Spring Use AI To Give Personalized Antidepressant Recommendations

Spring Use AI To Give Personalized Antidepressant Recommendations
Depression is the leading cause of disability worldwide. With an estimated 350 million people suffering from the disorder, it is a major contributor to the overall global burden of disease. Depression is a treatable illness and there are effective treatments, however, finding the right medication can be a process of trial and error. There is still little way to know which treatment will work, and many need to try two or three drugs or drug combinations before experiencing relief. A process that can take several years.

The New York-based startup Spring is determined to end the trial and error process of early depression treatment. They have developed a machine-learning model, based on peer-reviewed and published research, that help doctors make the best treatment decisions. The online assessment tool is a 10-minute test, consisting of 25 questions, which helps analyze the patient’s specific symptom profile and help understand which drugs might be most effective. Their algorithm can identify 66% of patients who will not recover from a specific drug, performing significantly better than a psychiatrist.

The technology is based on research by Adam Chekroud, who published a paper in the medical journal The Lancet Psychiatry on the model. April Koh read the paper and found it compelling after having friends and family go through the trial and error process, and decided to reach out. Together with Abhishek Chandra they founded Spring, and have now developed an entire platform where physicians can administer digital screenings of patients, a simple 10 question survey, and track patient progress to make better treatment decisions.

The innovative, yet simple digital tool could help match patients with effective drugs. Currently, less than one-third of people treated for depression find relief from their first antidepressant, which is not only frustrating but can have serious consequences. Although Spring and their technology cannot replace physicians, it can play a major role in influencing decisions, potentially giving millions of patients their life back. The platform could even increase accuracy of assessment, reducing numbers of misdiagnosis. The digital nature of the solution also mean that it could increase access to care, potentially helping reduce the social stigma associated with mental disorders.

Several hundred patients have so far used Spring since its launch, and the startup is working with a network of psychiatrists around the US. It has been well received by both patients and physicians, and their feedback help guide the startup’s development and refine their product. They are also looking to develop additional tests for other behavioral health conditions and drugs.

Spring has previously participated in the YEI Fellowship, an 8-week boot camp for accelerating ventures run by the Yale Entrepreneurial Institute, and won the $25,000 Thorne Prize for Social Innovation in Health or Education from InnovateHealth Yale. They have also received a $30,000 Bioscience Pipeline award, and been part of the New York Digital Health Accelerator. In 2017, Spring is part of PULSE@MassChallenge, a startup program specifically designed to bring digital health innovators and the clinical community together to drive better and faster innovation.
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