Digital Health Innovations, Tools, and Resources to Support Helping Babies Survive Programs

Development and implementation of a digital clinical decision support system to increase the quality of primary healthcare delivery in a refugee setting in Chad

BACKGROUND

Digital clinical decision support systems (CDSS) enhance the quality of primary healthcare service delivery for vulnerable populations in resource-limited settings. This improvement occurs by strengthening healthcare providers' clinical skills and enabling them to operate more independently while adhering to standard treatment guidelines. From January 2019 to June 2023, we developed and implemented a digital, tablet-based CDSS for children aged 2-59 months. The phases of development, validation, and implementation, as well as lessons learnt and bottlenecks requiring attention, are analysed.

METHODS

The project was carried out in three primary healthcare facilities within a health district in southern Chad, covering a population of 48,000, which includes a significant number of refugees from the Central African Republic. The intended end users were nurses, nurse assistants, and midwives, with supervision provided by health district teams.

RESULTS

The CDSS, based on the WHO's Integrated Management of Childhood Illness (IMCI) and national guidelines, was tailored to the context of available resources and epidemiological patterns. From the outset, the active involvement of a diverse group of local, national, and international technical stakeholders (clinicians, information and communication technology (ICT) specialists, health workers, and district health authorities) facilitated mutual knowledge sharing and product co-creation processes. The CDSS was adapted to the local context, which enhanced local ownership. However, its complexity requires significant effort from clinicians and ICT specialists for development and validation. Additionally, health centres must rely on a technical infrastructure (electricity, internet connection, and server solutions).

CONCLUSIONS

From the outset, a participatory approach involving key stakeholders from the local to the national level of the health system significantly contributed to the successful development and implementation of the CDSS. The sustainability of such an intervention necessitates ongoing long-term commitment. This includes establishing and maintaining the infrastructure, ensuring continuous human resources and technical expertise for implementation and quality assurance, and updating content to reflect advancements in clinical medicine.

Remote versus in-person pre-service neonatal resuscitation training: A noninferiority randomized controlled trial in Ethiopia

BACKGROUND

In-person neonatal resuscitation training in resource-limited settings faces barriers of geographic access, facilitator and participant availability, and high costs. Remote training could overcome these challenges while maintaining effectiveness. We hypothesized that remote neonatal resuscitation training was noninferior to in-person training for acquiring and retaining knowledge and skills for pre-service education in Ethiopia.

METHODS

We conducted a randomized, controlled, noninferiority trial comparing remote versus in-person neonatal resuscitation training in Jimma, Ethiopia. Medical students without prior resuscitation training were randomized (1:1) to Zoom or in-person delivery of the Helping Babies Breathe curriculum supplemented with videos. Outcome assessors were unmasked, but investigators not involved in participant recruitment remained masked. The primary outcome was neonatal resuscitation skills assessed by the Objective Structured Clinical Examination B (OSCE B) two months after training. The noninferiority margin was 13%. Group differences were assessed by intention-to-treat analysis using one-sided t-tests. A p-value < 0.05 suggests noninferiority. The trial was registered at Clinicaltrials.gov, NCT05854745, and is complete.

RESULTS

Between May 1-June 4, 2023, 354 medical students were randomized to remote or in-person training (177 per group). 262 attended the training and completed assessments (remote n = 123, in-person n = 139; 51% male overall). Two months after training, 199 participants completed assessments (remote n = 94, in-person n = 105). Mean OSCE B scores were 19.8 (SD 3.24) in the remote group and 20.5 (SD 2.24) in the in-person group (difference -0.76 [95% CI -1.53 to 0.01]; p < 0.001 for noninferiority), with similar passing rates (remote 62.8%, in-person 60.0%). No adverse events occurred.

CONCLUSION

Remote training achieved noninferiority to in-person training in resuscitation skill retention at two months among medical students with minimal resuscitation experience, suggesting an effective and promising approach to expanding essential clinical training access globally in resource-limited settings.

