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Case Study: Belongly

Service

AI-Driven Matching

Industry

Healthcare

Location

USA

Website

belongly.com

Case Study: Belongly

Service

AI-Driven Matching

Industry

Healthcare

Location

USA

Website

belongly.com

Introduction

A leading mental health services provider needed to streamline how individuals connect with suitable therapists. Traditional methods - manual matching, form-based submissions, and siloed data - proved time-consuming for both patients and practitioners.

Seeking to accelerate user onboarding and improve matching accuracy, the provider partnered with Techjays to create an AI-powered solution that balances advanced data processing, generative AI, and strict healthcare compliance requirements. This whitepaper outlines the approach, technology, and key considerations in building a HIPAA-aligned platform that delivers immediate, relevant therapist recommendations.

Business & Regulatory Landscape

Mental Health Access Challenges

High Demand: Rising mental health needs require scalable solutions that can handle large user volumes.

Complex Matching Variables: Users differ in location, therapy type, insurance coverage, and personal preferences. Manual processes often fail to keep pace or capture sufficient nuance.

Regulatory Environment

HIPAA Compliance: The platform handles protected health information (PHI). Every data flow, storage mechanism, and user interaction must meet HIPAA security and privacy rules.

FDA & Healthcare AI: Although not classified as a regulated medical device, the AI system’s potential clinical impact calls for FDA-inspired risk management and thorough documentation for future scalability.

Key Requirements for the AI Platform

  1. High Accuracy: Deliver consistently relevant and personalized recommendations.
  2. Data Governance: Ensure PHI is protected through de-identification, role-based access, and robust audit trails.
  3. Scalability: Support thousands of concurrent user sessions, especially during peak intake periods.
  4. Explainability & Trust: Provide rationale behind AI-driven recommendations without exposing sensitive or proprietary model details.
  5. Bias Mitigation: Detect and correct any demographic or specialty-based biases to maintain equitable care.

Architecture & Approach

The solution combined generative AI modules with a secure, HIPAA-compliant cloud environment. 

The high-level steps included:

Data Ingestion & Standardization

  • Therapist Information: Sourced from various databases; cleaned, structured, and anonymized via AI-driven data validation.
  • Patient Inputs: Collected through secure, HIPAA-compliant endpoints. User consents govern data usage and retention.

Generative AI Pipeline

  • Synthetic Data Creation: Generative models help fill gaps in inconsistent therapist data, creating uniform, searchable profiles while minimizing exposure of raw PII.
  • Multi-Layer Matching: Two or more large language models (LLMs) work in sequence to narrow down potential therapist matches from extensive lists, then refine to the top candidates based on user-specific criteria.
  • Explainability Module: A specialized LLM provides high-level justifications for recommendations—balancing transparency with the need to protect AI logic and private information.

Security & Governance

  • Encrypted Data Flows: AES-256 at rest, TLS 1.2+ in transit, ensuring end-to-end protection.
  • Role-Based Access Control (RBAC): Strict privileges limit exposure to PHI, with multi-factor authentication where feasible
  • Audit & Logging: Immutable logs capture each data query and model interaction, supporting future compliance audits

Ongoing Model Governance

  • Bias Detection: Periodic audits reveal patterns in recommendations that may skew demographic or specialty preferences; iterative retraining corrects these biases.
  • Performance Tuning & Version Control: Each new model version undergoes offline testing for compliance, accuracy, and user experience metrics before deployment.

Security & Governance

  • Encrypted Data Flows: AES-256 at rest, TLS 1.2+ in transit, ensuring end-to-end protection.
  • Role-Based Access Control (RBAC): Strict privileges limit exposure to PHI, with multi-factor authentication where feasible
  • Audit & Logging: Immutable logs capture each data query and model interaction, supporting future compliance audits

Ongoing Model Governance

  • Bias Detection: Periodic audits reveal patterns in recommendations that may skew demographic or specialty preferences; iterative retraining corrects these biases.
  • Performance Tuning & Version Control: Each new model version undergoes offline testing for compliance, accuracy, and user experience metrics before deployment.

Generative AI Highlights

Data Cleaning & Consolidation

  • AI-Assisted Standardization: Different therapist directories often have varied data structures and inconsistencies. Generative AI unifies these records into coherent, standardized formats.
  • Synthetic Summaries: To maintain privacy, the system may generate short descriptive texts that convey essential therapist details without exposing personal identifiers.

Multi-Stage Matching

  • Parallel LLM Evaluations: One LLM prioritizes relevant factors (location, therapy niche, insurance, etc.), while a second LLM refines the shortlist. This layered design optimizes both accuracy and speed.
  • Contextual Filters: If a user’s situation indicates specific needs (e.g., language preference, modality), generative AI seamlessly factors those into the final recommendation—reducing the chance of irrelevant results.

Explainability & Grounding

  • Justification Outputs: A dedicated module condenses the AI reasoning into user-friendly bullet points or short statements, building trust without revealing the entire algorithmic workflow.
  • Guardrails: Real-time checks ensure no PHI or unauthorized internal data is inadvertently disclosed in explanations.

Bias & Ethical Oversight

  • Regular Model Audits: Tools and manual reviews identify potential biases. If certain user demographics are under-served, or if certain therapists are consistently overlooked, retraining cycles correct model drift.
  • Policy Frameworks: The platform integrates with established ethical guidelines, ensuring that any new feature or data source respects confidentiality and fairness.

Security & Privacy Measures

Holistic Encryption

  • At Rest: Secure storage on HIPAA-compliant cloud infrastructure using AES-256.
  • In Transit: TLS encryption for all inbound and outbound data transfers.

Access Controls

  • Granular Permissions: Only authorized personnel and system components can view or manipulate sensitive data.
  • Audit Logs: Comprehensive logs track each data access event. Anomalies trigger automated alerts to security teams.

Incident Response

  • Predefined Protocols: Policy documents specify immediate steps if a breach or suspicious activity is detected.
  • Transparent Reporting: While not publicly disclosed, the process includes timely notifications to impacted parties if legally required.

Clinical Outcomes & Insights

  • Accelerated Matching: The streamlined, AI-driven approach reduces the manual overhead of matching, improving the speed at which patients can find suitable care.
  • Higher Satisfaction: Early feedback suggests patients experience fewer mismatches and re-referrals, enhancing their overall journey.
  • Therapist Efficiency: Clinicians receive referrals that better match their specialties, saving time on intake processes and allowing greater focus on patient treatment.

ROI & Business Impact

  • Operational Savings: Automated data cleaning and matching reduce reliance on manual processes, cutting labor costs and administrative overhead.
  • Scalability: As more therapists join and patient volume grows, the AI pipeline dynamically scales while maintaining performance.
  • Brand Differentiation: Demonstrating advanced AI capabilities and HIPAA-grade compliance elevates market standing in a competitive mental health sector.

Conclusion

This whitepaper demonstrates how a healthcare-focused AI solution can combine generative AI with stringent security and privacy protocols to deliver immediate, personalized therapist recommendations. By emphasizing HIPAA compliance, data lifecycle governance, and robust generative AI guardrails, the mental health platform described here stands as a model for future innovations in healthcare.

Techjays is proud to have been the driving force behind this transformation—merging cutting-edge AI with proven data governance practices. As healthcare continues to embrace digital transformation, our experience ensures that organizations in regulated industries can deploy intelligent, scalable solutions that respect user privacy and meet regulatory demands.

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