Malingering, or feigning an illness to escape work responsibilities, can be extremely costly to employers and the US government. A study published in 2011 estimated that $20 billion was paid that year in Social Security Disability benefits to malingering patients. Malingering can be especially hard to diagnose for mental disorders because much of the diagnosis is based on feedback from the patient. The current method to test whether a patient is guilty of feigning mental disorders requires a series of 30-40 written tasks that may take 8 hours to complete. This approach is inefficient and costly, and may lack accuracy since they only compare the patients results against themselves.
This past week, Philips Inc. was granted an application (U.S. Pub. No. 20200185110) for a method and apparatus for use in detecting malingering in patients. The method uses AI to analyze a patients results of either physical or mental tasks, and compares the results to a library of measurements from the general population. After each task, the systems provides an evaluation of how the patient performed compared to how the general public would have performed. The AI integrated into the software then provides a statistical analysis of the likelihood that the patient was malingering. This should allow for much more accurate results while reducing the time necessary to make the determination someone is malingering.
Philips electronics is no stranger to the AI Neurology sector - their current portfolio contains 11 applications and 4 patents in the sector in the past 3 years. Make sure to check out the Magic Number AI/Biotech Patent Forecast® to stay up to date with emerging technologies such this one!