Preclinical usability evaluation of the Liveborn app: A mobile health application that provides feedback for neonatal resuscitation

Neonatal mortality, particularly due to failure to breathe at birth, remains a significant challenge in low- and middle-income countries (LMICs). Effective neonatal resuscitation is essential to improving survival, but is challenging to implement consistently at the bedside. The Liveborn mobile health application for newborn resuscitation was developed to provide real-time guidance and support debriefing for healthcare workers in LMICs. Liveborn allows an observer to document the timing of key actions during a resuscitation; it then compares the observer data to recommended care and provides data-driven feedback. This study aimed to evaluate the usability of Liveborn in simulated resuscitations. We conducted two rounds of simulated resuscitations using Liveborn with midwives at one health facility in the Democratic Republic of Congo. Each round included ten simulations, with half testing real-time guidance and half focusing on debriefing. Between rounds, Liveborn was iteratively refined based on analysis of video-recordings of the simulations and participant surveys. Midwives' perceptions of usability and feasibility were assessed using previously validated survey tools including the System Usability Scale (SUS) with a score >68 considered above average, and the Feasibility of Intervention Measure (FIM) with a score >12 considered above neutral. Round 1 of testing identified several key usability issues including difficulty accurately recording events, poor adherence to audio guidance that was insufficiently specific, and poor flow of debriefing for intrapartum stillbirth cases. The Liveborn app, after iterative refinement, demonstrated excellent usability (median SUS score of 90 [Q1, Q3: 85, 95]) and excellent feasibility (median FIM score of 19 [16, 20]). Further research is needed to assess Liveborn's effectiveness in real clinical settings and its impact on neonatal outcomes in LMICs.

Advancing newborn care: Precise time of birth detection using ai-driven thermal imaging with adaptive normalization

Around 5%-10% of newborns need assistance to start breathing. Currently, there is a lack of evidence-based research, objective data collection, and opportunities for learning from real newborn resuscitation emergency events. Generating and evaluating automated newborn resuscitation algorithm activity timelines relative to the Time of Birth (ToB) offers a promising opportunity to enhance newborn care practices. Given the importance of prompt resuscitation interventions within the "golden minute" after birth, having an accurate ToB with second precision is essential for effective subsequent analysis of newborn resuscitation episodes. Instead, ToB is generally registered manually, often with minute precision, making the process inefficient and susceptible to error and imprecision. In this work, we explore the fusion of Artificial Intelligence (AI) and thermal imaging to develop the first AI-driven ToB detector. The use of temperature information offers a promising alternative to detect the newborn while respecting the privacy of healthcare providers and mothers. However, the frequent inconsistencies in measurements when using uncooled thermal sensors make normalization strategies critical. Our methodology involves a three-step process: first, we propose an adaptive normalization method based on Gaussian mixture models (GMM) to mitigate issues related to temperature variations; second, we implement an AI model to detect the presence of the newborn within the thermal video frames; and third, we evaluate and post-process the model's predictions to estimate the ToB. A precision of 88.1% and a recall of 89.3% are reported in the detection of the newborn within thermal frames during performance evaluation. Our approach achieves an absolute median deviation of 2.7 s in estimating the ToB relative to the manual annotations.

Essential Newborn Care Virtual Simulations for Skills Retention in Newborn Care

Importance

Newborn mortality accounts for approximately 47% of all mortality of children under the age of 5 years. Virtual simulation may be a viable approach to support retention of essential newborn care knowledge and skills among health care professionals in low- and middle-income countries.

Objective

To evaluate the association between mobile virtual simulation using Virtual Essential Newborn Care (vENC) and knowledge and skills retention in early newborn care in low-resource settings and to propose a frequency of virtual simulation use for among health care professionals who care for newborns in low-resource settings.

Design, Setting, and Participants

This cohort study was conducted at 23 primary, secondary, and tertiary health care facilities in Lagos, Nigeria, for 6 months between December 1, 2022, and June 30, 2023. Participants included nurses and midwives who participated in deliveries and provided newborn care. Potential participants who attended a Helping Babies Breathe or Essential Newborn Care (ENC) course within the past 1 year were excluded.

Exposures

All participants received in-person training using the World Health Organization ENC 1 and ENC 2 curricula along with virtual simulation practice at variable recommended frequencies for 6 months after course completion.

Main Outcomes and Measures

Primary outcomes included assessments of bag-valve-mask (BVM) ventilation skills, and performance on ENC 1 and ENC 2 case A and B scenarios conducted by trained research assistants before, immediately after, and 6 months after the in-person course. All scores ranged from 0% to 100%, with higher scores indicating better performance.

