Analgesia quality index improves the quality of postoperative pain management: a retrospective observational study of 14,747 patients between 2014 and 2021

Abstract

Background The application of artificial intelligence patient-controlled analgesia (AI-PCA) facilitates the remote monitoring of analgesia management, the implementation of mobile ward rounds, and the automatic recording of all types of key data in the clinical setting. However, it cannot quantify the quality of postoperative analgesia management. This study aimed to establish an index (analgesia quality index (AQI)) to re-monitor and re-evaluate the system, equipment, medical staff and degree of patient matching to quantify the quality of postoperative pain management through machine learning. Methods Utilizing the wireless analgesic pump system database of the Cancer Hospital Affiliated with Nantong University, this retrospective observational study recruited consecutive patients who underwent postoperative analgesia using AI-PCA from June 1, 2014, to August 31, 2021. All patients were grouped according to whether or not the AQI was used to guide the management of postoperative analgesia: The control group did not receive the AQI guidance for postoperative analgesia and the experimental group received the AQI guidance for postoperative analgesia. The primary outcome was the incidence of moderate-to-severe pain (numeric rating scale (NRS) score ≥ 4) and the second outcome was the incidence of total adverse reactions. Furthermore, indicators of AQI were recorded. Results A total of 14,747 patients were included in this current study. The incidence of moderate-to-severe pain was 26.3% in the control group and 21.7% in the experimental group. The estimated ratio difference was 4.6% between the two groups (95% confidence interval [CI], 3.2% to 6.0%; P < 0.001). There were significant differences between groups. Otherwise, the differences in the incidence of total adverse reactions between the two groups were nonsignificant. Conclusions Compared to the traditional management of postoperative analgesia, application of the AQI decreased the incidence of moderate-to-severe pain. Clinical application of the AQI contributes to improving the quality of postoperative analgesia management and may provide guidance for optimum pain management in the postoperative setting.

Knowledge, Attitude, Practice towards Artificial intelligent Patient-controlled analgesia among Anesthesiologists ：a cross-sectional study in east China's Jiangsu Province

“…While many valued participation in Ai-PCA training and were willing to address work-related issues collaboratively, attitudes towards updating knowledge and learning the latest expert consensus were less proactive. This nding aligns with reports from pain management guidelines indicating fewer advanced strategies employed in patient treatment in China [27]. Insu cient attention is directed towards the prevention and treatment of adverse events.…”

Knowledge, Attitudes and Practices Among Anesthesia and Thoracic Surgery Medical Staff Toward Ai-PCA

“…The Questionnaire was developed with guidance from the “Intelligent Patient-Controlled Pain Management Expert Consensus”, 12 and relevant literatures. 3 , 13 , 14 The initial draft underwent revisions based on feedback from two senior experts specializing in anesthesia and thoracic surgery both holding the title of Chief Physician. Subsequently, a preliminary trial was conducted on a limited scale (n=48), resulting in a Cronbach’s alpha coefficient value of 0.875, indicating good internal consistency.…”

Incorporation of “Artificial Intelligence” for Objective Pain Assessment: A Comprehensive Review

“…For example, wearable devices or mobile applications could track pain levels throughout the day, alert healthcare providers, and prevent pain-related complications [ 55 ]. A recent study compared postoperative analgesia by traditional management versus the application of the newly introduced “analgesia quality index” (AQI) through the application of AI-patient controlled analgesia (AI-PCA) and showed that the AQI decreased the incidence of moderate-to-severe pain, improved the quality of analgesia, and may provide guidance for optimum postoperative pain management [ 93 ]. However, the monitoring of physical activity may be affected by many factors, such as personal mood, weather conditions, and other psychosocial factors [ 94 , 95 ].…”

“…The utilization of AI algorithms in pain assessment enables the analysis of large-scale datasets, pattern identification, and the emergence of trends and correlations within these datasets, leading to valuable insights into pain mechanisms, risk factors, and treatment efficacy. Clinical studies showed that the application of AI-based algorithms is associated with better pain relief and improved quality of analgesia [ 8 , 15 , 93 , 106 ]. Clinical application can provide guidance for optimum pain management in a postoperative setting [ 93 , 106 ].…”

“…Clinical studies showed that the application of AI-based algorithms is associated with better pain relief and improved quality of analgesia [ 8 , 15 , 93 , 106 ]. Clinical application can provide guidance for optimum pain management in a postoperative setting [ 93 , 106 ]. Evidence suggests that facial expressions of pain are sensitive and specific to pain and that these expressions can be distinguished from facial expressions associated with basic emotions [ 107 , 108 ].…”