Preventing Opiates: Using AI for Pain Relief

A study backed by NIH and led by Worcester Polytechnic Institute (WPI) seeks to harness the power of artificial intelligence to redirect chronic pain patients towards mindfulness-based treatments and away from opioids. The study uses machine learning to analyze patient data and identify those who would significantly benefit from non-pharmacological interventions. This innovative method could potentially reduce opioid dependence, providing more personalized care, specifically for chronic lower back pain patients across diverse groups, and potentially leading to a transformation in pain management and healthcare costs. Credit: Melissa E. Arndt

The research supported by the NIH and taking place at Worcester Polytechnic Institute uses artificial intelligence to detect effective mindfulness-based treatments for those suffering from chronic lower back pain, thus providing alternatives to their reliance on opioids.

A five-year study, being led by Worcester Polytechnic Institute, explores the potential use of artificial intelligence in assisting healthcare professionals guide chronic pain patients towards mindfulness-based treatments as an alternative to dependency on opioids.

The newly launched National Institutes of Health (NIH) HEAL (Helping to End Addiction Long-term) initiative is funding a study that will apply machine learning to detect patterns in patient data to help healthcare experts determine who will benefit the most from mindfulness-based stress reduction (MBSR) for pain management.

Jean King, the Peterson Family Dean of Arts and Sciences at WPI, believes this initiative can transform healthcare by predicting who will respond well to non-pharmacological interventions and can potentially save lives.

WPI has been awarded $1.6 million by the NIH to initiate the trial design. If the team achieves their outlined benchmarks, they can potentially receive around $9 million in total research funding over the next five years.

The study could equip healthcare providers with tools to help patients avoid opioid dependency which often leads to lifelong addiction challenges. Data from 2021 revealed alarming rates of opioid-related deaths; over 16,000 people died from opioids prescribed by doctors, and over 80,000 died from opioid-related overdoses overall.

Chronic pain is a major health concern, as recent data from the U.S. Centers for Disease Control and Prevention Morbidity and Mortality Report showed that more than 51 million people in the US, or more than 20% of adult Americans, suffer from chronic pain.

Prior studies have confirmed that mindfulness-based stress reduction (MBSR) effectively helps in chronic pain management. However, the success rate is not uniform for everyone, and healthcare practitioners are still figuring out who it would work best for and why.

The current study, focusing specifically on chronic lower back pain in diverse populations, will analyze physiological data from 350 participants. The fitness-sensor-collected data includes sleep patterns, heart rate, physical activity, in addition to self-reported mental health data. This data will be analyzed using machine-learning models to predict whether a patient would respond well to mindfulness-based treatment plans.

Carolina Ruiz, the WPI Associate Dean of Arts and Sciences and Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science, believes that the predictability provided by machine learning can empower doctors to make more informed decisions, sparing patients from ineffective treatments and potentially saving healthcare costs.

The study, dubbed Integrative Mindfulness-based Predictive Approach for Chronic low back pain Treatment, or IMPACT, will bring together a diverse group of researchers at WPI, UMass Chan Medical School, and Boston University Chobanian & Avedisian School of Medicine. Along with King and Ruiz, WPI faculty researchers include Emmanuel Agu, the Harold L. Jurist ’61 and Heather E. Jurist Dean’s Professor of Computer Science and MPI, Angela Incollingo Rodriguez, assistant professor of psychological and cognitive sciences, Zheyang Wu, professor, mathematical sciences, and Benjamin Nephew, assistant research professor, biology and biotechnology.

Agu’s expertise in analyzing sensor data using smartphones and fitness trackers will play a critical role in the study. The devices will track several data points, but Agu said of particular interest to researchers will be participants’ circadian rhythms–sleep and wake cycles.

“Sleep has an immense impact on our overall health,” said Agu, who is a co-principal investigator on the study. “An individual in pain is more likely to experience broken sleep, which can lead to a host of other health issues. Mindfulness-based approaches may help participants sleep better, which can reduce some of those other risk factors.”

The study will include racially and ethnically diverse populations typically underrepresented in both the research and practice of mindfulness-based stress reduction, despite being at increased risk for stress, chronic pain, and the associated adverse outcomes they bring. Participants will be recruited from the Boston metro region through Boston Medical Center and Cambridge Health Alliance, and from the Worcester region through UMass Chan and WPI.

Partners on the grant and community leaders are excited for the work to begin.

Dr. Natalia Morone, associate professor of medicine at Boston University Chobanian and Avedisian School of Medicine, a primary care physician at Boston Medical Center, and a co-principal investigator on the study, said the key will be identifying specific markers that indicate people will respond to mindfulness treatment. “We are doing this in an innovative way because we are using machine learning to figure this out,” Morone said. “I am very excited to partner with my colleagues at WPI and UMass Chan to accomplish this study. It has the potential to help many people.”

Dr. David D. McManus, the Richard M. Haidack Professor in Medicine and chair and professor of medicine at UMass Chan, said the medical school will bring invaluable experience to the study gained from overseeing the cores of prominent studies, such as the Framingham Heart Study, National Institutes of Health Rapid Acceleration of Diagnostics (RADx) initiative, and the Risk Underlying Rural Areas Longitudinal (RURAL) study.

“The wealth of knowledge accumulated through the administration and management of critical components in these studies positions us at the forefront of groundbreaking research,” McManus said. “Our enthusiasm is heightened as we join forces with WPI and BU under the capable leadership of Jean King.”

Dr. Matilde Castiel, commissioner of health and human services in Worcester, said AI is a tool to help the healthcare system deliver better and more personalized care.

“I am thrilled that WPI will use AI to address chronic back pain and make an impact on the opioid epidemic, which is truly a public health emergency not only in our city and state, but nationally,” Castiel said. “This intervention can decrease the reliance of opioids for chronic back pain and provide a more targeted approach that is specific to the individual.”