T1PS1
T1PS1
Proactive Healthcare using SDoH
Proactive Healthcare using SDoH
The Problem
The Problem
Healthcare systems struggle to predict and prepare for sudden increases in patient demand, such as a 30% spike in emergency room visits during flu season or economic downturns. This reactive approach waiting for patients to arrive before scaling resources leads to overcrowded facilities, staff burnout, and delayed care. Social Determinants of Health (SDoH), like unemployment rates, literacy levels, and eviction trends, are proven indicators of these demand spikes, yet most systems lack tools to analyze this data proactively. For example, a rise in local evictions often correlates with increased hospital admissions within 2–3 months, but by the time providers notice, it’s too late to allocate staff or supplies efficiently. This gap in predictive capability costs lives and resources, demanding a shift to a data-driven, preventive strategy.
Expected Outcomes
Expected Outcomes
Develop an AI-powered predictive analytics system that uses real-time SDoH data to forecast healthcare demand spikes and enable proactive resource planning. The solution should follow these steps:
Data Collection: Aggregate SDoH data (e.g., unemployment stats from government APIs, eviction records from public databases, school attendance from local reports) in real time
Model Development: Build a machine learning model (e.g., using regression or time-series analysis) to correlate SDoH trends with healthcare demand (e.g., hospital admissions).
Risk Identification: Identify at-risk communities by outputting a risk score or demand forecast (e.g., “20% increased ER visits in ZIP code 12345 in 6 weeks”).
Resource Coordination: Create a dashboard for healthcare providers to visualize forecasts and adjust staffing or supplies, ensuring HIPAA-compliant data handling (e.g., encrypted storage, anonymized patient data).
Deployment: Integrate the system into a web interface for accessibility, linking providers with social services for community-level interventions.
Bonus (Nice to haves): • Integrate additional data sources and community feedback to enhance predictive accuracy.
Develop an AI-powered predictive analytics system that uses real-time SDoH data to forecast healthcare demand spikes and enable proactive resource planning. The solution should follow these steps:
Data Collection: Aggregate SDoH data (e.g., unemployment stats from government APIs, eviction records from public databases, school attendance from local reports) in real time
Model Development: Build a machine learning model (e.g., using regression or time-series analysis) to correlate SDoH trends with healthcare demand (e.g., hospital admissions).
Risk Identification: Identify at-risk communities by outputting a risk score or demand forecast (e.g., “20% increased ER visits in ZIP code 12345 in 6 weeks”).
Resource Coordination: Create a dashboard for healthcare providers to visualize forecasts and adjust staffing or supplies, ensuring HIPAA-compliant data handling (e.g., encrypted storage, anonymized patient data).
Deployment: Integrate the system into a web interface for accessibility, linking providers with social services for community-level interventions.
Bonus (Nice to haves): • Integrate additional data sources and community feedback to enhance predictive accuracy.
Develop an AI-powered predictive analytics system that uses real-time SDoH data to forecast healthcare demand spikes and enable proactive resource planning. The solution should follow these steps:
Data Collection: Aggregate SDoH data (e.g., unemployment stats from government APIs, eviction records from public databases, school attendance from local reports) in real time
Model Development: Build a machine learning model (e.g., using regression or time-series analysis) to correlate SDoH trends with healthcare demand (e.g., hospital admissions).
Risk Identification: Identify at-risk communities by outputting a risk score or demand forecast (e.g., “20% increased ER visits in ZIP code 12345 in 6 weeks”).
Resource Coordination: Create a dashboard for healthcare providers to visualize forecasts and adjust staffing or supplies, ensuring HIPAA-compliant data handling (e.g., encrypted storage, anonymized patient data).
Deployment: Integrate the system into a web interface for accessibility, linking providers with social services for community-level interventions.
Bonus (Nice to haves): • Integrate additional data sources and community feedback to enhance predictive accuracy.
Resource
Resource
Python and ML libraries (TensorFlow, PyTorch)
Real-time SDoH data integration (e.g., via APIs or CSV imports)
Basic web development (HTML, CSS, JavaScript)
HIPAA compliance and security basics (e.g., encryption, access controls)
Python and ML libraries (TensorFlow, PyTorch)
Real-time SDoH data integration (e.g., via APIs or CSV imports)
Basic web development (HTML, CSS, JavaScript)
HIPAA compliance and security basics (e.g., encryption, access controls)
Python and ML libraries (TensorFlow, PyTorch)
Real-time SDoH data integration (e.g., via APIs or CSV imports)
Basic web development (HTML, CSS, JavaScript)
HIPAA compliance and security basics (e.g., encryption, access controls)
T1PS2
T1PS2
Decentralized, Secure, and Patient-Controlled Health Records
Decentralized, Secure, and Patient-Controlled Health Records
The Problem
The Problem
In emergency situations, fragmented personal health records delay critical care. Disconnected systems paper-based records and siloed EHR databases force healthcare providers to waste up to 30 minutes verifying patient history, increasing risks in time-sensitive cases like trauma or stroke. Existing standards (FHIR, HL7) enable data exchange but lack seamless interoperability across providers. Centralized EHRs also limit patient control, leaving data vulnerable to breaches (e.g., 15 million U.S. records exposed in 2023). Current solutions like blockchain face compliance challenges (e.g., GDPR), leaving a gap: a secure, patient-owned health record system that ensures rapid, authorized access in emergencies.
