Milestone & Budget
Milestone 1
Milestone Name: Data Acquisition and Preparation
Milestone Description: This involves gathering diverse dataset encompassing demographic, environmental and health factors related to malaria occurrence and prevalence across our test population groups.
Milestone Deliverable: The procurement of an organized dataset ready for AI model training and machine learning
Deliverable Description: Curated datasets with standardized formats, ensuring data quality and consistency. Milestone Related Budget: $500 (for data procurement and database/storage infrastructure set up)
Timeline: 4 weeks
Outcome: We would have arrived at an organized data set that would form the foundational basis for the engagement of this AI model.
Milestone 2
Milestone Name: AI Model Development
Milestone Description: The development and training of our AI model using advanced learning machine algorithms
Milestone Deliverable: A Prototype AI model with initial predictive capabilities
Deliverable Description: A functional AI model demonstrating the potential to predict malaria occurrences and outcomes.
Milestone Related Budget: $1000 (for engagement of data scientists and computational resources)
Timeline: 3 weeks
Outcome: We would have the emergence of an AI model that has undergone an initial phase of useability testing
Milestone 3
Milestone Name: AI Model Validation and Calibration
Milestone Description: This involves validating our AI model's performance against historical malaria data and calibrate it for accuracy
Milestone Deliverable: A validated and calibrated AI model
Deliverable Description: An AI model with proven accuracy is accurately predicting malaria occurrence and outcomes
Milestone related budget: $400 (for data validation and model refinement)
Timeline: 1 week
Outcome: The AI model would have attained an enhanced degree of accuracy in terms of its predictive ability as a result of series of systematic iterations and refinement.
Milestone 4
Milestone Name: Integration of AI model with Healthcare systems
Milestone Description: This involves the integration of our AI model with healthcare systems and data pipelines (pathways) that allow for real-time data analysis.
Milestone Deliverable: Operational integration of our AI model with healthcare systems Deliverable Description: Our AI model is seamlessly integrated into our test healthcare infrastructure for continuous monitoring.
Milestone related budget: $1000 (for system integration and IT support)
Timeline: 2 weeks
Outcome: Our AI model would have proven its functionality within an environment that allows for its application in real time.
Milestone 5
Milestone Name: Pilot Implementation
Milestone Description: This involves the implementation of our AI model in a pilot project in collaboration with a healthcare provider or a humanitarian aid health agency.
Milestone Deliverable: Generating a successful pilot implementation report
Deliverable Description: A report detailing the outcomes and benefits of using our AI model in real-life settings
Milestone related budget: $300 (for pilot project execution, evaluation and documentation)
Timescale: 1 week
Outcome: Our AI model would have undergone its first field operational deployment in preparation for larger scale utility.
Milestone 6
Milestone Name: Regulatory Compliance
Milestone Description: This entails ensuring the compliance of our AI Model with healthcare regulations
Milestone Deliverable: Ensuring the attainment of regulatory compliance certification
Deliverable Description: To obtain official certification demonstrating adherence to data privacy and security regulations.
Milestone related budget: $200 (legal and compliance consultation).
Timeline: 2 weeks
Outcome: Our AI model would have navigated all the relevant regulatory landscape to allow for its unhindered operation.
Milestone 7
Milestone Name: Scaling Implementation
Milestone Description: This involves expanding our AI model’s implementation to multiple healthcare providers and regions.
Milestone Deliverable: A scalable AI model deployment plan
Deliverable Description: This entails developing a plan outlining the strategy for the expansion of the usage of our AI model.
Milestone related budget: $200
Timescale: 2 weeks
Outcome: We would gain insights on the best strategies to utilize in piloting our AI model across multiple regions.
Milestone 8
Milestone Name: Long Term Monitoring and Optimization
Milestone Description: The establishment of continuous monitoring and optimization processes for our AI model’s performance.
Milestone Deliverable: Ongoing monitoring and optimization frameworks
Deliverable Description: A framework for maintaining and improving our AI model’s accuracy over time
Milestone related budget: $600 (for procurement of monitoring tools and data analysis)
Timescale: 3 weeks
Outcome: We would successfully established a framework that ensures viability of our AI model.