Results

Of 70 enrolled participants (67 of 69 [97%] female), 62 (89%) completed the 6-month follow-up. Immediate posttraining performance (median [IQR] scores: BVM ventilation skills, 93% [86%-100%]; ENC 1 case scenario A, 72% [61%-78%]; ENC 1 case scenario B, 76% [68%-88%]; ENC 2 case scenario A, 80% [73%-87%]; and ENC 2 case scenario B, 88% [70%-95%]) improved compared with pretraining performance for all skill assessments (median [IQR] scores: BVM ventilation skills, 57% [29%-64%]; ENC 1 case scenario A, 39% [28%-50%]); ENC 2 case scenario A, 33% [20%-45%]) (all P < .001). There were further gains in performance at the 6-month follow-up assessment for BVM ventilation (median [IQR], 100% [86%-100%]; P = .04) and the ENC1 and ENC2 assessments by case scenario (case scenario A: ENC 1 median [IQR] score, 78% [72%-83%]; P = .001 and ENC 2 median [IQR] score, 87% [80%-93%]; P = .008; and case scenario B: ENC 1 median [IQR] score, 88% [76%-92%]; P = .009 and ENC 2 median [IQR] score, 93% [80%-100%]; P = .004) relative to the immediate postcourse assessment scores.

Conclusions and Relevance

Findings of this cohort study suggest that the app-based simulations may be effective in supporting the retention of knowledge and skills following ENC training and may contribute to further performance gains for health care professionals in low- and middle-income countries. More clinical and implementation research is needed to explore the impact of virtual simulations on health professionals' clinical practices and neonatal outcomes.

Neonatal outcomes and resuscitation practices following the addition of heart rate-guidance to basic resuscitation

AIM

To evaluate the impact of heart rate-guided basic resuscitation compared to Helping Babies Breathe on neonatal outcomes and resuscitation practices in the Democratic Republic of the Congo.

METHODS

We conducted a pre-post clinical trial comparing heart rate-guided basic resuscitation to Helping Babies Breathe in three facilities, enrolling in-born neonates ≥28 weeks gestation. We collected observational data during a convenience sample of resuscitations and extracted clinical data from the medical record for all participants. We evaluated our primary outcome of effective breathing at three minutes after birth among newborns not breathing well at 30 seconds after birth employing generalized linear models using maximum likelihood estimation.

RESULTS

Among 1,284 newborns with observational data, there was no difference in the proportion effectively breathing at three minutes (adjusted relative risk 1.08 [95% CI 0.81, 1.45]). Among 145 receiving bag mask ventilation, time to bag mask ventilation decreased 64.3 seconds during heart rate-guided resuscitation (p<0.001). Among 10,906 enrolled in the trial, perinatal mortality was unchanged (adjusted relative risk 1.19 [95% CI 0.96, 1.48]) and death before discharge increased (adjusted relative risk 1.43 [95% CI 1.03, 1.99]). Expert review of stillborn cases demonstrated a stillbirth misclassification rate of 33.3% during Helping Babies Breathe versus 5.9% in heart rate-guided resuscitation.

CONCLUSION

During heart rate-guided basic resuscitation, time to bag mask ventilation was reduced by greater than one minute. The increase in death before discharge and unchanged perinatal mortality may be due to resuscitation of newborns with a higher risk of mortality who were previously presumed stillborn. A cluster-randomized trial of heart rate-guided basic resuscitation is needed to evaluate its impact on neonatal mortality in low-resource settings.

Smartphone apps hold promise for neonatal emergency care in low-resource settings

AIM

Many countries risk failing the Sustainable Development Goal to reduce neonatal mortality to 12 in 1000 live births before 2030, necessitating intervention. This scoping review assesses available evidence from studies implementing smartphone application-based education and clinical decision support in neonatal emergency care in low- and middle-income countries and describes applied assessment tools to highlight gaps in the current literature.

METHODS

A systematic search on 28 March 2024 of PubMed, Web of Science, and EMBASE identified original research papers published in peer-reviewed journals after 2014 in English. The evaluation was based on Kirkpatrick's framework.

RESULTS

In total, 20 studies assessing eight different smartphone applications were included. Participants found applications acceptable and feasible in 11 of 14 studies. Knowledge and/or skills were improved in 11 of 12 studies. Behaviour was assessed in 10 studies by tracking app usage. Patient outcome was assessed in four studies, focusing on perinatal mortality, Basic Newborn Care outcomes and correct assessment of newborns.

