AI-Powered Tools Could Individualize Post-Operative Pain Medication Prescribing
AI-based solutions from Harvard researchers: new insights on predicting post-operative opioid needs and reducing unnecessary prescriptions.
Researchers from Harvard School of Dental Medicine reveal that artificial intelligence and machine learning tools can more precisely predict post-operative opioid requirements, reduce "just-in-case" prescribing, and identify at-risk patients.
The Challenge of Determining Post-Operative Pain Relief Needs
How much pain medication patients will need after surgical operations such as tooth extractions remains a significant question. While over-the-counter medications like acetaminophen and ibuprofen provide sufficient relief for most patients, some may experience severe pain that does not respond to these drugs.
Scope and Details of the Published Study
In a new article published in the journal Current Surgery Reports, the authors discuss how artificial intelligence and machine learning can help clinicians predict post-operative opioid needs. Oral and maxillofacial surgery procedures account for more than 60 percent of opioid prescriptions written by dentists in the U.S.
Unnecessary Prescribing Habits and Unexpected Rates
A prospective study of patients undergoing third molar surgery showed that only 7 percent of patients with an uneventful recovery process required opioids. In contrast, more than half of the opioids prescribed after dental surgery remain unused.
Individualized Solutions with Machine Learning
By using machine learning, clinicians can analyze large amounts of data and account for individual biological, clinical, and social differences. This allows for clearer identification of patterns associated with opioid use, misuse, and prolonged use.
Clinical Decisions and Future Goals
While the authors emphasize that artificial intelligence will never replace clinical judgment, they note that the majority of current research is retrospective in nature with limited external validation. The ultimate goal is to make pain management much more precise without compromising patient outcomes.