CareAdvocate: An AI-Powered Patient Case Management Platform for Personal Injury
CareAdvocate is a multi-sided, AI-driven mobile platform designed to significantly enhance patient compliance, streamline communication, and generate objective data within the personal injury (PI) ecosystem. Our goal is to leverage advanced AI to transform inefficient, human-intensive processes into automated, data-driven workflows, benefiting patients, legal teams, and medical providers.
Core Technical Architecture (High-Level):
The platform will primarily be a cloud-native, microservices-based mobile application (iOS/Android) with a robust backend, designed for scalability and HIPAA compliance.
- Mobile Frontends: Intuitive user interfaces tailored for patients, attorneys, and medical providers.
- Backend Services: A suite of APIs and microservices hosted on a compliant cloud platform (e.g., AWS, Azure, GCP) managing user authentication, data storage, communication routing, and AI model serving.
- Database(s): Secure, scalable, and compliant databases (likely a mix of relational for structured data and NoSQL for flexible/large datasets) to store PHI and operational data.
- Security & Compliance Layer: End-to-end encryption (data in transit and at rest), robust access controls (RBAC), audit logging, and adherence to HIPAA (including de-identification protocols) and other relevant data privacy regulations will be fundamental. We’ll explore tokenization and secure data clean rooms for de-identified data sharing.
Key AI & Automation Capabilities:
- Personalized Patient Engagement Engine (NLP/Generative AI):
- Dynamic Question Generation: AI (likely an LLM combined with rule-based systems) generates daily check-in questions for patients (pain levels, mood, treatment adherence) based on their individualized treatment plans, previous responses, and scheduled appointments. This moves beyond static questionnaires.
- Sentiment Analysis: NLP models analyze patient textual inputs (mood, comments) to detect shifts in sentiment, identifying potential psychological distress or non-compliance risk factors.
- Gamification Logic: Backend logic manages point accrual for compliance (e.g., perfect attendance, timely data entry, outcome measure completion) and manages gift card redemption.
- Intelligent Communication & Workflow Automation:
- AI-Powered Q&A Chatbot: An in-app chatbot leveraging NLP provides immediate, accurate answers to common patient questions based on a curated knowledge base, reducing direct staff inquiries.
- Proactive Alert System: AI monitors patient compliance data (e.g., missed appointments flagged from integrations, lack of daily check-ins) and triggers automated alerts to attorneys (e.g., “Patient X missed PT session”) or providers (e.g., “Patient Y completed treatment plan”).
- Intelligent Routing: For questions the AI cannot answer, it routes the query to the appropriate human staff member (e.g., paralegal for legal questions, medical assistant for clinical) based on predefined rules and context.
- Objective Data Aggregation & Analytics (Machine Learning/Predictive AI):
- Continuous Data Capture: Systematically collects structured and unstructured data points on patient progress, pain, mood, treatment adherence, and outcome measures over time.
- Compliance Scoring: ML models analyze patient engagement patterns to generate compliance scores, identifying individuals at risk of non-adherence.
- De-identified Dataset Generation: A robust process for de-identifying collected PHI to create large, anonymized datasets for analytical purposes (e.g., for insurance companies). This requires strict adherence to HIPAA Safe Harbor or Expert Determination.
- Predictive Insights (Future): ML models can analyze aggregated data to identify trends in recovery timelines, predict potential complications, or highlight inconsistencies that might indicate atypical recovery patterns (for internal use by funders, or for de-identified insights to insurers).
Data Ingestion & Integration:
- Manual Patient Input: Primary data source via mobile app.
- Third-Party Integrations (Future/MVP consideration): APIs for seamless, bidirectional data exchange with:
- EHR Systems: To pull appointment schedules, treatment plans, and potentially push compliance data back to medical records. (Leveraging FHIR standards where possible).
- Legal Case Management Software: To synchronize case status, legal milestones, and potentially push patient compliance reports into legal files.
Monetization & Value Drivers (Technical Perspective):
- SaaS Subscriptions: Standard recurring revenue model for attorney and provider access to the platform’s features and data.
- API/Data Access Fees: Settlement funders pay for access to the platform’s competitive bidding system, which benefits from the rich compliance data. Future revenue from secure, de-identified data access for analytics by insurers.