Soula Care

Freemium

Soula Care Image

Soula is an innovative AI-powered assistant that aims to democratize access to top maternity experts. With tailored content based on solid, expert-backed advice, Soula ensures that mothers receive the necessary support for a successful and enjoyable motherhood journey. It leverages cutting-edge GPT3 technology and experienced doula insights to deliver personalized interactions, practical guidance, and emotional support.

How Soula can help you:

Provides evidence-based content and practical tips for pregnancy and parenting.
Personalized support through an AI chatbot with advanced GPT3 technology.
Offers a mental health-oriented pregnancy planner and tracker.
Features doulas with expertise to support mothers.
Can initiate conversations and remember past interactions for continuity.

Why choose Soula: Key features:

Available anytime, overcoming the limitations of specialist availability.
Wide range of structured content to reduce the overwhelm of information.
Reduces the risk of human error in care through reliable AI assistance.
Offers more consistent support than typical prenatal visits.

Who should choose Soula:

First-time mothers seeking conception and pregnancy guidance.
Women who feel overwhelmed by pregnancy information and resources.
Pregnant women and new mothers experiencing loneliness.
Those diagnosed with or at risk of postpartum depression or anxiety.
Mothers looking for 24/7 expert support and community throughout their motherhood journey.

About Soula Care

Website

https://soula.care

Release Date

February 2024

Pricing

Freemium

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The AI tool identified challenges related to medical technology use and staff performance, indicating that ongoing training and support could improve care outcomes. It also provided insights into how COVID-19 affected maternity services, underscoring the need for adaptability in practices. The analysis also indicated that certain human factors might have a greater impact on mothers from ethnic minority groups. However, due to the limited number of reports that included ethnicity data, further research is required to reach definitive conclusions. Next steps The Loughborough researchers hope to secure funding to refine the AI model using a larger, more diverse dataset as expanded testing is essential to validate the tool's effectiveness and further understand the challenges faced by mothers from ethnic minority groups in maternity care. "We are seeking to collaborate with hospitals, health care organizations, and investigation bodies to further refine and apply our AI tool to reports. 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Wed, 20 Nov, 10:04 PM UTC

New AI tool identifies key human factors influencing maternity care outcomes

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However, identifying the human factors involved is often more challenging, as they tend to be complex and nuanced. Currently, experts must carry out manual reviews to extract human factor insights from incident reports. This process is resource-intensive, time-consuming, and relies on individual interpretation and expertise, which can lead to varying conclusions. The AI tool addresses these challenges by identifying and categorising human factors in reports quickly and consistently. Its standardised approach allows it to analyse multiple reports and identify recurring factors, helping pinpoint areas that would benefit most from additional support. The AI model was trained and tested on data from 188 real maternity incident reports. It successfully identified human factors in each report and analysed them collectively, providing insights into where extra support could improve outcomes. AI has transformed our analysis of maternity safety reports. We've uncovered crucial insights far quicker than manual methods. This has enabled us to gather a comprehensive understanding of where there are areas for improvement in maternity care, and these insights can help identify ways to enhance patient safety and improve outcomes for mothers and babies." Professor Georgina Cosma Insights from the reports Teamwork and communication emerged as the most frequently identified human factors across all the analysed reports, underscoring the importance of effective collaboration and clear information exchange among healthcare professionals and patients in promoting safety and quality in maternity care. The analysis also emphasised the importance of thorough patient evaluations - including assessments, investigations, and screenings - as well as the impact of individual patient characteristics, such as birth history and conditions like pre-eclampsia, on care outcomes. The AI tool identified challenges related to medical technology use and staff performance, indicating that ongoing training and support could improve care outcomes. It also provided insights into how COVID-19 affected maternity services, underscoring the need for adaptability in practices. The analysis also indicated that certain human factors might have a greater impact on mothers from ethnic minority groups. However, due to the limited number of reports that included ethnicity data, further research is required to reach definitive conclusions. Next steps The Loughborough researchers hope to secure funding to refine the AI model using a larger, more diverse dataset as expanded testing is essential to validate the tool's effectiveness and further understand the challenges faced by mothers from ethnic minority groups in maternity care. "We are seeking to collaborate with hospitals, healthcare organisations, and investigation bodies to further refine and apply our AI tool to reports. These partnerships will help us extract vital intelligence to prevent adverse incidents and ensure the safety of all mothers and babies," said Professor Cosma. "We also hope to adapt the tool for use with other types of reports, such as adverse police incident reports, where understanding the human factors involved can help prevent future incidents and improve response strategies." The importance of the research for improving maternity care Professor Waterson said: "Our work opens up new possibilities for understanding the complex interplay between social, technical, and organisational factors influencing maternal safety and population health outcomes. "The need for such research was highlighted in the Ockenden Review, which examined maternity care and set to improve safety and care quality in maternity services. "By taking a more comprehensive view of maternal healthcare delivery, we can develop targeted interventions to improve maternal outcomes for all mothers and babies." 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