application prototype was refined in two phases, with expanding numbers of end-users. Some of the key weaknesses identified in the system during the development cycles were user error when inserting and assembling cables and interacting with the 1-D ultrasound-recording interface, as well as unexpectedly poor bandwidth for data uploads in the central healthcare facility. Safety nets for these issues were developed and the resultant system was well accepted and highly utilized by the end-users. To evaluate the effectiveness of the system after full field deployment, data quality, and corruption over time, as well as general usage of the system and the volume of application support for end-users required by the in-country team was analyzed. Through iterative review of data quality and consistent use of user feedback, the volume and percentage of high quality recordings was increased monthly. Final analysis of the impact of the system on obstetrical referral volume and maternal and neonatal clinical outcomes is pending conclusion of the ongoing clinical trial.