CONCLUSION

Data from included studies further strengthens hope that smartphone applications can improve neonatal mortality rates in low- and middle-income countries. However, further research into the effectiveness of these applications is warranted. This review highlights gaps in the current literature and provides guidance for future trials.

Using Virtual Reality-Based Simulation in Neonatal Resuscitation Program Training

In this article, we investigate the incorporation of virtual reality (VR) into Neonatal Resuscitation Program (NRP) training. We describe the potential advantages and challenges of the use of VR with NRP. We compare conventional training approaches to VR-based simulation, reviewing diverse VR platforms and their specific roles in neonatal resuscitation education. In addition, technological and ethical aspects in medical training, current research, and prospective developments in this innovative educational tool are discussed.

Mothers' acceptability of using novel technology with video and audio recording during newborn resuscitation: A cross-sectional survey

OBJECTIVE

This study aims to assess the acceptability of a novel technology, MAchine Learning Application (MALA), among the mothers of newborns who required resuscitation.

SETTING

This study took place at Bharatpur Hospital, which is the second-largest public referral hospital with 13 000 deliveries per year in Nepal.

DESIGN

This is a cross-sectional survey.

DATA COLLECTION AND ANALYSIS

Data collection took place from January 21 to February 13, 2022. Self-administered questionnaires on acceptability (ranged 1-5 scale) were collected from participating mothers. The acceptability of the MALA system, which included video and audio recordings of the newborn resuscitation, was examined among mothers according to their age, parity, education level and technology use status using a stratified analysis.

RESULTS

The median age of 21 mothers who completed the survey was 25 years (range 18-37). Among them, 11 mothers (52.4%) completed their bachelor's or master's level of education, 13 (61.9%) delivered first child, 14 (66.7%) owned a computer and 16 (76.2%) carried a smartphone. Overall acceptability was high that all participating mothers positively perceived the novel technology with video and audio recordings of the infant's care during resuscitation. There was no statistical difference in mothers' acceptability of MALA system, when stratified by mothers' age, parity, or technology usage (p>0.05). When the acceptability of the technology was stratified by mothers' education level (up to higher secondary level vs. bachelor's level or higher), mothers with Bachelor's degree or higher more strongly felt that they were comfortable with the infant's care being video recorded (p = 0.026) and someone using a tablet when observing the infant's care (p = 0.046). Compared with those without a computer (n = 7), mothers who had a computer at home (n = 14) more strongly agreed that they were comfortable with someone observing the resuscitation activity of their newborns (71.4% vs. 14.3%) (p = 0.024).

CONCLUSION

The novel technology using video and audio recordings for newborn resuscitation was accepted by mothers in this study. Its application has the potential to improve resuscitation quality in low-and-middle income settings, given proper informed consent and data protection measures are in place.

Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study

BACKGROUND

Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms.

OBJECTIVE

This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe.

METHODS

Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital's health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS).

RESULTS

Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively.

CONCLUSIONS

This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings.

Simulation in a blended learning curriculum for neonatology

Blended learning is a learner-centered educational method that combines online and traditional face-to-face educational strategies. Simulation is a commonly utilized platform for experiential learning and an ideal component of a blended learning curriculum. This section describes blended learning, including its strengths and limitations, educational frameworks, uses within health professions education, best practices, and challenges. Also included is a brief introduction to simulation-based education, along with theoretical and real-world examples of how simulation may be integrated into a blended learning curriculum. Examples of blended learning in Neonatal-Perinatal Medicine, specifically within the Neonatal Resuscitation Program, procedural skills training, and the National Neonatology Curriculum, are reviewed.

The NeoRoo mobile app: Initial design and prototyping of an Android-based digital health tool to support Kangaroo Mother Care in low/middle-income countries (LMICs)

