The proposed innovation for improving access to maternal health in this research protocol is the use of a mobile health application called the Safe Delivery mhealth Application (SDA). This application aims to support clinical decision making among nurses and midwives in basic emergency obstetric and newborn care (BEmONC) by providing timely access to BEmONC guidelines.
The study will investigate the effect of the SDA on nurses’ and midwives’ clinical decision making in two district hospitals in Rwanda. It will use a quasi-experimental design and collect data through a pre-intervention assessment of BEmONC outcomes (such as Apgar score and postpartum hemorrhage progressions), as well as related knowledge, skills, and perceptions of nurses and midwives.
Following the pre-intervention assessment, the SDA will be implemented for six months. After the intervention period, the study will evaluate the effect of the SDA on BEmONC outcomes and the knowledge and skills of nurses and midwives.
Overall, this research protocol suggests that the use of the SDA mobile health application has the potential to improve access to maternal health by supporting clinical decision making among nurses and midwives in low-income countries like Rwanda.
AI Innovations Description
The recommendation proposed in this research protocol is to develop and implement a mobile health application called the Safe Delivery mhealth Application (SDA) to improve access to Basic Emergency Obstetric and Newborn Care (BEmONC) guidelines. The goal is to support nurses and midwives in making clinical decisions during childbirth, ultimately reducing maternal and newborn deaths.
The study will use a quasi-experimental design and employ convergent parallel mixed-methods to collect, analyze, and interpret data. Before the intervention, an assessment of BEmONC outcomes such as Apgar score and postpartum hemorrhage progressions, as well as the knowledge, skills, and perceptions of nurses and midwives, will be conducted.
The intervention will involve implementing the SDA in two district hospitals in Rwanda for a period of six months. After the intervention, the researchers will evaluate the impact of the SDA on BEmONC outcomes and the knowledge and skills of nurses and midwives.
By utilizing mobile technology and providing access to BEmONC guidelines through the SDA, this innovation aims to improve clinical decision making and enhance obstetric care, leading to a reduction in maternal and newborn deaths. The findings from this study will inform the development and implementation of similar mhealth interventions in specific healthcare contexts.
AI Innovations Methodology
Based on the description provided, here are some potential recommendations for innovations to improve access to maternal health:
1. Mobile Health (mHealth) Applications: Develop and implement mobile applications that provide access to Basic Emergency Obstetric and Newborn Care (BEmONC) guidelines. These applications can support clinical decision making by providing real-time information and guidance to healthcare providers, even in remote areas.
2. Telemedicine: Establish telemedicine platforms that allow healthcare providers to remotely consult with specialists and receive guidance on complex cases. This can help improve access to specialized care and enhance clinical decision making in areas where there is a shortage of skilled healthcare professionals.
3. Community Health Workers: Train and empower community health workers to provide basic maternal health services and education in underserved areas. These workers can play a crucial role in improving access to care, especially in remote or marginalized communities.
4. Health Information Systems: Implement robust health information systems that capture and analyze data related to maternal health outcomes. This can help identify gaps in care, monitor progress, and inform evidence-based decision making for improving access to maternal health services.
To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:
1. Define the variables: Identify the key indicators that measure access to maternal health, such as the number of maternal deaths, neonatal mortality rates, or the percentage of women receiving antenatal care.
2. Collect baseline data: Gather data on the current state of access to maternal health services in the target population or region. This can include information on healthcare infrastructure, availability of skilled healthcare providers, and utilization of maternal health services.
3. Introduce the innovations: Implement the recommended innovations, such as mHealth applications, telemedicine platforms, or community health worker programs, in the target population or region.
4. Monitor and collect data: Continuously collect data on the selected indicators to measure the impact of the innovations. This can involve tracking the usage of mHealth applications, monitoring the number of telemedicine consultations, or evaluating the performance of community health workers.
5. Analyze and compare data: Compare the data collected post-intervention with the baseline data to assess the impact of the innovations on improving access to maternal health. This analysis can involve statistical methods, such as regression analysis or trend analysis, to determine the significance of the changes observed.
6. Evaluate outcomes: Evaluate the outcomes of the innovations based on the analysis of the data. This can include assessing changes in maternal and neonatal mortality rates, improvements in healthcare utilization, or increased knowledge and skills of healthcare providers.
By following this methodology, it would be possible to simulate the impact of the recommended innovations on improving access to maternal health and inform future interventions in similar contexts.