Milestone 9
Milestone Name: Impact Assessment
Milestone Description: The assessment of the impact of our AI model on malaria control and healthcare outcomes
Milestone Deliverable: Procurement of an Impact Assessment Report
Deliverable Description: A comprehensive report that provides quantitative and qualitative data on the contributions of our AI model to malaria control and general healthcare.
Milestone related budget: $400 (for impact assessment studies and reporting)
Timescale: 2 weeks
Outcome: We would have had a good knowledge on the strengths and challenges associated with real life application of our AI model in the healthcare ecosystem.
Milestone 10
Milestone Name: Market Expansion
Milestone Description: This involves exploring opportunities for international market expansion and partnerships.
Milestone Deliverable: Market Expansion Strategy
Deliverable Description: A strategy outlining the steps to be taken in penetrating global markets and establish strategic partnerships.
Milestone Related Budget: $500 (for market research and partnership development)
Timescale: 3 weeks
Outcome: We would have gained useful insights on how to pilot our AI model across multinational borders and global scale platforms.
Long Description
Company Name
HIESMEDIC
Summary
Our project proposes a ground breaking initiative that harnesses the power of machine learning to create a robust AI model that can accurately forecast malaria outbreaks and their impact on various population groups. This innovation incorporates a viable and round-the-clock database that can provide predictability patterns for other communicable diseases and can be accessed as API's for other novel innovations and tech systems that would require such category of data for other forms of predictive analysis.
Funding Amount
$5000
The Problem to be Solved
Malaria, a vector borne disease remains a significant global health challenge. There was an estimated 247 million malaria cases in 2021 in about 84 malaria endemic countries. This showed a significant increase from the 2020 prevalence data by the World Health Organization which stood at 245 million cases. Despite substantial progress in malaria control and prevention, the ability to predict malaria occurrences and outcomes across different demographics remain very limited. This limitation hinders timely deployment of targeted interventions resulting in outbreaks and loss of lives.
There is there for an urgent need for an AI Model that can enhance malaria predictability in terms of outcomes and occurrence. The current state of malaria prediction models often lack the granularity required to account for unique vulnerabilities among specific population groups. This is common among other predictive systems. There is therefore a need for the AI4M Model as it provides API's which can interconnect other predictive systems and promote data sharing to allow for more efficient resource allocation and intervention strategies.
Our Solution
Our AI4M Model provides a robust malaria prediction system that enhances malaria predictability among diverse demographics by integrating and analyzing the complex interplay of factors that precipitates it's occurrence and progression. These factors include; environmental conditions, socio-economic status, genetics, healthcare access, amongst other factors that influence malaria dynamics.
Our model utilizes advanced machine learning techniques and extensive analysis of epidemiological data to provide actionable insights into the occurrence and outcomes of malaria among different human groups. These actionable insights are then integrated into a real time database system that are then transmuted into API's. These API's are made accessible and can be integrated as functional tools in other epidemiological prediction systems or other specific prediction systems.
Future Scope
We envision several avenues for further development and expansion of our AI model. Some of these include:
1. We aim to continuously refine our model to achieve greater precision and granularity in predicting malaria occurrences. These refinement processes include but are not limited to development of sub-models tailored to specific population groups and geographic regions, allowing for more localized predictions.
2. We will integrate real-time data feeds and Internet of Things (IoT) devices into our model which enable healthcare providers respond swiftly to emerging malaria threats.
3. Our team will also integrate into our AI model, user-friendly mobile applications, web applications and telemedicine platforms to empower healthcare workers in remote regions. These tools can assist in diagnosis and treatment, thereby bridging healthcare gaps.
4. We will also ensure the adaptability and expansion of our AI model to address other infectious diseases. In doing this, we seek to gain global expansion by contributing to global health efforts. We will collaborate with other research institutions and AI developers to foster data sharing and ensure the creation of a collective database. This will allow for further diversification and refinement of our project. With these new and reformed ideas, we will gain a broader perspective of our project and introduce these new found perspectives into successive funding rounds (New project pool) of the SNET ecosystem.
Marketing Strategy
To effectively market our innovation, we will first conduct a thorough market analysis to understand our target audience, market trends and competition.
