Healthcare Data Validation and Quality Assurance

Softree's proposed approach combines deterministic validation rules with AI-assisted anomaly detection to identify data-quality issues and support structured QA workflows. The platform is designed to validate incoming healthcare records, reconcile information across connected systems, and route suspicious records for authorized review.

The QA approach covers the application lifecycle, from requirement analysis and test data preparation to API testing, AI evaluation, performance testing, security checks, and release readiness.

• Rule-based validation: Check mandatory fields, data types, formats, duplicate records, and business rules.
• AI-assisted anomaly detection: Identify unusual data patterns and evaluate false positives, false negatives, precision, and recall against labeled test data.
• Cross-system reconciliation: Verify consistency between EHR, LIS, and insurance claims APIs.
• Functional and API testing: Validate request and response schemas, integration workflows, error handling, retries, and duplicate submissions.
• Security and performance testing: Assess role-based access, sensitive-data exposure, response times, throughput, and concurrent requests.
• Auditability and human oversight: Track validation outcomes, rule or model versions, reviewer actions, and resolution history. Flagged clinical data remains subject to appropriate human review.