Cornerstone AI Data Cleaning Platform

Cornerstone AI Data Cleaning Platform is an AI-powered solution designed to dramatically reduce the time required to clean real-world clinical datasets while improving data quality and providing full traceability of every change made. The platform is built for life sciences teams working with EHR, registry, digital health, claims, clinical trial, and sensor data — supporting datasets ranging from fewer than 100 patients to more than 100,000 patients across any therapeutic indication.

At its core, Cornerstone AI uses high-dimensional AI and machine learning models to scan every table and data point in a dataset, infer structure and relationships, and automatically generate clinically relevant data cleaning rules. The result is a harmonized, standardized, analysis-ready dataset delivered in days, accompanied by explainable rules and a regulatory-grade audit trail so data scientists and clinical experts alike can have full confidence in the output.

How the Workflow Works

• Bring Raw Data: Import raw datasets — single or multiple sources — via the web user interface or API. No pre-processing is required before ingestion.
• Structure, Standardize, and Detect Errors: Cornerstone AI algorithms automatically parse the dataset to understand data structure, standardize values to clinical dictionaries, and detect errors and anomalies. High-dimensional AI/ML models learn patterns across the entire dataset to generate unique, clinically relevant cleaning rules tailored to each dataset and patient population.
• Review and Annotate: A human-in-the-loop workflow allows users to review flagged errors and standardization results in an intuitive web application. Detailed explanations are provided for every flagged item, but users retain final decision-making authority over all corrections.
• Improve Algorithms and Track Changes: User corrections feed back into the algorithms, continuously improving them over time. All changes are logged to a full audit trail for complete traceability.
• Export Clean Data: The cleaned, harmonized dataset — along with explainable rules and the audit trail — can be exported and immediately used for downstream analysis.

Data Profiling Capabilities

• Automatic Structure Detection: The platform intelligently parses all fields in a dataset, determines relationships between fields, and identifies whether each field represents a date, a unit, a medical code, or another data type.
• Multi-Source Harmonization: Multiple datasets can be automatically compared and stacked together without manual mapping, enabling combined analysis across data sources.
• Data Quality Score: From the moment data is imported, the platform provides a visualization of data quality across the entire dataset and within individual tables and records, helping teams prioritize the areas with the most issues.

Data Cleaning Capabilities

• Error Identification and Correction: Every data point is modeled to detect errors including unit mismatches (e.g., inches instead of centimeters), date swaps, biologically implausible values, and cross-field inconsistencies. Specific error types addressed include biologically implausible data, logical inconsistencies, misordered dates, transcription errors (where data is recorded in the wrong field), and missing data.
• Text and Code Standardization: The Standardization Module automatically identifies fields that would benefit from standardization and applies dictionary matching and natural language processing (NLP) to assign accurate codes. Supported standards include ICD-10, SNOMED, CPT, LOINC, CDISC, and others. The platform also provides crosswalks between coding systems (e.g., ICD-9 to ICD-10) and leverages hierarchical dictionary structures to aggregate granular codes into clinically meaningful categories.
• Missing Data Imputation: Optionally, missing data can be intelligently filled in using imputation methods specialized by data type. Accuracy metrics allow users to balance completeness with precision, and data can be exported with or without imputations applied.

Data Integrity and Compliance

• HIPAA Compliance: Cornerstone AI is fully HIPAA compliant and adheres to strict data use agreements. The platform undergoes regular penetration testing and maintains a continually updated security program covering physical, administrative, and technical safeguards.
• Audit Trail: All algorithmic actions and user-specified changes are captured in a regulatory-grade audit trail with detailed change logs and explanations, available for export at any time.
• Data Privacy: Customer data is never kept, aggregated, or resold. Data is used exclusively for the benefit of the submitting organization.

Integration and Deployment Options

• Web UI: Review data quality reports, adjudicate discovered errors, and configure algorithm settings through a browser-based interface accessible to both data scientists and clinical experts.
• API: For teams requiring real-time data profiling, error detection, and standardization, Cornerstone AI offers APIs that can be integrated directly into existing data pipelines.
• Deployment Flexibility: The platform is a secure, cloud-first solution but can also be deployed within a customer's own cloud environment for organizations that require data to remain behind their firewall.

Cornerstone AI supports a broad range of therapeutic areas — including Rheumatoid Arthritis, Oncology, Alzheimer's Disease, Infertility, and COVID, among others — and has demonstrated success with real-world data, patient registries, and Phase II, III, and IV clinical trial datasets.