Premature birth and neonatal mortality are significant global health challenges, with 15 million premature births annually and an estimated 2.5 million neonatal deaths. Approximately 90% of preterm births occur in low/middle income countries, particularly within the global regions of sub-Saharan Africa and South Asia. Neonatal hypothermia is a common and significant cause of morbidity and mortality among premature and low birth weight infants, particularly in low/middle-income countries where rates of premature delivery are high, and access to health workers, medical commodities, and other resources is limited. Kangaroo Mother Care/Skin-to-Skin care has been shown to significantly reduce the incidence of neonatal hypothermia and improve survival rates among premature infants, but there are significant barriers to its implementation, especially in low/middle-income countries (LMICs). The paper proposes the use of a multidisciplinary approach to develop an integrated mHealth solution to overcome the barriers and challenges to the implementation of Kangaroo Mother Care/Skin-to-skin care (KMC/STS) in LMICs. The innovation is an integrated mHealth platform that features a wearable biomedical device (NeoWarm) and an Android-based mobile application (NeoRoo) with customized user interfaces that are targeted specifically to parents/family stakeholders and healthcare providers, respectively. This publication describes the iterative, human-centered design and participatory development of a high-fidelity prototype of the NeoRoo mobile application. The aim of this study was to design and develop an initial ("A") version of the Android-based NeoRoo mobile app specifically to support the use case of KMC/STS in health facilities in Kenya. Key functions and features are highlighted. The proposed solution leverages the promise of digital health to overcome identified barriers and challenges to the implementation of KMC/STS in LMICs and aims to equip parents and healthcare providers of prematurely born infants with the tools and resources needed to improve the care provided to premature and low birthweight babies. It is hoped that, when implemented and scaled as part of a thoughtful, strategic, cross-disciplinary approach to reduction of global rates of neonatal mortality, NeoRoo will prove to be a useful tool within the toolkit of parents, health workers, and program implementors.

Recent Progress in Neonatal Global Health Quality Improvement

Quality improvement methodologies, coupled with basic neonatal resuscitation and essential newborn care training, have been shown to be critical ingredients in improving neonatal mortality. Innovative methodologies, such as virtual training and telementoring, can enable the mentorship and supportive supervision that are essential to the continued work of improvement and health systems strengthening that must be done after a single training event. Empowering local champions, building effective data collection systems, and developing frameworks for audits and debriefs are among the strategies that will create effective and high-quality health care systems.

Delayed and Interrupted Ventilation with Excess Suctioning after Helping Babies Breathe with Congolese Birth Attendants

There is a substantial gap in our understanding of resuscitation practices following Helping Babies Breathe (HBB) training. We sought to address this gap through an analysis of observed resuscitations following HBB 2nd edition training in the Democratic Republic of the Congo. This is a secondary analysis of a clinical trial evaluating the effect of resuscitation training and electronic heart rate monitoring on stillbirths. We included in-born, liveborn neonates ≥28 weeks gestation whose resuscitation care was directly observed and documented. For the 2592 births observed, providers dried/stimulated before suctioning in 97% of cases and suctioned before ventilating in 100%. Only 19.7% of newborns not breathing well by 60 s (s) after birth ever received ventilation. Providers initiated ventilation at a median 347 s (>five minutes) after birth; no cases were initiated within the Golden Minute. During 81 resuscitations involving ventilation, stimulation and suction both delayed and interrupted ventilation with a median 132 s spent drying/stimulating and 98 s suctioning. This study demonstrates that HBB-trained providers followed the correct order of resuscitation steps. Providers frequently failed to initiate ventilation. When ventilation was initiated, it was delayed and interrupted by stimulation and suctioning. Innovative strategies targeting early and continuous ventilation are needed to maximize the impact of HBB.

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree

Introduction

Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS.

Methods

Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-of-the-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree.

Results

Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects.

Conclusion

Human-centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

Utilization of a novel mobile application, "HBB Prompt", to reduce Helping Babies Breathe skills decay

BACKGROUND

Helping Babies Breathe (HBB) is a newborn resuscitation training program designed to reduce neonatal mortality in low- and middle-income countries. However, skills decay after initial training is a significant barrier to sustained impact.

OBJECTIVE

To test whether a mobile app, HBB Prompt, developed with user-centred design, helps improve skills and knowledge retention after HBB training.

METHODS

HBB Prompt was created during Phase 1 of this study with input from HBB facilitators and providers from Southwestern Uganda recruited from a national HBB provider registry. During Phase 2, healthcare workers (HCWs) in two community hospitals received HBB training. One hospital was randomly assigned as the intervention hospital, where trained HCWs had access to HBB Prompt, and the other served as control without HBB Prompt (NCT03577054). Participants were evaluated using the HBB 2.0 knowledge check and Objective Structured Clinical Exam, version B (OSCE B) immediately before and after training, and 6 months post-training. The primary outcome was difference in OSCE B scores immediately after training and 6 months post-training.