MARKET ANALYSIS
1. Understanding our target audience: our primary target audiences will include
a. Government Health Agencies - they are responsible for malaria control at national levels and would require accurate predictive tools.
b. Non-profit and Humanitarian Aid Organizations - these groups often focus on specific regions or vulnerable population and would require a predictive tool for tailored interventions.
c. Healthcare Providers - Hospitals and clinics in malaria prone or endemic areas can benefit from this novel innovation for timely interventions.
d. Research Institutions - Academic and research organizations can utilize our AI model and database for epidemiological studies and other predictive analysis.
2. Market Trends: Certain indicators which showcases the high propensity of adoption and deployment of our AI Model includes the following;
a. Increasing Emphasis on Precision Healthcare - The healthcare industry is moving towards personalized and data-driven health interventions. This strongly aligns with the value proposition of our AI Model.
b. Global Health Initiatives - There is a growing global focus on combatting infectious diseases including malaria. This makes our AI Model timely and relevant.
c. Data Privacy and Compliance - With the increasing demand for confidentiality in healthcare, as well as the fact that strict data regulations are shaping the market, there is need to adopt AI championed solutions in healthcare.
3. Competitive Landscape: Adopting the following strategies will help ensure sustainability and relevance of our AI Model in the face of growing competition both now and in the future;
a. We will continuously identify and evaluate existing players in the AI-driven healthcare prediction space, particularly those focusing on infectious diseases like malaria.
b. Analyze the strength and weaknesses of these existing key players to identify opportunities for differentiation and improvement on our AI-model
c. We will in all our engagements continuously highlight how our AI-model offers unique benefits such As demographic-specific predictability
MARKETING STRATEGY
1. Content marketing
a. We will create educational contents that explain the importance of accurate malaria prediction and how our AI Model works
b. We will regularly develop case studies showcasing real world application of our AI model and the success stories recorded.
2. Thought Strategy
a. We will regularly publish research papers and journals on reputable healthcare and AI publications.
b. We will publicize the findings of our research at conferences and seminars to establish ourselves as experts in the field.
3. Partnerships and Collaborations
a. We will collaborate with government agencies and humanitarian aid health organizations for pilot projects
b. We will also partner with academic and research institutions for ongoing research and clinical validation studies.
4. We will offer personalized presentations and demonstrations to potential clients, showcasing how our AI model can meet their specific needs.
5. We will emphasize our commitment data security and compliance with healthcare data regulations like Health Insurance Portability and Accountability Act (HIPAA)
6. Pricing Models
a. We will develop flexible pricing structures to accommodate the budget of our clients
b. We will consider tiered pricing based on usage and population size
7. We will provide comprehensive user training and ongoing support to ensure effective integration and utilization of our AI model.
8. Clearly demonstrate measurable outcomes which our AI model provides including cost effectiveness, efficiency and lives saved.
9. We will establish feedback mechanisms to continuously improve our AI model based on user experiences and changing malaria patterns.
10. We will continuously showcase the contribution of our AI model to the long term sustainability of malaria control efforts by optimizing resource allocation and reducing the spread of drug resistant malaria strains.
Our Project Milestones and Cost Breakdown
Milestone 1
Milestone Name: Data Acquisition and Preparation
Milestone Description: This involves gathering diverse dataset encompassing demographic, environmental and health factors related to malaria occurrence and prevalence across our test population groups.
Milestone Deliverable: The procurement of an organized dataset ready for AI model training and machine learning
Deliverable Description: Curated datasets with standardized formats, ensuring data quality and consistency. Milestone Related Budget: $500 (for data procurement and database/storage infrastructure set up)
Timeline: 4 weeks
Outcome: We would have arrived at an organized data set that would form the foundational basis for the engagement of this AI model.
Milestone 2
Milestone Name: AI Model Development
Milestone Description: The development and training of our AI model using advanced learning machine algorithms
Milestone Deliverable: A Prototype AI model with initial predictive capabilities
Deliverable Description: A functional AI model demonstrating the potential to predict malaria occurrences and outcomes.
