Abstract
The aim of this evidence-based project was to improve anesthesia provider awareness of the benefits of artificial intelligence algorithms in perioperative pain management.
Traditional approaches to intraoperative pain management reflect suboptimal real-time pain monitoring and potential opioid under or overdosing that impact clinical outcomes and patient satisfaction. Artificial Intelligence (AI) shows promise in providing data-driven personalized solutions for optimizing pain management to enable real-time adjustments, and deliver safe patient-centered care. AI-powered algorithms serve as a supportive tool to enhance clinical practice, improve safety, and sponsor patient tailored care.
A literature search utilized PubMed, EMBASE, Cochrane Library, and ScienceDirect. Keywords included "adults," "pain management," "artificial intelligence," "anesthesia," "AI-assisted and/or guided pain management," "nociception monitoring," "AI algorithms," and "intraoperative and/or perioperative opioid administration and/or delivery.” The search results were limited to publications from 2018 to 2024 of Level I-IV evidence published in English in the adult population 18 years and older. The Johns Hopkins Research Evidence Appraisal Tool was employed to assess the quality of the studies for inclusion.
AI-assisted technologies have shown sensitivity and specificity in detecting noxious stimuli, facilitating nociception assessment in anesthetized patients undergoing surgery without compromising anesthesia quality or hemodynamics. The evidence indicates a direct association with a nearly 30% reduction in opioid use, ensuring more precise interventions, shorter extubation time, and faster recovery by minimizing opioid-related complications such as postoperative nausea and vomiting, and respiratory depression. Research on the current state of AI-assisted strategies emphasizes their potential to enhance patient outcomes by exploring AI's role in pain management during surgery, highlighting strengths and weaknesses and common restrictions, including sample size, ethical considerations, and economic implications. Future research should corroborate the benefits, limitations, and broader applicability of AI and explore the opportunities for interdisciplinary utilization of AI-generated data to enhance the perioperative patient experience.
Notes
References: Chen Q, Chen E, Qian X. A narrative review on perioperative pain management strategies in enhanced recovery pathways-the past, present and future. J Clin Med. Jun 10 2021;10(12) doi:10.3390/jcm10122568
Shahiri TS, Richebé P, Richard-Lalonde M, Gélinas C. Description of the validity of the Analgesia Nociception Index (ANI) and Nociception Level Index (NOL) for nociception assessment in anesthetized patients undergoing surgery: a systematized review. J Clin Monit Comput. 2022;36(3):623-635. doi:10.1007/s10877-021-00772-3
Fuica R, Krochek C, Weissbrod R, et al. Reduced postoperative pain in patients receiving nociception monitor guided analgesia during elective major abdominal surgery: a randomized, controlled trial. J Clin Monit Comput. 2023;37(2):481-491. doi:10.1007/s10877-022-00906-1
Ma D, Ma J, Chen H, et al. Nociception monitors vs. standard practice for titration of opioid administration in general anesthesia: a meta-analysis of randomized controlled trials. Front Med (Lausanne). 2022;9:963185. Published 2022 Aug 25. doi:10.3389/fmed.2022.963185
Sigma Membership
Pi Alpha
Type
Poster
Format Type
Text-based Document
Study Design/Type
Other
Research Approach
Translational Research/Evidence-based Practice
Keywords:
Instrument and Tool Development, Emerging Technologies, Artificial Intelligence, Intraoperative Pain Management, Anesthesia
Recommended Citation
Diaz, Valerie J. and Jacob, Cristine, "Utilization of Artificial Intelligence Algorithms in Perioperative Management and Reduction of Pain" (2025). International Nursing Research Congress (INRC). 29.
https://www.sigmarepository.org/inrc/2025/posters_2025/29
Conference Name
36th International Nursing Research Congress
Conference Host
Sigma Theta Tau International
Conference Location
Seattle, Washington, USA
Conference Year
2025
Rights Holder
All rights reserved by the author(s) and/or publisher(s) listed in this item record unless relinquished in whole or part by a rights notation or a Creative Commons License present in this item record.
Review Type
Abstract Review Only: Reviewed by Event Host
Acquisition
Proxy-submission
Utilization of Artificial Intelligence Algorithms in Perioperative Management and Reduction of Pain
Seattle, Washington, USA
The aim of this evidence-based project was to improve anesthesia provider awareness of the benefits of artificial intelligence algorithms in perioperative pain management.
Traditional approaches to intraoperative pain management reflect suboptimal real-time pain monitoring and potential opioid under or overdosing that impact clinical outcomes and patient satisfaction. Artificial Intelligence (AI) shows promise in providing data-driven personalized solutions for optimizing pain management to enable real-time adjustments, and deliver safe patient-centered care. AI-powered algorithms serve as a supportive tool to enhance clinical practice, improve safety, and sponsor patient tailored care.
A literature search utilized PubMed, EMBASE, Cochrane Library, and ScienceDirect. Keywords included "adults," "pain management," "artificial intelligence," "anesthesia," "AI-assisted and/or guided pain management," "nociception monitoring," "AI algorithms," and "intraoperative and/or perioperative opioid administration and/or delivery.” The search results were limited to publications from 2018 to 2024 of Level I-IV evidence published in English in the adult population 18 years and older. The Johns Hopkins Research Evidence Appraisal Tool was employed to assess the quality of the studies for inclusion.
AI-assisted technologies have shown sensitivity and specificity in detecting noxious stimuli, facilitating nociception assessment in anesthetized patients undergoing surgery without compromising anesthesia quality or hemodynamics. The evidence indicates a direct association with a nearly 30% reduction in opioid use, ensuring more precise interventions, shorter extubation time, and faster recovery by minimizing opioid-related complications such as postoperative nausea and vomiting, and respiratory depression. Research on the current state of AI-assisted strategies emphasizes their potential to enhance patient outcomes by exploring AI's role in pain management during surgery, highlighting strengths and weaknesses and common restrictions, including sample size, ethical considerations, and economic implications. Future research should corroborate the benefits, limitations, and broader applicability of AI and explore the opportunities for interdisciplinary utilization of AI-generated data to enhance the perioperative patient experience.
Description
AI-powered algorithms designed to support pain management, can improve patient care and safety. Developing guidelines for integrating these technologies into clinical practice, including patient selection, monitoring parameters, and data interpretation, is imperative for accurate execution and vital for the Nurse Anesthesiology discipline to embrace this emerging technology.