RESULTS

Twenty-nine HCWs were trained in HBB (17 in intervention, 12 in control). At 6 months, 10 HCW were evaluated in intervention and 7 in control. In intervention and control respectively, the median OSCE B scores were: 7 vs. 9 immediately before training, 17 vs. 21 immediately after training, and 12 vs. 13 at 6 months after training. Six months after training, the median difference in OSCE B scores was -3 (IQR -5 to -1) in intervention and -8 (IQR -11 to -6) in control (p = 0.02).

CONCLUSION

HBB Prompt, a mobile app created by user-centred design, improved retention of HBB skills at 6 months. However, skills decay remained high 6 months after training. Continued adaptation of HBB Prompt may further improve maintenance of HBB skills.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.

Using Mobile Virtual Reality Simulation to Prepare for In-Person Helping Babies Breathe Training: Secondary Analysis of a Randomized Controlled Trial (the eHBB/mHBS Trial)

BACKGROUND

Neonatal mortality accounts for approximately 46% of global under-5 child mortality. The widespread access to mobile devices in low- and middle-income countries has enabled innovations, such as mobile virtual reality (VR), to be leveraged in simulation education for health care workers.

OBJECTIVE

This study explores the feasibility and educational efficacy of using mobile VR for the precourse preparation of health care professionals in neonatal resuscitation training.

METHODS

Health care professionals in obstetrics and newborn care units at 20 secondary and tertiary health care facilities in Lagos, Nigeria, and Busia, Western Kenya, who had not received training in Helping Babies Breathe (HBB) within the past 1 year were randomized to access the electronic HBB VR simulation and digitized HBB Provider's Guide (VR group) or the digitized HBB Provider's Guide only (control group). A sample size of 91 participants per group was calculated based on the main study protocol that was previously published. Participants were directed to use the electronic HBB VR simulation and digitized HBB Provider's Guide or the digitized HBB Provider's Guide alone for a minimum of 20 minutes. HBB knowledge and skills assessments were then conducted, which were immediately followed by a standard, in-person HBB training course that was led by study staff and used standard HBB evaluation tools and the Neonatalie Live manikin (Laerdal Medical).

RESULTS

A total of 179 nurses and midwives participated (VR group: n=91; control group: n=88). The overall performance scores on the knowledge check (P=.29), bag and mask ventilation skills check (P=.34), and Objective Structured Clinical Examination A checklist (P=.43) were similar between groups, with low overall pass rates (6/178, 3.4% of participants). During the Objective Structured Clinical Examination A test, participants in the VR group performed better on the critical step of positioning the head and clearing the airway (VR group: 77/90, 86%; control group: 57/88, 65%; P=.002). The median percentage of ventilations that were performed via head tilt, as recorded by the Neonatalie Live manikin, was also numerically higher in the VR group (75%, IQR 9%-98%) than in the control group (62%, IQR 13%-97%), though not statistically significantly different (P=.35). Participants in the control group performed better on the identifying a helper and reviewing the emergency plan step (VR group: 7/90, 8%; control group: 16/88, 18%; P=.045) and the washing hands step (VR group: 20/90, 22%; control group: 32/88, 36%; P=.048).

CONCLUSIONS

The use of digital interventions, such as mobile VR simulations, may be a viable approach to precourse preparation in neonatal resuscitation training for health care professionals in low- and middle-income countries.

Pediatric Critical Care in Resource Limited Settings-Lessening the Gap Through Ongoing Collaboration, Advancement in Research and Technological Innovations

Pediatric critical care has continued to advance since our last article, "Pediatric Critical Care in Resource-Limited Settings-Overview and Lessons Learned" was written just 3 years ago. In that article, we reviewed the history, current state, and gaps in level of care between low- and middle-income countries (LMICs) and high-income countries (HICs). In this article, we have highlighted recent advancements in pediatric critical care in LMICs in the areas of research, training and education, and technology. We acknowledge how the COVID-19 pandemic has contributed to increasing the speed of some developments. We discuss the advancements, some lessons learned, as well as the ongoing gaps that need to be addressed in the coming decade. Continued understanding of the importance of equitable sustainable partnerships in the bidirectional exchange of knowledge and collaboration in all advancement efforts (research, technology, etc.) remains essential to guide all of us to new frontiers in pediatric critical care.