Milestone Related Budget: $1000 (for engagement of data scientists and computational resources)
Timeline: 3 weeks
Outcome: We would have the emergence of an AI model that has undergone an initial phase of useability testing
Milestone 3
Milestone Name: AI Model Validation and Calibration
Milestone Description: This involves validating our AI model's performance against historical malaria data and calibrate it for accuracy
Milestone Deliverable: A validated and calibrated AI model
Deliverable Description: An AI model with proven accuracy is accurately predicting malaria occurrence and outcomes
Milestone related budget: $400 (for data validation and model refinement)
Timeline: 1 week
Outcome: The AI model would have attained an enhanced degree of accuracy in terms of its predictive ability as a result of series of systematic iterations and refinement.
Milestone 4
Milestone Name: Integration of AI model with Healthcare systems
Milestone Description: This involves the integration of our AI model with healthcare systems and data pipelines (pathways) that allow for real-time data analysis.
Milestone Deliverable: Operational integration of our AI model with healthcare systems Deliverable Description: Our AI model is seamlessly integrated into our test healthcare infrastructure for continuous monitoring.
Milestone related budget: $1000 (for system integration and IT support)
Timeline: 2 weeks
Outcome: Our AI model would have proven its functionality within an environment that allows for its application in real time.
Milestone 5
Milestone Name: Pilot Implementation
Milestone Description: This involves the implementation of our AI model in a pilot project in collaboration with a healthcare provider or a humanitarian aid health agency.
Milestone Deliverable: Generating a successful pilot implementation report
Deliverable Description: A report detailing the outcomes and benefits of using our AI model in real-life settings
Milestone related budget: $300 (for pilot project execution, evaluation and documentation)
Timescale: 1 week
Outcome: Our AI model would have undergone its first field operational deployment in preparation for larger scale utility.
Milestone 6
Milestone Name: Regulatory Compliance
Milestone Description: This entails ensuring the compliance of our AI Model with healthcare regulations
Milestone Deliverable: Ensuring the attainment of regulatory compliance certification
Deliverable Description: To obtain official certification demonstrating adherence to data privacy and security regulations.
Milestone related budget: $200 (legal and compliance consultation).
Timeline: 2 weeks
Outcome: Our AI model would have navigated all the relevant regulatory landscape to allow for its unhindered operation.
Milestone 7
Milestone Name: Scaling Implementation
Milestone Description: This involves expanding our AI model’s implementation to multiple healthcare providers and regions.
Milestone Deliverable: A scalable AI model deployment plan
Deliverable Description: This entails developing a plan outlining the strategy for the expansion of the usage of our AI model.
Milestone related budget: $200
Timescale: 2 weeks
Outcome: We would gain insights on the best strategies to utilize in piloting our AI model across multiple regions.
Milestone 8
Milestone Name: Long Term Monitoring and Optimization
Milestone Description: The establishment of continuous monitoring and optimization processes for our AI model’s performance.
Milestone Deliverable: Ongoing monitoring and optimization frameworks
Deliverable Description: A framework for maintaining and improving our AI model’s accuracy over time
Milestone related budget: $600 (for procurement of monitoring tools and data analysis)
Timescale: 3 weeks
Outcome: We would successfully established a framework that ensures viability of our AI model.
Milestone 9
Milestone Name: Impact Assessment
Milestone Description: The assessment of the impact of our AI model on malaria control and healthcare outcomes
Milestone Deliverable: Procurement of an Impact Assessment Report
Deliverable Description: A comprehensive report that provides quantitative and qualitative data on the contributions of our AI model to malaria control and general healthcare.
Milestone related budget: $400 (for impact assessment studies and reporting)
Timescale: 2 weeks
Outcome: We would have had a good knowledge on the strengths and challenges associated with real life application of our AI model in the healthcare ecosystem.
Milestone 10
Milestone Name: Market Expansion
Milestone Description: This involves exploring opportunities for international market expansion and partnerships.
Milestone Deliverable: Market Expansion Strategy
Deliverable Description: A strategy outlining the steps to be taken in penetrating global markets and establish strategic partnerships.
Milestone Related Budget: $500 (for market research and partnership development)
Timescale: 3 weeks
Outcome: We would have gained useful insights on how to pilot our AI model across multinational borders and global scale platforms.
