Research and Development Hub
Driving continuous innovation through research, emerging technologies, and evidence-based practices in public health informatics.
About the Hub
The Research and Development Hub (R&D-Hub) focuses on exploring innovative approaches, technologies, and methodologies in health informatics. We bridge the gap between cutting-edge research and practical applications to address emerging health challenges.
Our interdisciplinary team conducts applied research, develops prototypes, evaluates new technologies, and translates promising innovations into scalable solutions that advance the field of health informatics.
Core Focus Areas:
Hub Leadership
Dr. Amara Okafor
Director, R&D-Hub
Renowned researcher in AI applications for global health with over 40 published papers and multiple patents.
Contact the Hub
Research Areas
AI & Machine Learning in Public Health
Developing and evaluating artificial intelligence and machine learning approaches to predict disease outbreaks, identify patterns in health data, and optimize public health responses.
- Predictive models for disease surveillance
- Natural language processing for health reports
- Computer vision for medical imaging
- Explainable AI for healthcare decisions
Mobile Health Technologies
Researching innovative approaches to leverage mobile technologies for health data collection, patient monitoring, and healthcare delivery in diverse settings.
- Low-resource mobile diagnostics
- Wearable health monitoring systems
- SMS-based health interventions
- Remote consultation technologies
One Health Informatics
Exploring integrated approaches to connect human, animal, and environmental health data for comprehensive health monitoring and intervention.
- Cross-domain data integration models
- Zoonotic disease surveillance systems
- Environmental health monitoring
- Ecosystem health indicators
Health Data Analytics
Developing advanced methods for analyzing complex health datasets to extract meaningful insights for public health decision-making.
- Big data analytics for health systems
- Geospatial health data analysis
- Trend detection algorithms
- Predictive analytics for health outcomes
Health Data Security & Privacy
Researching innovative approaches to protect health data while enabling appropriate sharing and analysis for public health purposes.
- Privacy-preserving analytics
- Secure multi-party computation
- Federated learning for health data
- Blockchain for health data integrity
Emerging Technologies
Exploring the application of cutting-edge technologies in health informatics to solve complex public health challenges.
- IoT for health monitoring
- Virtual and augmented reality
- Digital twins for health systems
- Remote sensing technologies
Featured Research Projects
AI-Enhanced Early Warning System for Infectious Disease Outbreaks
This research project combines multiple data streams—including clinical reports, social media, environmental sensors, and population movement data—to predict infectious disease outbreaks weeks before traditional methods.
Key Innovations:
- Multi-modal deep learning architecture for heterogeneous data sources
- Novel approach to integrating spatiotemporal data with clinical indicators
- Explainable AI techniques to help public health officials understand predictions
- Adaptive learning system that improves with each outbreak response
Impact: Field testing has demonstrated the ability to detect outbreaks 2-3 weeks earlier than traditional surveillance methods with 85% accuracy.
Low-Cost Community Health Monitoring System
This project aims to develop an affordable, easy-to-deploy community health monitoring system for resource-constrained settings that enables early detection of health issues without requiring clinical infrastructure.
Key Innovations:
- Low-cost, low-power sensor network for community deployment
- SMS-based reporting system requiring minimal technology
- AI-driven analysis of basic health indicators to detect community health trends
- Open-source hardware designs and software for local manufacturing and adaptation
Impact: Initial pilot in three rural communities has shown a 40% increase in early detection of respiratory illness clusters and improved response time by health authorities.
One Health Data Integration Platform
This groundbreaking research project has developed a novel approach to integrate human health, animal health, and environmental data into a cohesive platform for comprehensive health monitoring and analysis.
Key Innovations:
- Novel ontology framework linking human, animal, and environmental health concepts
- Cross-domain data harmonization techniques
- Integrated analysis methods for detecting inter-domain health patterns
- Early warning algorithm for zoonotic disease spillover events
Impact: Successfully deployed in two regions, the system has identified three previously unrecognized connections between environmental changes and human disease patterns.
Technical Approach
Our platform combines data from human health, animal health, and environmental monitoring sources through a sophisticated integration layer for comprehensive analysis.
Our Innovation Process
Explore
We systematically scan the horizon for emerging health challenges, new technologies, and innovative approaches from across disciplines.
- Monitor global health challenges
- Identify technology trends
- Connect with stakeholders
- Scan scientific literature
Research
We conduct rigorous scientific research to develop new approaches, test hypotheses, and generate evidence for promising innovations.
- Design research studies
- Analyze existing data
- Develop theoretical models
- Publish findings
Prototype
We transform promising research findings into working prototypes to demonstrate feasibility and refine concepts through iterative development.
- Create proof-of-concept models
- Develop minimal viable products
- Iterate based on feedback
- Document technical specifications
Test
We rigorously evaluate our innovations in real-world settings to assess effectiveness, usability, and impact before scaling.
- Conduct controlled pilot tests
- Gather user feedback
- Measure key performance indicators
- Assess implementation challenges
Transfer
We facilitate the transition of proven innovations to operational implementation through our other CHII hubs or external partners.
- Develop implementation guidelines
- Create training materials
- Collaborate on deployment planning
- Monitor adoption and impact
Our Team
Dr. Amara Okafor
Director
AI specialist with extensive experience in predictive modeling for disease surveillance and outbreak detection.
Dr. Rajiv Kumar
Senior Research Scientist
Expert in mobile health technologies and low-resource diagnostic tools for global health applications.
Dr. Isabella Martinez
Lead Data Scientist
Specializes in advanced analytics and machine learning for complex health datasets with a focus on predictive modeling.
Dr. James Washington
Research Engineer
Hardware and software engineer focused on developing innovative field-deployable technologies for health monitoring.
Recent Publications
Our team regularly contributes to the scientific community through peer-reviewed publications, white papers, and technical reports.
Machine Learning for Early Detection of Zoonotic Disease Spillover Events
Okafor, A., Kumar, R., et al. (2023). Journal of Applied Health Informatics, 15(3), 234-251.
View PaperLow-Cost Sensor Networks for Community Health Monitoring in Resource-Limited Settings
Washington, J., Martinez, I., & Okafor, A. (2023). Global Health Technology, 8(2), 112-128.
View PaperIntegrating Human, Animal, and Environmental Health Data: Challenges and Opportunities
Martinez, I., Kumar, R., et al. (2022). One Health Informatics Journal, 5(4), 345-362.
View PaperCollaborate With Us
The R&D Hub actively seeks collaborations with academic institutions, healthcare organizations, technology companies, and other partners interested in advancing health informatics innovation.
Collaboration Opportunities:
"Our collaboration with CHII's R&D Hub has accelerated our research and helped translate our findings into practical tools that are making a real difference in disease surveillance capabilities."
— Professor of Epidemiology, University Research Center
