Rural Disease Diagnosis Assistant

The specific problem addressed by our solution is the significant challenge in early disease detection and healthcare accessibility in rural Colombia, compounded by a critical shortage of healthcare professionals and insufficient medical infrastructure. This deficiency is particularly acute in distinguishing diseases with overlapping symptoms such as dengue, chikungunya, and Zika, which are prevalent in these regions. The World Health Organization underscores the difficulties in diagnosing these diseases due to their similar clinical presentations, necessitating precise diagnostic tools in resource-limited settings [1]

In Colombia, the scenario is grim as rural areas have substantially fewer medical resources—such as doctors, nurses, and healthcare facilities—compared to urban centers. This disparity leads to delayed diagnosis and treatment of diseases, contributing to higher morbidity and mortality rates. The healthcare system's challenges are compounded by financial constraints, with residents facing higher out-of-pocket expenses relative to more affluent regions [2] [3].

Further complicating the scenario is the co-circulation of these diseases in areas like the Colombian-Venezuelan border, where differentiation based solely on clinical symptoms can lead to misdiagnosis and suboptimal patient care [4]

Our solution, the Rural Disease Diagnosis Assistant, utilizes a sophisticated approach to enhance diagnostic accuracy in rural Colombia. The platform integrates multiple language models and internally developed disease-specific assistants, tailored for each disease, to improve diagnostic performance. By leveraging the main clinical guidelines for each disease and direct access to the most updated articles on PubMed, healthcare providers can rely on the latest medical knowledge for more accurate diagnoses.

The platform's interface is specially designed to adapt to the infrastructure of local institutions in rural areas. This design ensures that the platform can operate efficiently in resource-limited settings while protecting patient data. The infrastructure is cloud-based, allowing it to scale to multiple rural regions in Colombia and other areas of Latin America where these diseases are prevalent. This scalability ensures that healthcare providers in various regions can benefit from the platform's diagnostic capabilities.

Furthermore, the platform's cloud infrastructure is capable of handling the demands of multiple rural regions simultaneously. This scalability is crucial for ensuring that healthcare providers in different areas can access the platform without experiencing delays or disruptions in service. Overall, the Rural Disease Diagnosis Assistant is designed to improve healthcare outcomes in rural Colombia and other regions of Latin America by providing healthcare providers with accurate and timely diagnostic support.

For a closer look at how our platform operates in a real-world setting, you can view a product demo video here: Arkangel AI Demo.

Our solution specifically targets individuals in rural and remote areas in Colombia. These populations often struggle with access to healthcare due to geographical isolation and limited medical facilities. Many lack regular screenings and early disease detection services, resulting in higher rates of advanced illness and mortality.

By deploying our AI-driven platform, we provide these underserved communities with early disease detection, which can be crucial in areas where traditional medical testing is not readily available, and AI tools for guidance and diagnostic suggestions, in order to help local health practitioners for a better decision-making.

The impact of our solution is the increase of healthcare acces, by improving  treatment outcomes and reducing healthcare costs while preventing severe disease progression. This approach not only saves lives but also enhances the overall healthcare capacity of these communities.

Our team at Arkangel AI is positioned to deliver this solution because we have established a strong connection with the communities we aim to serve. Some of our team members have experience working in the isolated regions of Colombia and most of our models, such as Malaria Detection Model, have been deployed in remote zones. This familiarity enables us to understand the specific challenges and needs that arise in these areas.

Our approach is community-centric. We engage with local healthcare providers and community leaders to ensure our solution is not only technologically advanced but also culturally relevant. This relationships allows us to adapt our platform continuously, in order to guarantee it effectively meets the needs of those it is designed to serve. 

We measure our success by the real improvements in healthcare access and outcomes we achieve. Our previous initiatives have demonstrated our ability to enhance health services in vulnerable populations. This hands-on experience and our focused approach ensure we are active participants in the communities we work with.

We selected the prototype stage for our Rural Disease Diagnosis Assistant because we have not yet tested this specific solution it in the field. However, based on our experience deploying similar assistants, such as patient support models and malaria detection systems, we are confident in our ability to deploy and scale this new solution effectively. 