Risk and Mitigation
1. Data Quality and Availability
Risk Description – Incomplete or inaccurate data can hinder our AI model’s accuracy
Mitigation Strategy – We will implement rigorous data quality checks and source data only from reliable providers. We would also maintain a feedback system for data validation as well as development data augmentation techniques to address data gaps.
2. AI Model Over-fitting
Risk description – Our AI model may become overly specific due to the training and prediction patterning offered by our machine learning algorithm and may fail to generalize when applied to other forms of predictive analysis
Mitigation Strategy – We would employ cross validation techniques to assess model generalization. We will also regularly update and retrain our AI model with new data to prevent over fitting.
3. Regulatory Compliance
Risk description – This could arise from failure to comply with relevant healthcare data regulations (example is the Health Insurance Portability and Accountability Act).
Mitigation Strategy – We will engage renowned and experienced legal experts to ensure full regulatory compliance.
4. Technical Challenges
Risk description – Our AI model may experience technical issues such as hardware failures or software bugs which can interrupt its operations.
Mitigation Strategy – We could organize and maintain a dedicated technical support team to quickly address and resolve technical issues. We would also develop and implement redundancy and fail over mechanisms for the critical components of our AI model.
5. Model Interpretability
Risk description – Our AI model may occasionally lack interpretability making it difficult for its users to trust and act on the predictive analysis it provides.
Mitigation Strategy – We would develop and implement model interpretability tools that provides insights into how the model reaches its predictions. We would also utilize user-friendly dashboards and explanations for predictions.
6. Ethical Concerns
Risk description – Our AI model might inadvertently introduce biases and ethical concerns in its predictions.
Mitigation Strategy – We will continuously monitor and audit our model for biases. We will also develop and implement fairness-aware machine learning techniques and regularly update ethical guidelines for our AI model’s operability.
7. Resistance to Change
Risk description – This could arise from resistance of healthcare systems and workers to accepting and adopting AI driven solutions due to adaption to traditional systems.
Mitigation Strategy – We will offer comprehensive full user training and support during the integration process and highlight the tangible benefits of our AI model through pilot projects and success stories.
8. Data Security Breach
Risk description – Unauthorized access to healthcare information in our database can result in data breaches.
Mitigation Strategy – We will develop and implement state-of-the-art encryption, access and intrusion detection systems. We will also conduct regular security audits and train our IT support staff on cyber security measures.
9. Market Competition
Risk description – This results from intense competition in the AI healthcare prediction space from springing up of other Prediction platforms.
Mitigation Strategies – We will continuously innovate and enhance our AI model to maintain a competitive edge in the market space. We will also diversify and seek strategic partnerships to expand our market reach.
10. Funding Shortfalls
Risk description – This could arise from budget constraints that could affect the progress of our project
Mitigation Strategy – We will develop a contingency budget and seek for additional funding sources. We will also prioritize and phase project activities to ensure critical milestones are not delayed.
Voluntary Revenue
We recognize the immense value of community support in driving the success of our AI product model for malaria prediction. Engaging with the SNET deep funding community offers us a unique opportunity to foster trust and collaboration. We have developed a structured framework where we can regularly share the success of our project especially as regards the positive impact of our AI model on malaria control and health outcomes. To further elevate the involvement of the SNET community in our project’s successes, we will define clear progress metrics to allow for impact assessment. These could include (but are not limited to) lives saved or reduced cost in healthcare. Finally, we will also actively involve the SNET community in decision making processes related to how shared benefits on our AI model are allocated and utilized.
Open Source
We are keen on open-sourcing our AI prediction Model. We believe this will provide immense benefits in ways which include;
1. Open sourcing our AI model will provide transparency in the development of our AI model. By opening up our prediction methodologies to the public, we allow for a more robust scrutiny and understanding of how our AI model works. This builds trust among our users and stakeholders.
2. This approach will also open the door to contributions from a diverse pool of experts. With these sort of contribution, we can harness into a wealth of knowledge leading to more robust algorithms and methodologies.
3. Open sourcing our model fosters rapid innovation. The collectively contributions of the global AI community can enable us quickly identify key challenging areas and addresses them quickly.
