The artificial intelligence models also obtained a 30% to 35% increase in positive patient outcomes, Bennett said.
"And we determined that tweaking certain model parameters could enhance the outcome advantage to about 50% more improvement at about half the cost, he said.
The cost of diagnosing and treating a patient was $189, compared to the treatment-as-usual cost of $497, Bennett said.
"The framework here easily outperforms the current treatment-as-usual, case-rate/fee-for-service models of healthcare," Bennet said.
The researchers used mathematical modeling frameworks, known as "The Markov Decision Processes" and "Dynamic Decision Networks" to perform the tests. The computer modeling considered all the different possible sequences of actions and effects of medical treatment in advance, "even in cases where we are unsure of the effects," Bennett said.
"Modeling lets us see more possibilities out to a further point, which is something that is hard for a doctor to do," Hauser added. "They just don't have all of that information available to them."
Previous work by Hauser and Bennett had shown how machine learning can determine the best treatment at a single point in time for an individual patient. This is the first time they used the computer modeling with a large group of patients.
Lucas Mearian covers storage, disaster recovery and business continuity, financial services infrastructure and health care IT for Computerworld. Follow Lucas on Twitter at @lucasmearian or subscribe to Lucas's RSS feed. His e-mail address is firstname.lastname@example.org.
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