Effect of Training Healthcare Providers in Helping Babies Breathe Program on Neonatal Mortality Rates

BACKGROUND

Training in the Helping Babies Breathe (HBB) Program has been associated with a reduction in early neonatal mortality rate (ENMR), the neonatal mortality rate (NMR), and fresh stillbirth rate (FSBR) in low- and middle-income countries (LMICs). This program was implemented in five different healthcare facilities in the Oliver Reginald Tambo (ORT) District, South Africa from September 2015 to December 2020.

OBJECTIVE

To determine and compare the FSBR, ENMR, and NMR between 2015 before initiation of the program (baseline) and subsequent years up to 2020 following the implementation of facility-based training of HBB in five hospitals in ORT District.

METHODS

Records of perinatal statistics from January 2015 to December 2020 were reviewed to calculate FSBR, ENMR, and NMR. Data were collected from the five healthcare facilities which included two district hospitals (Hospital A&B), two regional hospitals (Hospital C&D), and one tertiary hospital (Hospital E). Comparisons were made between pre- (2015) and post- (2016-2020) HBB implementation periods. Differences in changes over time were also assessed using linear regression analysis.

RESULTS

There were 19,275 births in 2015, increasing to 22,192 in 2020 with the majority (55.3%) of births occurring in regional hospitals. There were significant reductions in ENMR (OR-0.78, 95% CI 0.70-0.87) and NMR (OR-0.81, 95% CI 0.73-0.90), but not in FSBR, in the five hospitals combined when comparing the two time periods. Significant reduction was also noted in trends over time in ENMR (r ² = 0.45, p = 0.001) and NMR (r ² = 0.23, p = 0.026), but not in FSBR (r ² = 0.0, p = 0.984) with all hospitals combined. In looking at individual hospitals, Hospital A (r ² = 0.61, p < 0.001) and Hospital E (r ² = 0.19, p = 0.048) showed a significant reduction in ENMR over time, but there were no significant changes in all mortality rates for Hospitals B, C, and D, and for the district or regional hospitals combined.

CONCLUSION

There was an overall reduction of 22% and 19% in ENMR and NMR, respectively, from pre- to post-HBB implementation periods, although there were variations from year to year over the 5-year period and, across hospitals. These differences suggest that there were other factors that affected the perinatal/neonatal outcomes in the hospital sites in addition to the implementation of training in HBB.

Effect of resuscitation training and implementation of continuous electronic heart rate monitoring on identification of stillbirth

AIM

To evaluate the effect of resuscitation training and continuous electronic heart rate (HR) monitoring of non-breathing newborns on identification of stillbirth.

METHODS

We conducted a pre-post interventional trial in three health facilities in the Democratic Republic of the Congo. We collected data on a retrospective control group of newborns that reflected usual resuscitation practice (Epoch 1). In the prospective, interventional group, skilled birth attendants received resuscitation training in Helping Babies Breathe and implemented continuous electronic HR monitoring of non-breathing newborns (Epoch 2). Our primary outcome was the incidence of stillbirth with secondary outcomes of fresh or macerated stillbirth, neonatal death before discharge and perinatal death. Among a subset, we conducted expert review of electronic HR data to estimate misclassification of stillbirth in Epoch 2. We used a generalized estimating equation, adjusted for variation within-facility, to compare risks between EPOCHs.

RESULTS

There was no change in total stillbirths following resuscitation training and continuous electronic HR monitoring of non-breathing newborns (aRR 1.15 [0.95, 1.39]). We observed an increased rate of macerated stillbirth (aRR 1.58 [1.24, 2.02]), death before discharge (aRR 3.31 [2.41, 4.54]), and perinatal death (aRR 1.61 [1.38, 1.89]) during the intervention period. In expert review, 20% of newborns with electronic HR data that were classified by SBAs as stillborn were liveborn.

CONCLUSION

Resuscitation training and use of continuous electronic HR monitoring did not reduce stillbirths nor eliminate misclassification.

