Artificial intelligence has been touted as a healthcare analytics cure-all that will impose order on massive data streams, extract essential insights, and generate predictions with pinpoint accuracy. But is there evidence that AI and predictive analytics have begun to deliver real value?
Author: Colin Beam, PhD
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NEJM Catalyst: analysis of healthcare disparities for health insurer
NASHVILLE, Tenn., June 16, 2022 — Ursa Health’s analytics development platform, Ursa Studio, enabled an analysis of healthcare inequities in utilization and spending published on June 2 in NEJM Catalyst. The article highlights cost and quality insights into the commercially insured population of Blue Cross and Blue Shield of North Carolina (Blue Cross NC) resulting from Blue Cross NC’s collaboration with Ursa Health.
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Making sense of advanced analytics terminology
For years, we’ve been reading headlines touting the power of artificial intelligence and machine learning algorithms, such as deep learning, to transform big data into actionable insights. Sounds impressive, right? But what does it all actually mean? Some argue these terms have strayed far from their original definitions, while others have criticized their misguided or cynical application.
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Why causality is central to questions of algorithmic bias
Healthcare organizations increasingly rely on algorithms to guide decisions regarding patient care. If bias exists in the algorithms, organizations run the risk of treating patients unfairly. Although evidence of such bias has been documented, the good news is that steps can be taken to reduce bias.
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A closer look at Ursa Studio’s Advanced Analytics suite
The first step and arguably greatest challenge in healthcare data analysis is transforming incalculable quantities of messy raw data into an asset capable of producing measures that are timely, accurate, and clinically relevant. Successful completion of this first step will produce immediate insights from even simple descriptive statistics, such as counts, proportions, and averages.
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UCLA Health: overcoming racial bias in predictive algorithms
Algorithmic bias—what it is, who it impacts, what it causes, and how it is best overcome—is a topic of increasing interest in healthcare.
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When better is better than best: predictive model development
The field of machine learning (ML), a subset of artificial intelligence in which computer algorithms use statistics to find patterns in data that can predict future outcomes, is undergoing rapid development. New methods, many of which are available through open source tools, are highly flexible in that they can automatically approximate complex, nonlinear functional relationships between predictors and response variables.