AI's Role in Tailoring Opioid Prescriptions for Pain Management

After a wisdom tooth extraction, patients often face a pivotal question: just how much pain medication will they actually need? While over-the-counter options like acetaminophen and ibuprofen suffice for many, some still experience significant pain that may necessitate opioids. For dental professionals, identifying which patients will need additional pain relief post-surgery is a difficult task.

Researchers from the Harvard School of Dental Medicine are investigating the potential of artificial intelligence (AI) to enhance the precision of these prescribing decisions. A recent paper published in Current Surgery Reports highlights how machine learning can aid clinicians in predicting opioid requirements post-surgery and in flagging patients who might be at higher risk for misuse or inadequate pain control.

Tim Wang, chief resident of the Oral and Maxillofacial Surgery (OMFS) program at the Dental School and Massachusetts General Hospital, emphasizes, “The fundamental problem is that opioid prescribing is still largely standardized, while patients’ actual pain medication needs are highly individualized.” Two patients undergoing the same surgical procedure can have vastly different pain experiences based on their unique biology and pain tolerance.

Rethinking Conventional Prescribing Practices

Dentists primarily prescribe opioids for oral and maxillofacial surgery, with these procedures accounting for over 60% of opioid prescriptions by dentists in the U.S. Notably, initial opioid exposure often happens during these dental surgeries. However, the amounts prescribed frequently do not align with actual patient needs; a study indicated only 7% of patients needed opioids after a straightforward wisdom tooth extraction.

Research also reveals that more than half of opioids prescribed in post-operative care go unused, creating a risk of misuse or diversion. As Wang points out, the dilemma for surgeons lies in making prescribing decisions before fully understanding how a patient will respond to the procedure.

This “just-in-case” prescribing model anticipates potential pain rather than tailoring prescriptions based on individual risk factors. The challenge then is striking a balance: minimizing needless prescriptions while ensuring that patients requiring additional pain relief receive it.

Samat Borbiev, D.M.D. ’28, one of the paper’s student authors, notes that “with more breakthroughs in clinical biomarkers and AI, we can merge both to create tools for physicians to make informed decisions on pain management.” These tools could help physicians recognize when patients might need extra care or support for their pain management.

Harnessing AI to Address Individual Variability

Machine learning presents a promising avenue for accommodating individual patient differences. AI algorithms can comb through extensive clinical, behavioral, and biological data to discern patterns related to opioid use, misuse, and prolonged use. For instance, one study analyzing over 560,000 Medicare beneficiaries demonstrated that machine learning could more effectively predict opioid overdose risk than traditional statistical methods.

The real advantage of implementing AI lies in the prospect of personalized care. Integrating AI into electronic health records could guide clinicians in determining the correct type, amount, and duration of opioid prescriptions. It could also highlight patients who may require closer monitoring, thus bolstering existing prescription drug monitoring initiatives.

Wang believes AI can expedite the review of prescription drug monitoring data, allowing for quicker identification of trends in a patient's history. Additionally, these systems could extend monitoring support beyond the clinical environment. By incorporating real-time patient-reported pain and medication usage data, AI could refine recommendations and help clinicians educate patients on medication management and pain control.

Matthew Watt, D.M.D. ’26, another contributing author, explains, “While inpatient settings allow easier management of pain, once patients are discharged, we lose direct supervision.” He argues that AI can be particularly beneficial in home settings, offering tailored pain management strategies as patients recover.

While AI presents significant potential for enhancing prescribing practices, it won't replace the need for clinical judgment. The authors caution that much existing research remains retrospective, with many predictive models lacking extensive validation across diverse populations.

Wang further clarifies that the intention isn't merely to cut back on opioid prescriptions but to refine them, ensuring patients avoid unnecessary pain or complications. “Patients should expect a pain-management plan shaped not only by their medical procedures but also by their individual circumstances,” he concludes.