Improving Newborn Resuscitation by Making Every Birth a Learning Event

One third of all neonatal deaths are caused by intrapartum-related events, resulting in neonatal respiratory depression (i.e., failure to breathe at birth). Evidence-based resuscitation with stimulation, airway clearance, and positive pressure ventilation reduces mortality from respiratory depression. Improving adherence to evidence-based resuscitation is vital to preventing neonatal deaths caused by respiratory depression. Standard resuscitation training programs, combined with frequent simulation practice, have not reached their life-saving potential due to ongoing gaps in bedside performance. Complex neonatal resuscitations, such as those involving positive pressure ventilation, are relatively uncommon for any given resuscitation provider, making consistent clinical practice an unrealistic solution for improving performance. This review discusses strategies to allow every birth to act as a learning event within the context of both high- and low-resource settings. We review strategies that involve clinical-decision support during newborn resuscitation, including the visual display of a resuscitation algorithm, peer-to-peer support, expert coaching, and automated guidance. We also review strategies that involve post-event reflection after newborn resuscitation, including delivery room checklists, audits, and debriefing. Strategies that make every birth a learning event have the potential to close performance gaps in newborn resuscitation that remain after training and frequent simulation practice, and they should be prioritized for further development and evaluation.

eHBB: a randomised controlled trial of virtual reality or video for neonatal resuscitation refresher training in healthcare workers in resource-scarce settings

OBJECTIVE

To assess the impact of mobile virtual reality (VR) simulations using electronic Helping Babies Breathe (eHBB) or video for the maintenance of neonatal resuscitation skills in healthcare workers in resource-scarce settings.

DESIGN

Randomised controlled trial with 6-month follow-up (2018-2020).

SETTING

Secondary and tertiary healthcare facilities.

PARTICIPANTS

274 nurses and midwives assigned to labour and delivery, operating room and newborn care units were recruited from 20 healthcare facilities in Nigeria and Kenya and randomised to one of three groups: VR (eHBB+digital guide), video (video+digital guide) or control (digital guide only) groups before an in-person HBB course.

INTERVENTIONS

eHBB VR simulation or neonatal resuscitation video.

MAIN OUTCOMES

Healthcare worker neonatal resuscitation skills using standardised checklists in a simulated setting at 1 month, 3 months and 6 months.

RESULTS

Neonatal resuscitation skills pass rates were similar among the groups at 6-month follow-up for bag-and-mask ventilation (BMV) skills check (VR 28%, video 25%, control 22%, p=0.71), objective structured clinical examination (OSCE) A (VR 76%, video 76%, control 72%, p=0.78) and OSCE B (VR 62%, video 60%, control 49%, p=0.18). Relative to the immediate postcourse assessments, there was greater retention of BMV skills at 6 months in the VR group (-15% VR, p=0.10; -21% video, p<0.01, -27% control, p=0.001). OSCE B pass rates in the VR group were numerically higher at 3 months (+4%, p=0.64) and 6 months (+3%, p=0.74) and lower in the video (-21% at 3 months, p<0.001; -14% at 6 months, p=0.066) and control groups (-7% at 3 months, p=0.43; -14% at 6 months, p=0.10). On follow-up survey, 95% (n=65) of respondents in the VR group and 98% (n=82) in the video group would use their assigned intervention again.

CONCLUSION

eHBB VR training was highly acceptable to healthcare workers in low-income to middle-income countries and may provide additional support for neonatal resuscitation skills retention compared with other digital interventions.

Development of a Mobile, Self-Sovereign Identity Approach for Facility Birth Registration in Kenya

Birth registration is a critical element of newborn care. Increasing the coverage of birth registration is an essential part of the strategy to improve newborn survival globally, and is central to achieving greater health, social, and economic equity as defined under the United Nations Sustainable Development Goals. Parts of Eastern and Southern Africa have some of the lowest birth registration rates in the world. Mobile technologies have been used successfully with mothers and health workers in Africa to increase coverage of essential newborn care, including birth registration. However, mounting concerns about data ownership and data protection in the digital age are driving the search for scalable, user-centered, privacy protecting identity solutions. There is increasing interest in understanding if a self-sovereign identity (SSI) approach can help lower the barriers to birth registration by empowering families with a smartphone based process while providing high levels of data privacy and security in populations where birth registration rates are low. The process of birth registration and the barriers experienced by stakeholders are highly contextual. There is currently a gap in the literature with regard to modeling birth registration using SSI technology. This paper describes the development of a smartphone-based prototype system that allows interaction between families and health workers to carry out the initial steps of birth registration and linkage of mothers-baby pairs in an urban Kenyan setting using verifiable credentials, decentralized identifiers, and the emerging standards for their implementation in identity systems. The goal of the project was to develop a high fidelity prototype that could be used to obtain end-user feedback related to the feasibility and acceptability of an SSI approach in a particular Kenyan healthcare context. This paper will focus on how this technology was adapted for the specific context and implications for future research.