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

Journal: BMC anesthesiology
PMID: 37598151

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.

Authors

  • Di Wang
    Center for Endocrine Metabolism and Immune Diseases, Beijing Luhe Hospital, Capital Medical University, Beijing, People's Republic of China.
  • Yihui Guo
    Department of Anesthesiology, The People's Hospital of Pizhou, Pizhou Hospital affiliated to Xuzhou Medical University, Xuzhou, Jiangsu, China.
  • Qian Yin
    Department of Radiology, Tangdu Hospital, the Fourth Military Medical University, Xi'an 710038, China.
  • Hanzhong Cao
    Department of Anesthesiology, Tumor Hospital Affiliated to NanTong University, Nantong, Jiangsu, China.
  • Xiaohong Chen
    Department of Neurology, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, China.
  • Hua Qian
    Chronic Disease Research Center, Medical College, Dalian University, Dalian, China.
  • Muhuo Ji
    Department of Anesthesiology, Pain, and Perioperative Medicine, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
  • Jianfeng Zhang
    Department of Vascular Surgery, The First Affiliated Hospital of Guangxi Medical University, Nanning, Guangxi 530021, P.R. China.

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