Justin Nicols

Founder & CEO

A leading industry expert in data analytics technology and has an extensive background in corporate finance and investment banking. Prior to founding Sift, Justin served on the executive team of venture backed ad technology company, and an e-commerce technology company.

The $265 Billion Problem: How Unified Data Unlocks Revenue Intelligence in Healthcare

Discover how unified clinical and financial data can solve healthcare’s $265B revenue cycle inefficiency problem with actionable AI-driven insights.

Published on: January 7, 2025
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Want To Add AI to Your Revenue Cycle? Data Intelligence is the First Step.

So you want to add AI to your revenue cycle? You have to start by establishing a solid foundation of data intelligence. This comes from normalizing and organizing payments data in a way that provides actionable insights.

Published on: November 3, 2021
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3 Reasons Why The Future Of Healthcare Payments Is Not Automation.

RPA Follows Rules. Machine Learning Generates Intelligence. Your revenue cycle will benefit from both. Learn about the limitations of RPA and how machine learning provides can have a more meaningful impact on the healthcare revenue cycle.

Published on: May 11, 2021
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Automation Does Not Mean Data Science

The term “automation” can refer to any number of automatic processes within the revenue cycle workflows. But, it doesn’t necessarily refer to the use of data science. Just because a process is “automated” doesn’t mean predictive analytics or any data science is being applied.

Published on: December 10, 2019
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Every Revenue Cycle Improvement Matters

Improving the revenue cycle means minimizing costs and increasing collections, from both patients and payers. Even on a small scale, artificial intelligence has a meaningful impact on healthcare payments and operations.

Published on: October 1, 2019
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Your Data Is Too Messy For Data Science

In healthcare payments, where data flows from multiple systems and standards are a moving target, data can be pretty filthy. This means mismatched formats, errors and inconsistencies. Having clean data is often the biggest roadblock to being able to reap the benefits of data science.

Published on: July 9, 2019
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