Our track record of working with top pharmaceutical companies like AstraZeneca, Novartis, Novo Nordisk, Sanofi, and Johnson & Johnson demonstrates our expertise in the field and our commitment to improving healthcare outcomes in rural areas, deploying in 305 hospitals, and impacting over 68 million people across 18 countries. These deployments have resulted in more than 35,400 hours saved in healthcare operations, highlighting the efficiency of our platform. 

While our current focus is on refining the Rural Disease prototype, we are prepared to deploy a first 6-month pilot and then, scale our operations and reach more underserved communities once the solution is ready.

We are applying to Solve to seek both financial support and strategic partnerships. Specifically, we aim to overcome market barriers and expand our solution to other regions in Latin America, following its implementation in Colombia. Our goal is to connect with potential partners who can provide technical, legal, and cultural insights to help us navigate different healthcare systems and practices. Solve's platform offers us the opportunity to showcase our project, attract partners, and gain visibility in the healthcare innovation space.

Our solution, the Rural Disease Diagnosis Assistant, enhances disease diagnosis by providing AI-powered support to healthcare professionals in regions with limited access to healthcare infrastructure. The assistant utilizes AI to analyze health data, assisting doctors in distinguishing between diseases and optimizing the diagnosis process. This approach represents a significant improvement for rural regions where there is a lack of professionals and resources, thereby enhancing the accuracy and efficiency of diagnoses.

One of the key innovations of our solution is its ability to operate effectively in low-resource settings. It is specifically designed to function in areas with poor connectivity and limited technical equipment, ensuring that healthcare providers in rural areas can access advanced diagnostic tools.

Our solution has the potential to catalyze broader positive impacts by promoting a shift towards more preventive care in the healthcare market. By assisting physicians in rural zones in Colombia, we can reduce the occurrence of extensive and costly medical errors, ultimately saving on healthcare costs and improving patient outcomes. This shift could set a new standard for healthcare accessibility and efficiency, benefiting underserved communities in Colombia.

The Rural Disease Diagnosis Assistant, aims to improve healthcare outcomes in rural areas by providing AI-powered support to healthcare professionals. This assistance helps in more accurate and efficient disease diagnosis, leading to early detection and better management of diseases. By enabling healthcare providers to differentiate between diseases and optimize the diagnosis process, the assistant helps reduce medical errors and improve patient outcomes. Ultimately, this can lead to lower healthcare costs and better access to healthcare services in rural communities.

Theory of Change Structure

Activities:

1. • Development and deployment of the Rural Disease Diagnosis Assistant.
   • Implementation in hospitals and clinics in rural areas of Colombia.

2. Immediate Outcomes:

   • Improved accuracy and efficiency of disease diagnosis.
   • Reduction of medical errors in diagnosis.
   • Access to advanced diagnostic tools in resource-limited areas.
     

3. Short-Term Outcomes:

   • Early detection of diseases, leading to more effective treatments.
   • Reduction of treatment costs for diseases in advanced stages.
   • Improved health outcomes for patients in rural areas.

4. Long-Term Outcomes:

   • Reduction of disease burden in rural communities.
   • Improved accessibility and quality of healthcare in rural areas.
   • Establishment of a standard for healthcare accessibility and efficiency in rural settings.

Our impact goals for the Rural Disease Diagnosis Assistant are focused on enhancing healthcare accessibility and outcomes in rural Colombia:

1. Improved Healthcare Accessibility: Increase access to healthcare services for rural populations, particularly in regions with limited infrastructure and healthcare professionals. Facilitate a higher number of healthcare consultations, providing timely and accurate support to healthcare professionals in diagnosing and treating diseases.

2. Reduced Mortality Rates: Aim to reduce mortality rates from treatable diseases in rural areas by ensuring early diagnosis and timely treatment.

3. Early Disease Detection: Target the early detection of diseases to improve treatment outcomes and reduce mortality rates. Use AI to analyze health data for early diagnosis, saving lives and improving health outcomes.
   

4. Cost Savings: Anticipate significant reduction in healthcare costs for rural communities by preventing diseases from progressing to advanced stages. Demonstrate measurable cost savings associated with the prevention of late-stage disease complications and hospitalizations.

