ClinicLocator ZW

Inspiration

The inspiration behind ClinicLocator ZW comes from a deep and personal understanding of the challenges many Zimbabweans face when seeking healthcare. In our communities, it’s not uncommon to travel long distances to a clinic, only to wait several hours without being seen or worse, to be told that the medication you need is out of stock. These experiences are not only frustrating but life-threatening for vulnerable groups like pregnant women, children, and the elderly.

Growing up in a low-income family, we have personally felt the effects of limited access to timely healthcare. We've seen relatives suffer simply because they couldn’t afford to "clinic-hop" in search of available treatment or medicine. These real, everyday struggles sparked a desire to create a solution that could make a tangible difference.

During the COVID-19 pandemic and recent cholera outbreaks, the lack of transparent, real-time information about facility congestion and resource availability became even more obvious. This project was born from a belief that technology, when applied with empathy and context, can solve real problems even in low-resource settings.

We were motivated to use our coding skills and data knowledge not just to build another app, but to create something with the potential to save lives, empower communities, and help health officials make smarter decisions.

ClinicLocator ZW is our answer to a system that’s often slow, blind, and unpredictable a step toward a healthcare future that’s smarter, fairer, and more accessible for all Zimbabweans.

What it does

ClinicLocator ZW is a smart, web-based platform that helps Zimbabweans quickly find the nearest and most efficient healthcare facility by showing:

1. Real-Time Wait Times – It tells patients how long they can expect to wait at nearby clinics and hospitals, so they don’t waste time standing in long queues.

2. Live Drug Availability – It shows which clinics currently have essential medicines in stock (like antibiotics, insulin, or paracetamol), so patients don’t have to visit multiple places searching for treatment.

3. Location-Based Search – Using geolocation, the platform suggests the best nearby health facility based on where the user is, how crowded it is, and what medications are available.

4. Predictive Analytics with Machine Learning – The system uses past data, day of the week, and current trends to estimate future wait times — helping users plan their visits better.

5. Health Insights Dashboard – Health officials get access to live data on facility usage, medicine shortages, and patient flow — enabling smarter decision-making and faster response during outbreaks or crises.

   How we built it

l built ClinicLocator ZW using Bolt AI, a low-code/no-code platform that accelerates the development of AI-powered web applications with minimal infrastructure overhead.

Here’s how l put the system together:

1. Frontend (User Interface)

Built using Bolt AI’s UI Builder, which allows rapid creation of responsive pages. Key components include: A live map to locate nearby clinics. Search and filter tools (by location, radius). Real-time wait time and drug availability cards.

1. Machine Learning (Wait Time Prediction) We integrated Bolt AI’s ML engine to: Train models on historical clinic queue data. Predict estimated wait times based on day, time, and patient flow patterns. Web and ERP scraping and crawling to find authentic and verified data for the system to learn.

2. Backend (Data Management) Bolt AI’s Workflow Engine handles logic for:

User data submission (crowdsourced reports). Real-time clinic staff updates.

1. Analytics Dashboard Using Bolt’s built-in data visualizations to create: A live dashboard for health authorities showing congestion trends, drug stock levels, beds available, hospitals machines or equipment available, staff and specialists present.

2. Real-Time Features Leveraged Bolt AI’s API and database tools to implement: Real-time updates using database triggers. Notifications for users when drug stock changes or a facility gets overcrowded.

Challenges we ran into

1. Lack of Real-Time Health Data

In Zimbabwe, there is no centralized, open-access API for live health facility data like patient volumes or drug stock levels.

1. Connectivity & Device Limitations Many users in rural areas have limited internet or only basic mobile phones.

   1. Privacy & Ethics

Collecting health-related data, even anonymously, raises serious concerns about data privacy. l had to ensure our system design follows ethical principles and prepares for compliance with data protection policies if scaled.

Accomplishments that we're proud of

1. Built a Fully Functional MVP in a Short Time • Despite tight hackathon deadlines, we developed a working prototype that integrates real-time wait time display, drug availability tracking, and location-based hospitals search.
2. Implemented Machine Learning for Wait Time Prediction • Successfully trained and deployed a machine learning model that uses historical, web scraping, ERP crawling and live data to predict clinic wait times, providing users with smart, data-driven recommendations.
3. Designed a User-Friendly Interface Optimized for Low-Bandwidth Areas • Created an intuitive, mobile-responsive design that works smoothly even with limited internet connectivity a vital factor for reaching underserved rural communities.
4. Enabled Real-Time Updates via Crowdsourcing and Clinic Staff Inputs • Developed a system where both patients and hospitals personnel can update wait times and drug stock levels live, improving data accuracy and community engagement.
5. Built a Health Dashboard for Officials • Delivered a prototype analytics dashboard that visualizes key metrics such as hospitals congestion and drug shortages or availability, which can help health authorities make faster, informed decisions.

What we learned

1. The Power of Technology to Solve Real Problems • I learned how technology, especially AI and real-time data analytics, can be harnessed to tackle everyday challenges faced by communities — even in low-resource settings like Zimbabwe.
2. Importance of User-Centered Design • Building for real users with limited internet and device access taught me to prioritize simplicity, accessibility, and usability in design to make a meaningful impact.
3. Challenges of Data Scarcity and Quality • I realized how crucial good quality and real-time data are for machine learning and analytics and the difficulties that arise when such data isn’t readily available.
4. Effective Time Management and Prioritization • Working under hackathon time constraints sharpened my ability to focus on building a Minimum Viable Product (MVP) that delivers real value before adding extra features.
5. Collaboration and Resourcefulness • I learned the importance of leveraging available platforms like Bolt AI to accelerate development and overcome infrastructure limitations without building everything from scratch.
6. Ethical Considerations in Health Tech • I gained awareness of the ethical and privacy responsibilities when handling sensitive health data and the need to build trust with users through transparent and secure systems.

What's next for ClinicLocator ZW

To improve or integrate the following:

1. Real-Time Infrastructure Upgrades WebSocket Integration: Live data streaming for instant updates across all facilities Edge Computing: Deploy servers in each province for <100ms response times 5G Network Optimization: Ultra-low latency for critical emergency communications Offline-First Architecture: System works even without internet connectivity
2. Advanced AI & Machine Learning Predictive Disease Outbreak Detection: Early warning for cholera, malaria, COVID variants Resource Optimization Engine: 48-72 hour demand forecasting Computer Vision Triage: Smartphone-based patient assessment Natural Language Processing: Multi-language support (English, Shona, Ndebele)
3. Patient-Centric Digital Health Digital Health Passport: QR codes with complete medical history Telemedicine Platform: Video consultations with specialists Smart Appointment System: Real-time booking with SMS confirmations Medication Reminder System: Automated adherence tracking
4. Emergency Response Excellence Drone Medical Network: 30-minute supply delivery to remote areas Satellite Communication: Disaster-proof backup systems AI-Powered Mass Casualty Management: Automatic resource reallocation Ambulance GPS Integration: Optimal routing and dispatch
5. Government & International Integration National ID System: Automatic patient identification WHO Compliance: HL7 FHIR standards and disease surveillance Insurance Automation: Real-time claims processing Research Platform: Clinical trial management and epidemiological studies

Built With

• react
• typescript
• vite