4. By inviting researchers, developers and organizations to participate, we create a collaborative system where feedback and ideas flow freely.
5. Open sourcing our product model would allow different regions and healthcare systems to customize and fine tune our product model to suit their specific requirements.
6. Open sourcing can also allow for cost efficiency. With multiple inputs/contributions from the AI community, we can save resources and allocate budget more effectively.
7. Open sourcing our product model encourages discussions about bias, fairness and transparency of our prediction model. This allows incorporation of these principles into the functionality of our AI model thereby reinforcing ethical AI practices in our modes of operation.
Data Privacy and Security Concerns
We recognize the importance of ensuring strict privacy in handling healthcare data. Thus, We will ensure that all the processes involved in the development of our AI model adheres strictly to all required privacy regulations. Also, all data either on storage or in transmission, will be encrypted using state-of-the-art encryption algorithms. Role based access to data controls would also be used to ensure only personnel with legitimate need would be able to access it. Also, Personally Identifiable Information (PII) would be anonymized and de-identified as often as possible. These are some of the frameworks we would be employing to ensure strict compliance with privacy and security of healthcare data.
Our Team
Here are the top 5 members of our project team, each of whom brings a wealth of expertise and experience that is vital for the development and implementation of our AI model.
1. Mr. Akwa Erim (Chief Data Scientist)
Mr. Akwa holds certifications in machine learning. As a senior lecturer in a reputable University, he has spent over 6 years in researching and developing AI-driven healthcare solutions. He has an in-depth knowledge of advanced statistical modelling and data analysis. This would be invaluable to the construction and fine tuning of our AI prediction model. With numerous published papers on AI application in healthcare, his expertise will guide the technical development of our AI model, ensuring its capability of handling sophisticated demographic and health data.
2. Mr. Hope Okon (Epidemiologist and Public Health Specialist)
Mr. Hope holds a Bachelor degree in clinical and applied biochemistry and a Masters Degree in Genomic Epidemiology. He is an epidemiologist with extensive experiences in infectious diseases such as COVID-19 and Malaria. He has worked on the frontlines of healthcare in malaria endemic regions, understanding the nuances of disease transmission and public health intervention. He has clinical working experiences with reputable health institutions and organizations such as PFIZER. His practical knowledge will be essential for translating our AI model’s prediction into effective interventions, bridging the gap between data-driven insights and real world implementation.
https://www.linkedin.com/in/hope-okon-060a651a1?trk=contact-info
3. Miss Janet Odey (Software Engineering Lead)
Miss Janet is a full stark developer and software engineer with over 5 years of experience in software development. Miss Jane has curated the creation of scalable and robust healthcare applications, most notably the development of an AI algorithm for early stage cancer predictability in female patients using estimated breast density. She is a a Ford Community Impact Fellow, a licensee of the William Davidson Research Institute and a TECHNOVATION regional ambassador. Her technical leadership will ensure the accuracy and practicability of our AI model to allow for healthcare providers to seamlessly integrate it into their system, maximizing its impact.
https://www.linkedin.com/in/jane-odey-127375159/
4. Dr. Emmanuel Edu (Epidemiologist and Public Health Specialist)
Dr. Edu holds a bachelor degree in human physiology, an MBBS from the University of Kharkiv, Ukraine and is currently undergoing studies for a Masters degree in Public Health. He also holds certifications in Global Health Policies and has worked with reputable international organizations to shape strategies for disease control and healthcare access. His insights into the policy landscape and understanding of the needs of different population groups will guide and ensure the alignment of our project with broader healthcare initiatives and ensure it meets the demands of our client community.
https://ua.linkedin.com/in/emmanuel-edu-2181031a9
5. Clement Umoh (Data Privacy and Ethics Specialist)
Mr. Umoh is a software penetration tester with background in cyber security. He has relevant experiences in handling data privacy and ethics in healthcare, ensuring responsible use of medical data. With increasing concerns about data privacy and ethical biases on our AI model, Mr. Umoh works to ensure our project adheres to the strictest ethical guidelines and regulatory standards, safeguarding patient information and boosting our user trust.
Related Links
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