5. Enhanced Patient Outcomes: Improve patient outcomes and quality of life by providing healthcare professionals with tools and support for accurate diagnoses and effective treatment plans.

We measure progress through indicators such as the number of healthcare consultations facilitated, percentage of diseases diagnosed early, reduction in mortality rates, cost savings achieved, and improvements in patient outcomes, aligning with UN Sustainable Development Goal 3: Good Health and Well-being.

Our Rural Disease Diagnosis Assistant utilizes AI, ML, NLP, and Big Data to differentiate diseases, aiding accurate diagnoses in rural areas with limited healthcare access. It processes complex health data, providing insights and recommendations to healthcare providers. NLP enhances communication, crucial in low-tech settings. 

This technology improves healthcare in rural Colombia, potentially transforming healthcare accessibility and outcomes. The assistant can be accessed through a web app or our API. All data processing is done on virtual machines in the cloud. We obtain official clinical guidelines from medical or pharmaceutical teams for training and validate model performance internally before deployment

Our solution team at Arkangel AI consists of 17 members in total. We have 15 full-time employees, which work in thre three areas of the company: Growth, Tech and Evidence. Additionally, we have 3 part-time staff members who help us with legal and contractual needs. 

We began the initial planning and development of our solution in 2019, and officially founded Arkangel AI in March 2020. This means we have been actively working on our AI-driven healthcare solution for over four years now, continuously refining and expanding our technology to better serve underserved communities with limited healthcare infrastructure.

At Arkangel AI, we are committed to develop and inclusive environment for all our team members. We understand the importance of diversity in driving innovation, especially given our global outreach and the varied communities we serve.

A significant part of our technology team is comprised of women, reflecting our dedication to empowering women in the tech industry. Our leadership team, led by a female president, exemplifies our commitment to gender diversity and the promotion of female leadership within the technological and entrepreneurial sectors.

Our team is culturally diverse, with members hailing from different parts of the world. This diversity enriches our approach and understanding, allowing us to better tailor our solutions to the unique needs of the communities we serve across various geographical and cultural contexts.

We continuously strive to minimize barriers to opportunity for all staff members by ensuring that our hiring and operational practices are fair and inclusive. This includes regular training and awareness programs on diversity, equity, and inclusion for all employees, ensuring that every team member is valued, supported, and given equal opportunity to thrive and contribute. Our commitment to these principles is not just about creating a better workplace but also about effectively fulfilling our mission to improve global healthcare through innovation.

Our business model is a B2B model targeting pharmaceutical companies, hospitals, and insurers. We offer an AI-as-a-service platform where clients can purchase a license on a monthly or annual basis to access our application (in production) either through the web app or API. Each license includes a set number of requests to the assistant, with costs varying based on demand. We also provide consultancy hours led by data science experts to assist with project needs.

In addition to the license cost, there is a fee for the development or setup of the assistant before deployment. Clients typically seek the development of an assistant or predictive model to increase healthcare access through predictive models or to optimize processes and reduce operational costs, often due to medical errors, through conversational assistants or task-specific agents.

The business model is usually piloted in a specific area or region. Once its performance is validated, negotiations are conducted with the client to expand the model to other regions of the country or globally, or to develop another AI project.

Arkangel AI has achieved significant milestones in becoming financially sustainable. Through our equity financing, we raised USD 2.3 million in our first round, and we secured approximately USD 900,000 in non-equity funding through grants. These funding sources have been instrumental in supporting our growth and development.

One of our key projects, focused on Chronic Kidney Disease (CKD), initially launched in a few countries in the Andean region. Today, it has successfully scaled to cover the entirety of Latin America, encompassing approximately 14 countries. This expansion demonstrates not only the economic viability of our solution but also the scalability of our business model.

Additionally, our partnerships with six of the top 10 pharmaceutical companies globally, including Novartis, AstraZeneca, Johnson & Johnson, Novo Nordisk, Sanofi, and Boehringer Ingelheim, among others, underscore the industry's recognition of the value and potential impact of our AI-powered solutions. These collaborations have not only provided financial support but also validated our business model and solution effectiveness on a global scale.