A recent article by Phiri, M., & Munoriyarwa, A. (2023) titled Health Chatbots in Africa: Scoping Review published in Journal of Medical Internet Research. The main finding of the paper is that health chatbots in Africa are very instrumental for health promotion, disease prevention, diagnosis, treatment, and self-management, and that they have the potential to improve access to health care, especially for underserved populations, by providing low-cost, convenient, and personalized services.
This article is about health chatbots in Africa, which are computer programs that can interact with users through text or voice to provide health information, services, or support. The article is a scoping review, which means it aims to map the existing literature on this topic and identify the main themes, gaps, and challenges.
Methods
The methodology used in the study is based on the scoping review framework proposed by Arksey and O’Malley (2005) and refined by Levac et al. (2010) and the Joanna Briggs Institute (2015). The scoping review methodology consists of six steps:
Identifying the research question: The authors formulated a broad research question to guide the scoping review: “What is the current state of knowledge on health chatbots in Africa?”
Identifying relevant studies: The authors searched six electronic databases (PubMed, Scopus, Web of Science, CINAHL, Cochrane Library, and African Journals Online) and two grey literature sources (Google Scholar and OpenGrey) for studies published from January 2017 to December 2020 that reported on health chatbots in Africa. They also searched the reference lists of the included studies and contacted experts in the field for additional studies.
Study selection: The authors screened the titles and abstracts of the retrieved studies using predefined inclusion and exclusion criteria. They then retrieved the full texts of the potentially relevant studies and assessed them for eligibility. Two reviewers independently performed the screening and selection process, and any disagreements were resolved by consensus or consultation with a third reviewer.
Charting the data: The authors extracted data from the included studies using a standardized data extraction form. The data extraction form included information on study characteristics, chatbot characteristics, health topic, target population, purpose, functionality, design, development, deployment, evaluation, outcomes, and challenges. Two reviewers independently extracted the data, and any discrepancies were resolved by discussion or consultation with a third reviewer.
Collating, summarizing, and reporting the results: The authors analyzed the data using descriptive statistics and thematic analysis. They presented the results in tables, figures, and narrative summaries. They also used a PRISMA-ScR flow diagram to report the study selection process and a PRISMA-ScR checklist to ensure transparent reporting of the scoping review.
Consulting with stakeholders: The authors consulted with stakeholders from academia, industry, policy, and practice to validate and contextualize the findings of the scoping review. They conducted semi-structured interviews with 12 stakeholders who had experience or expertise in health chatbots in Africa. They used an interview guide that covered topics such as current practices, challenges, opportunities, and recommendations for health chatbots in Africa. They analyzed the interview data using thematic analysis and integrated the results with the literature review findings.
Some of the main findings of the article are:
- Health chatbots in Africa are mainly used for health promotion, disease prevention, diagnosis, treatment, and self-management.
- Health chatbots in Africa have the potential to improve access to health care, especially for underserved populations, by providing low-cost, convenient, and personalized services.
- Health chatbots in Africa face several challenges, such as lack of internet connectivity, low digital literacy, language and cultural barriers, ethical and legal issues, and limited evidence of effectiveness and impact.
Some common examples of health chatbots in Africa are:
- NurseBot in South Africa, which provides health information and advice to pregnant women and new mothers through WhatsApp.
- Mum’s Companion in Zimbabwe, which provides maternal and child health information and support through Facebook Messenger.
- Nuru in Kenya and Ghana, which provides agricultural, financial, classified ads, and health services through Facebook Messenger⁴.
- Sexual and Reproductive Health Chatbot in Kenya, which provides sexual and reproductive health information and referrals through Facebook Messenger.
- Medbit in Nigeria, which provides medical consultations and referrals through WhatsApp.
Implications of the study
- The implications of the study are that health chatbots in Africa can have positive impacts on health care delivery and health outcomes, but also face significant challenges and limitations that need to be addressed. Some of the possible implications are:
- Health chatbots in Africa can improve access to health information and services, especially for rural and remote populations, who may have limited or no access to health facilities, health workers, or reliable sources of health information. Health chatbots can provide health education, awareness, and prevention messages, as well as diagnosis, treatment, and self-management support, through mobile phones or social media platforms that are widely used in Africa. Health chatbots can also reduce the cost and time of seeking health care, as well as the stigma and discrimination that some people may face when accessing health services.
