By R. Dana Barlow
October 2024 – The Journal of Healthcare Contracting
Seeing only a vast gray area instead of a solid demarcation line, some may toss analytics and artificial intelligence (AI) into a mental mixer and blend a little information technology fusion.
Not that there’s necessarily anything wrong with that.
Certainly, David Dobrzykowski, Ph.D., professor and director, Walton College Healthcare Initiatives, and senior Ph.D. program coordinator, JB Hunt Transport Department of Supply Chain Management within the Sam M. Walton College of Business at the University of Arkansas, doesn’t mind or express offense at the notion.
“My opinion is that managers would be totally okay with conflating analytics and AI,” he observed, but embarking in full academic educator mode to set the record straight.
“Analytics involves interpreting data sets to identify patterns, trends and relationships among data elements or variables,” he said. “These activities involve collecting and analyzing structured and unstructured data to provide descriptive, predictive and prescriptive insights that can be used to improve performance. At a high level, this is all based on descriptive, predictive and prescriptive statistical methods – ultimately based on identifying variance (covariance) among variables.
“AI is an advanced approach to analytics that builds on traditional data analytics by incorporating artificial intelligence methods,” he continued. “AI employs algorithms including deep learning, natural language processing (NLP) and machine learning to analyze data, automate processes and produce insights. As such, AI is an advancement on analytics because it enables machines to identify patterns, make decisions and provide human-like insights.”
Essentially, one builds out from the other.
Steve Downey, Chief Supply Chain & Patient Support Services Officer, Cleveland Clinic, and CEO & President, Excelerate, a supply chain-concentrated joint venture between Cleveland Clinic, Vizient and OhioHealth, offers a healthcare provider-based impression.
“Analytics involves analyzing data to derive conclusions, often through visual representation,” he noted. “It typically is based on historical data, such as year-to-date inventory changes. Predictive analytics extends this by using historical data to forecast future performance, like forecasting inventory levels. AI operates similarly but can autonomously make assumptions, test them and continuously learn from the results.”
Whether either is an extension or offshoot of the other or one can generate information for the other proves only that both are joined at the hip.