Customers achieve significant productivity gains by accelerating data cleaning from many weeks to a single day through rapid quality assessments and data pipeline improvements

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This post is an interview with Richard KWOCK, Principal Data Scientist

The move comes upon the completion of a successful pilot program in which Cornerstone's proprietary algorithms strengthened the fidelity of Komodo's Lab Results, a product available within MapEnhance.

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This post is an interview with Clara Oromendia, Co-founder and VP of Data Science & Product Strategy

Errors in healthcare data can be hard to spot in the context of a complex dataset with many tables, fields, and patients. But sometimes the problems are so glaring that it feels offensive that they have not been remedied. We describe three common offensive data quality issues and discuss their implications. We use the word offensive in jest, but when you consider the stakes of healthcare data used for conducting research that inform on patient outcomes, it can be genuinely concerning.

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This post is an interview with Eugene Wang, Head of Product Engineering.

If you're a healthcare data scientist or analyst, chances are you've come across SNOMED CT. But what exactly is SNOMED CT, and why is it so important in the world of healthcare data? In this comprehensive guide, we'll delve into the intricacies of this terminology, exploring its purpose and other details.

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This post is an interview with PJ Allen, Director of Data Science.

Cornerstone AI is publishing a series of Q&As with team members to provide more information and context on their role at Cornerstone AI, as well as their professional background. This post is an interview with co-CEO Mike Elashoff.

In the ever-evolving field of healthcare, it is vital for healthcare data scientists and analysts to remain informed about the most up-to-date standards for medical codes. In this blog post, we will focus on two widely used diagnostic code systems called ICD-9 and ICD-10, which are highly prevalent in today's healthcare data.

Welcome, healthcare data scientists and analysts! If you're just starting to work with real-world data, you've come to the right place. This guide aims to provide you with step-by-step instructions on how to prepare healthcare data for analysis. We'll dive into understanding the data's structure, performing necessary pre-processing tasks, and identifying potential errors. So let's get started!

Mike Elashoff and Viraj Naranayan attended ISPOR 2024 in Atlanta in May, where conference participants discussed real-world data standardization, ensuring representative datasets and the challenges of improving data quality.

Cornerstone’s Enterprise platform launches to unlock the power of clinical data in a fraction of the time of traditional methods

San Francisco-based company Cornerstone AI, has launched a solution that automatically characterizes, harmonizes and cleans healthcare data in a fraction of the time taken by traditional methods. The company also announced it has raised $5 million in seed funding…