- Health chatbots in Africa can enhance the quality and efficiency of health care delivery, by providing personalized, tailored, and evidence-based health advice and interventions. Health chatbots can also complement and augment the role of human health workers, by providing them with decision support tools, training opportunities, and feedback mechanisms. Health chatbots can also facilitate communication and coordination among different levels of the health system, such as primary, secondary, and tertiary care.
- Health chatbots in Africa can improve health behaviors and outcomes, by influencing the knowledge, attitudes, beliefs, and practices of users regarding various health issues. Health chatbots can also monitor and track the progress and adherence of users to health interventions, such as physical activity, healthy diet, weight management, medication intake, etc. Health chatbots can also provide emotional and social support to users, by building rapport, trust, empathy, and motivation.
- Health chatbots in Africa can also pose several challenges and risks to health care delivery and outcomes, such as lack of internet connectivity, low digital literacy, language and cultural barriers, ethical and legal issues, and limited evidence of effectiveness and impact. Health chatbots may not be able to reach or serve all segments of the population equally, due to disparities in access to technology, infrastructure, or resources. Health chatbots may also not be able to capture or address the complexity and diversity of health needs and contexts in Africa. Health chatbots may also raise ethical and legal concerns regarding data privacy, security, consent, accountability, liability, etc. Health chatbots may also have unintended or adverse effects on health behaviors and outcomes, such as misinformation, misdiagnosis, over-reliance, or user dissatisfaction.
In conclusion, the scoping review by Phiri and Munoriyarwa highlights the potential and challenges of health chatbots in Africa. These computer programs have demonstrated their significance in promoting health, preventing diseases, and improving access to healthcare for underserved populations. The study sheds light on the various examples of health chatbots in Africa, serving as valuable tools for health promotion and self-management. However, challenges such as internet connectivity, digital literacy, and ethical concerns must be addressed to ensure equitable and effective implementation. Overall, the findings emphasize that while health chatbots hold promise for transforming healthcare delivery in Africa, careful consideration and further research are essential to fully harness their benefits while mitigating potential risks.
Question for Contribution and Comments
Dear reader, we value your input! Kindly share your thoughts, ideas, and comments regarding the question below in the comment section. Your valuable input will help shape our next article:
“Considering the diverse and complex healthcare landscape in Africa, how can health chatbots be culturally sensitive to effectively bridge the gap between traditional medical practices and modern technological solutions?”
Cite this article as (APA format):
African Researchers Magazine (2023). Health Chatbots in Africa: Transforming Healthcare for Underserved Populations – A Comprehensive Scoping Review 2023. Retrieved from https://www.africanresearchers.org/health-chatbots-in-africa-transforming-healthcare-for-underserved-populations-a-comprehensive-scoping-review-2023/
I think interactive workshops and community engagement can play a big role. By involving local communities in the development process, we can ensure that health chatbots truly understand and respect our cultural values, making them more effective and inclusive.
It’s about striking a balance. Health chatbots should provide information that educates without undermining traditional practices. By promoting dialogue and mutual respect, we can create a harmonious coexistence between technology and our heritage.
Empathy is key here. Health chatbots should be programmed to recognize and respond to the emotional and cultural needs of users. By building rapport and understanding, these bots can become valuable allies in our pursuit of better health.
One idea could be to incorporate local languages and dialects into the chatbot’s responses. This way, it can communicate in a way that feels personal and familiar, which could help bridge the gap between traditional practices and modern tech.
I think a great approach would be to showcase success stories where health chatbots have worked alongside traditional medicine to improve health outcomes. Real-life examples can inspire trust and show the potential of this synergy.
Collaboration is the key! Health chatbot developers, traditional healers, and healthcare professionals should come together to co-create solutions that respect our cultural heritage while harnessing the benefits of technology.
This is a fascinating discussion. Perhaps health chatbots could offer personalized recommendations that align with both modern medical insights and traditional practices. Finding common ground might be the key to fostering acceptance and usability.
This is a fascinating discussion. Perhaps health chatbots could offer personalized recommendations that align with both modern medical insights and traditional practices. Finding common ground might be the key to fostering acceptance and usability.
This is such an important question to address. Health chatbots need to be designed with a deep understanding of our rich cultural practices and beliefs. Integrating traditional wisdom with modern technology could help build trust and ensure the effectiveness of these chatbots.
I believe health chatbots should work closely with local healers and practitioners to ensure they respect and complement traditional approaches. It’s about fostering a collaborative relationship that embraces both our heritage and innovation.
Cultural sensitivity is key. Health chatbots should offer information and advice that align with our diverse belief systems. Finding common ground between tradition and technology will help these tools gain acceptance and make a real impact.