Cornerstone AI

Healthcare data is messy. We all know it. Datasets are often riddled with errors, inconsistencies and missing values, requiring data science teams to spend countless hours cleaning data before analyzing it. The Cornerstone AI assistant is purpose-built to clean RWD. Its proprietary machine learning models automatically generate unique clinically relevant data cleaning rules for every dataset. The Cornerstone AI assistant learns the structure and connections across data sources, harmonizing the structure. ML models then identify and correct errors, which are applied automatically or guided through our UI for optimal balance of scalability and nuance.

https://cornerstoneai.com/

2-10 employees

Growth Trajectory

Cornerstone AI's growth potential lies in expanding its AI-driven data cleaning and standardization techniques, driven by partnerships with companies like Diaceutics and Komodo Health, along with continuous algorithm improvements based on user feedback. The company's focus on supporting more clinical data types, terminologies, and a wider range of patient datasets signals a move towards broader market reach and product development in healthcare data solutions.

Technical Challenges

Ensuring accuracy and reliability of AI/ML models.

Handling diverse data formats and coding systems.

Developing models for every table, field, and data point to identify outliers and flag errors.

Standardizing and harmonizing diverse healthcare data sources.

Tech Stack

AIMachine Learning (ML)High-dimensional AI/ML modelsNatural Language Processing (NLP)APIs

Team Size

Data Science Teams

Clinical Experts

Real World Data Strategy Teams

Life Sciences BD Partnership Lead

Key Risks

Maintaining accuracy and explainability of AI/ML models in data cleaning processes.

Adapting to diverse data formats, coding systems, and the rapid growth of healthcare data.

Stringent rules and regulations around data security (HIPAA) may limit the scope of data cleaning and standardization.

Competition from traditional handcrafted data review methods or other AI-driven solutions.

Opportunities

Further development of AI/ML models to enhance data quality scoring and error detection methods.

Expansion of support for more clinical vocabularies and coding systems to broaden the platform's applicability.

Offering additional data cleaning and analysis services to reach more healthcare organizations and data types.

Leveraging partnerships with EMR vendors and data analytics platforms to integrate their solution into existing data workflows.

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