AI found better at diagnosing, treating patients, than doctors

AI can think like a doctor, but faster and with more information, Indiana U. researchers find

Applying the same technologies used for voice recognition and credit-card fraud detection to medical treatments could cut costs and improve patient outcomes by almost 50%, according to new research.

The research by Indiana University found that using patient data with machine-learning algorithms can drastically improve both the cost and quality of health care through simulation modeling.

The computer models simulated numerous alternative treatment paths out into the future and continually planned and re-planned treatment as new information became available. In other words, it can "think like a doctor," according to the university.

This is not the first time artificial intelligence has been brought to bear on healthcare.

Last year, IBM announced that its Watson supercomputer would be used in evaluating evidence-based cancer treatment options for physicians, driving the decision-making process down to a matter of seconds. The Watson supercomputer was first offered to Cedars-Sinai's Samuel Oschin Comprehensive Cancer Institute in Los Angeles. Later that year, Watson was brought in to help Memorial Sloan-Kettering Cancer Center physicians diagnose and treat cancer patients.

The new research at Indiana University was non-disease-specific -- it could work for any diagnosis or disorder, simply by plugging in the relevant information. The research is aimed at addressing three issues related to health care in the U.S.: Rising costs expected to reach 30% of the gross domestic product by 2050; quality of care where patients receive the correct diagnosis and treatment less than half the time on a first visit; and a lag time of 13 to 17 years between research and practice in clinical care, the university said.

The research was performed by computer science assistant professor Kris Hauser and doctoral student Casey C. Bennett. The researchers used 500 randomly selected patients for the computer simulations.

The two researchers had access to clinical data, demographics and other information from 6,700 patients kept by the Centerstone Research Institute, a nonprofit provider of community-based behavioral health care. From 60% to 70% of the patients had major clinical depression diagnoses but also had chronic physical disorders including diabetes, hypertension and cardiovascular disease, which were used in the simulations.

Using real patient data, the researchers compared actual doctor performance and patient outcomes against computer decision-making models.

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 lmearian@computerworld.com.

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Tags IBMhealthcare IThealth caresoftwareIndiana Universityvoice recognitionindustry verticalshigh performance computing

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1 Comment

Barry W Gendelman, M.D.

1

If you want a fair comparison, the computer should be financially liable for outcomes less than patient expectations and consider potential "malpractice" claims in it's decision making and cost analysis. I can see the personal liability vultures (sorry, I meant attorneys) drooling in their scotch now.
If you want to drastically cut medical costs one solution is simple. All medical, drug, and medical appliance liability claims should be reviewed by an expert medical panel created by the NIH. They would be solely responsible for determining if any errors were responsible for injuries to the patient and, if so, what financial remedy would be reasonable. The pool of money available as well as the funds necessary for the panel and their staff would be funded by physicians, pharmaceutical companies, and medical appliance manufacturers. No tax funds would be needed.
With little or no incentive for medical liability attorneys, cases would drop immediatley, and those depicable television ads, the modern version of ambulance chasing, would disappear. Awards would be much more reasonable and would go entirely to the injured party. This would also free physicians from the fear of being second guessed by an attorney who could convince a medically ignorant jury that the doctor should have ordered a CT scan (for example). This would reduce questionable lab tests, x-rays, and hospitalizitions that are now ordered to prevent liability claims. No one has ever been sued for ORDERING a CT scan, no matter how unnecessary.
But don't get me wrong. These computer programs will be very useful as they get more and more sophisticated. An elderly patient with multiple symptoms and on numerous medications has a myriad of possibilities which are better handled by a computer, overseen by a physician.

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