In emergency situations, fragmented personal health records delay critical care. Disconnected systems paper-based records and siloed EHR databases force healthcare providers to waste up to 30 minutes verifying patient history, increasing risks in time-sensitive cases like trauma or stroke. Existing standards (FHIR, HL7) enable data exchange but lack seamless interoperability across providers. Centralized EHRs also limit patient control, leaving data vulnerable to breaches (e.g., 15 million U.S. records exposed in 2023). Current solutions like blockchain face compliance challenges (e.g., GDPR), leaving a gap: a secure, patient-owned health record system that ensures rapid, authorized access in emergencies.
In emergency situations, fragmented personal health records delay critical care. Disconnected systems paper-based records and siloed EHR databases force healthcare providers to waste up to 30 minutes verifying patient history, increasing risks in time-sensitive cases like trauma or stroke. Existing standards (FHIR, HL7) enable data exchange but lack seamless interoperability across providers. Centralized EHRs also limit patient control, leaving data vulnerable to breaches (e.g., 15 million U.S. records exposed in 2023). Current solutions like blockchain face compliance challenges (e.g., GDPR), leaving a gap: a secure, patient-owned health record system that ensures rapid, authorized access in emergencies.
Expected Outcomes
Expected Outcomes
Develop a decentralized personal health record (PHR) system by following these concise, layered steps:
Core Data Ownership: Implement a blockchain-based system (e.g., Ethereum) where patients hold private keys to own and manage their health data, ensuring only they grant access.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Interoperability Layer: Design REST APIs compatible with FHIR/HL7 standards, enforcing patient-set permissions (e.g., "allow ER access for 24 hours") for real-time data retrieval.
Validation: Test the system with a simulated emergency scenario, ensuring data is accessible to authorized users in under 60 seconds while remaining encrypted for others.
Bonus (Nice to haves):
A mobile app enabling patients to view, edit, and share data via NFC or Bluetooth Low Energy.
AI-driven checks to flag inconsistencies (e.g., duplicate prescriptions) in records.
An emergency override feature granting one-time access to paramedics with a patient-approved PIN.
Develop a decentralized personal health record (PHR) system by following these concise, layered steps:
Core Data Ownership: Implement a blockchain-based system (e.g., Ethereum) where patients hold private keys to own and manage their health data, ensuring only they grant access.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Interoperability Layer: Design REST APIs compatible with FHIR/HL7 standards, enforcing patient-set permissions (e.g., "allow ER access for 24 hours") for real-time data retrieval.
Validation: Test the system with a simulated emergency scenario, ensuring data is accessible to authorized users in under 60 seconds while remaining encrypted for others.
Bonus (Nice to haves):
A mobile app enabling patients to view, edit, and share data via NFC or Bluetooth Low Energy.
AI-driven checks to flag inconsistencies (e.g., duplicate prescriptions) in records.
An emergency override feature granting one-time access to paramedics with a patient-approved PIN.
Develop a decentralized personal health record (PHR) system by following these concise, layered steps:
Core Data Ownership: Implement a blockchain-based system (e.g., Ethereum) where patients hold private keys to own and manage their health data, ensuring only they grant access.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Secure Sharing: Integrate zero-knowledge proofs (ZKPs) to allow providers to verify specific data (e.g., allergies, blood type) without revealing full records, preserving privacy.
Interoperability Layer: Design REST APIs compatible with FHIR/HL7 standards, enforcing patient-set permissions (e.g., "allow ER access for 24 hours") for real-time data retrieval.
Validation: Test the system with a simulated emergency scenario, ensuring data is accessible to authorized users in under 60 seconds while remaining encrypted for others.
Bonus (Nice to haves):
A mobile app enabling patients to view, edit, and share data via NFC or Bluetooth Low Energy.
AI-driven checks to flag inconsistencies (e.g., duplicate prescriptions) in records.
An emergency override feature granting one-time access to paramedics with a patient-approved PIN.
Resource
Resource
Blockchain & Cryptography – Basics of Ethereum, zero-knowledge proofs.
AI/ML & Data Processing – OCR, NLP with TensorFlow/PyTorch.
API & Healthcare Standards – REST APIs, FHIR, HL7 integration.
Secure Dev – Mobile/backend security, error handling, testing.
Blockchain & Cryptography – Basics of Ethereum, zero-knowledge proofs.
AI/ML & Data Processing – OCR, NLP with TensorFlow/PyTorch.
API & Healthcare Standards – REST APIs, FHIR, HL7 integration.
Secure Dev – Mobile/backend security, error handling, testing.
Blockchain & Cryptography – Basics of Ethereum, zero-knowledge proofs.
AI/ML & Data Processing – OCR, NLP with TensorFlow/PyTorch.
API & Healthcare Standards – REST APIs, FHIR, HL7 integration.
Secure Dev – Mobile/backend security, error handling, testing.
