Solvathon 2025

Language barriers hinder effective communication between healthcare providers and patients from diverse linguistic backgrounds, leading to misdiagnosis, treatment errors, and decreased patient satisfaction. This is particularly problematic in remote and underserved areas where access to language interpreters is limited. This solutions aims to bridge this language barries in healthcare, thereby enabling effective communication & improving patient outcomes, especially in rural areas.

Expected Outcomes:

▪Improved patient-provider communication
▪Reduced language barriers
▪Enhanced patient experience
▪Increased access to healthcare
▪Improved diagnosis and treatment accuracy

Many hospitalized patients lack real-time access to critical information about their treatment plan, their diagnostic test results, and discharge timeline and the Doctor visiting time. This often leads to anxiety and uncertainty among patients and their families. This solution will enable patients to be informed & reduce apprehensions. The in-room TV can display real-time updates on patient’s health status, treatment plan, upcoming appointments, doctor visit schedules, etc. This system can be integrated with the hospital’s EHR to provide accurate and up-to-date information.

Expected Outcomes:

▪Personalized Patient Dashboard: Display patient-specific information such as treatment plan, test results, medication schedule, and upcoming appointments.
▪Real-time Updates: Automatically update the dashboard with the latest information for patient.
▪Interactive Features: Allow patients to interact with the dashboard to access additional information or request assistance.
▪Privacy and Security: Ensure the privacy and security of patient information by implementing robust access controls and encryption techniques. By providing timely and accessible information, this solution can improve

In modern healthcare environments, medical professionals often encounter a diverse range of paper documents, including:
• Laboratory test results
• Patient charts
• Diagnostic imaging reports
• Treatment flowsheets
• Specialist consultation notes

These documents frequently arrive in varied formats, with inconsistent layouts and handwritten annotations. The manual process of transcribing these documents into electronic health record (EHR) systems is:
• Time-intensive
• Prone to human error
• Challenging for remote consultation workflows

The proposed solution should be able to:
• Automatically scan and digitize medical documents
• Extract structured data with high accuracy
• Recognize and label different types of medical information
• Generate standardized, machine-readable outputs
• Integrate seamlessly with existing electronic medical record systems
• Handle varied document layouts and handwritten text
• Maintain patient data privacy and security
• Ensure HIPAA compliance in data processing

Expected Outcomes:

1. Automated Document Conversion
   • 95%+ accuracy in text extraction
   • Automatic classification of document types
   • Structured data output compatible with major EHR platforms
2. Data Integrity and Standardization
   • Consistent data labeling
   • Preservation of original document context
   • Secure, encrypted data transmission
3. Technological Innovation
   • Easy upload of data by end user
   • Integration with existing platforms
   • Machine learning model for continuous improvement
   • Adaptive recognition of medical document formats
   • Scalable solution for different healthcare settings

Technical Solution Requirements:
• Input: Scanned medical documents (PDF, JPEG, TIFF)
• Processing: AI-powered OCR and information extraction
• Output: Structured JSON/XML with labeled medical data
• Integration: HL7 FHIR or similar medical data standards
• Security: HIPAA-compliant data handling

Elderly care nurses face challenges managing increasing patient loads, personalized care needs, and time constraints. Current tools focus on task efficiency but lack adaptive, patient-centric support. There is a critical gap in empowering nurses with AI that integrates real-time insights, lived experiences, and emotional context to enhance elderly care.

Expected Outcomes:

▪Develop a Clinical Agentic AI assistant designed for elderly care, enabling nurses to access dynamic, empathetic, and data-driven insights.
▪Improve care quality, reduce nurse workload, and foster personalized engagement for elderly patients.
▪NLP for nuanced patient communication, real-time data synthesis, care prioritization, and cultural adaptability.
▪Enhances patient outcomes, nurse satisfaction, and care efficiency in elderly healthcare settings.

International patients at Apollo Hospitals face challenges accessing care due to language barriers, cultural differences, and limited familiarity with the healthcare system. Current solutions lack real-time, culturally adaptive conversational support. A gap exists for a multilingual, context-aware AI bot to streamline patient communication and care navigation.

Expected Outcomes:

▪Enhance accessibility to care for international patients, reduce administrative workload, and improve patient satisfaction
▪NLP-powered multilingual capabilities for over 10 international languages

a) Phase 1 – English, Spanish, Indonesian & Swahili
▪Real-time translation and culturally contextual responses
▪Integration with appointment scheduling, billing, and care queries
▪Patient education and navigation support tailored to local norms
Promotes inclusivity and accessibility, improves patient trust and satisfaction, and positions Apollo Hospitals as a leader in international healthcare services.

Wearable devices for remote patient monitoring are growing in demand, particularly in chronic disease management, fitness tracking, and post-operative care. However, one significant challenge lies in their high power consumption, leading to:

1. Frequent Battery Replacements or Recharging:
   1. Interrupts continuous monitoring.
   2. Increases patient burden, especially for the elderly or those with limited mobility.
   3. May result in gaps in critical data collection.
2. Environmental Concerns:
   1. Increased battery production contributes to e-waste and environmental degradation.
   2. Power-hungry devices result in inefficiencies, undermining sustainability goals.
3. Scalability Issues in Remote Monitoring:
   1. Large-scale deployment of high-power wearables in rural or underserved areas is difficult due to limited access to charging infrastructure.
4. Patient Compliance:
   1. Patients may stop using the device if it is inconvenient to recharge frequently.

Expected Outcomes: Low-Power Wearables with Multimodal Transmission

Key Features:

Use machine learning to predict power consumption patterns and optimize battery usage by adapting sampling rates or transmission intervals.

Low Power Design:

Optimize hardware (e.g., low-power microcontrollers and sensors).

Use power-efficient communication protocols like BLE (Bluetooth Low Energy), LoRaWAN, or Zigbee.

Multimodal Data Transmission:

Dynamically choose transmission modes based on power availability, data type, and priority (e.g., sending only essential data over cellular while bulk data is uploaded via Wi-Fi when available).

Energy Harvesting:

Incorporate energy harvesting technologies like solar panels, thermoelectric generators (using body heat), or kinetic energy (motion-based).

AI-Driven Power Management:

Use machine learning to predict power consumption patterns and optimize battery usage by adapting sampling rates or transmission intervals.