AI Aims to Tailor Post-Surgical Pain Medication Needs
Standardized opioid prescriptions often result in unused medication or inadequate relief. New research suggests artificial intelligence could help clinicians predict individual patient needs more accurately.
For many patients recovering from oral surgery, the question of how much pain medication is actually needed remains difficult to answer. While over-the-counter drugs like ibuprofen and acetaminophen manage pain for most, some individuals experience breakthrough pain that requires stronger opioids. Clinicians currently face a significant challenge: they must decide on a prescription before the surgery, without knowing how that specific patient’s body will respond to the procedure.
Researchers at the Harvard School of Dental Medicine are investigating whether artificial intelligence can solve this dilemma. A new paper published in Current Surgery Reports argues that AI tools could help predict postoperative opioid needs. By moving away from one-size-fits-all prescribing, these tools could identify patients at higher risk for misuse or those who might be left in pain, potentially reducing the amount of unused medication that ends up in landfills or in the wrong hands.
The problem with standard prescriptions
Oral and maxillofacial surgery, such as wisdom tooth removal, accounts for the majority of opioid prescriptions written by dentists in the United States. However, data suggests that most patients do not need these medications. In one study of patients undergoing third-molar surgery, only 7 percent required opioids if their recovery was uneventful. Yet, more than half of the opioids prescribed for dental procedures are often left unused.
This practice, described by senior author Tim Wang as 'just-in-case' prescribing, creates a double-edged sword. On one hand, it risks contributing to the opioid crisis through diversion and misuse. On the other hand, strictly limiting prescriptions can leave patients who genuinely suffer from severe pain without adequate relief. The current system struggles to balance these two competing risks because it relies on generalized averages rather than individual biological responses.
Predicting individual pain responses
The core issue is that pain is highly individualized. Two patients can undergo the exact same procedure by the same surgeon and experience vastly different levels of pain and swelling due to their unique biology and pain tolerance. Wang notes that clinicians currently make prescribing decisions without knowing how an individual will respond. This lack of predictive power forces doctors to guess, leading to either over-prescription or under-treatment.
Student authors Samat Borbiev and Clark Morgan emphasize the need for a balanced approach. They suggest that as clinical biomarkers and AI technology advance, these tools can help physicians make more informed decisions. The goal is not just to reduce drug volume, but to nudge physicians when a patient might need additional counseling or a different pain management strategy, ensuring that care is both safe and effective.
Leveraging data for better care
Machine learning offers a way to account for the complex variables that influence pain. AI models can analyze vast amounts of clinical, behavioral, and biological data to identify patterns associated with opioid use and misuse. Factors influencing pain response extend far beyond the surgery itself, including genetic and social elements that a single provider may not fully capture during a brief consultation.
According to the research highlighted by GN technics/ai (en-US), the volume of data required to determine an ideal pain-control regimen exceeds what a human clinician can easily process. By integrating this data, AI could serve as a decision-support tool, helping to personalize prescriptions. This approach aims to reduce the reliance on standardized doses, thereby minimizing the risk of both inadequate pain control and unnecessary medication exposure.
