Dr. Yong Chen, a Senior Fellow at the Leonard Davis Institute (LDI) and a Professor at Penn Medicine, has received an $8 million grant from the National Institute of Mental Health (NIMH) to lead a project that will improve how mental health disorders are diagnosed and treated. This project aims to change the way mental health care is provided and help patients get better results over the next five years.
Pioneering New Methods in Mental Health Research
Dr. Chen directs the Penn Computer, Inference and Learning Lab (PennCIL) and the Center for Health AI and Synthesis of Evidence (CHASE) and oversees the IMPACT-MH project (Individually Measured Phenotypes to Advance Computational Translation in Mental Health). The project is in collaboration with Yale University and the Mayo Clinic and has raised more than $150 million in funding to tackle mental health care problems with innovative solutions.
The project sits within the area of precision medicine for mental health, which is challenging because mental health comes with large variability in both presentation and treatment responses. To best gather a patient’s information profile, the project will be multi-faceted with multiple streams of data collection, all integrated – clinical assessments, behavioral assessments, and even biological markers – to create a profile that will allow for better diagnostics and treatment planning.
Enhancing Diagnosis with Multimodal Data
The IMPACT-MH initiative stands out for its application of different types of data—clinical symptoms, behavioral assessments, and biological information—to create a composite representation of mental health. Dr. Chen describes how current mental health diagnoses largely use broad sets of categories that can be too broad in assessing diagnoses to be able to predict how an individual patient would respond to a treatment. By having a framework that provides a detailed and extensive description of each person, the project aims to create more nuanced patient profiles that take into consideration the complexity of mental health disorders.
“This combining of different kinds of data is a first step in how we assess and treat mental health conditions currently,” stated Dr. Chen. “It does not rely on traditional psychometric approaches and contributes to the opportunity for individualized care, tailored uniquely to each patient.”
Toward More Personalized Treatment
One of the primary goals of the IMPACT-MH project is to help clinicians develop more targeted and personalized treatment plans for mental health patients. By generating detailed patient profiles that include symptoms, behaviors, cognitive performance, and biological data, clinicians will be able to predict how different individuals might respond to specific treatments, improving their ability to provide effective care.
“In the future, this project could be used to inform treatment choices, track patient progress, and make more accurate predictions about outcomes,” Dr. Chen said. “The goal is to move away from a ‘one-size-fits-all’ approach and toward more individualized care that reduces uncertainty and improves treatment success.”
Collaboration for Better Mental Health Outcomes
The IMPACT-MH project – that is, a project that entails Penn, Yale, and Mayo Clinic having developed a partnership, wherein experts from various disciplines are collaborating to enhance data collection regarding mental health and the way it has been measured. They want to redefine standards for diagnosing, assessing, and treating mental health conditions to promote individualized care.
Dr. Chen highlights that this project is distinctive due to the multitude of expertise involved, so not only will they tackle some of the challenges regarding how mental health is diagnosed, but also develop ways of collaborating towards better understanding the data, and share it with one another so that they will set new benchmarks in the field. This might shape different ways of providing mental health care, particularly through much greater diagnostic precision, and ultimately lead to improved outcomes for service users.
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