From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives

Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic tech...

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Main Authors: Rui Wang, Shaoshuang Wang, Na Duan, Qiang Wang
Format: Article
Language:English
Published: Frontiers Media S.A. 2020-05-01
Series:Frontiers in Medicine
Subjects:
Online Access:https://www.frontiersin.org/article/10.3389/fmed.2020.00145/full
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spelling doaj-238ba3b68c5849ea8dabda0c5f80a92f2020-11-25T03:17:01ZengFrontiers Media S.A.Frontiers in Medicine2296-858X2020-05-01710.3389/fmed.2020.00145516516From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and PerspectivesRui WangShaoshuang WangNa DuanQiang WangPain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional “on-demand” parenteral analgesia, suggesting that it might be the manner of analgesia implementation that matters for effective postoperative pain management. A wireless intelligent PCA (Wi-PCA) system that incorporated remote monitoring, an intelligent alarm, intelligent analysis and assessment of the PCA equipment, as well as automatically recording and reserving key information functions under a wireless environment was introduced in our department in 2018. The practice showed that the Wi-PCA system significantly reduced the incidence of moderate to severe postoperative pain and relevant adverse effects, shortened hospital stays, and improved patient satisfaction with postoperative pain relief. Nevertheless, for both traditional and Wi-PCA, analgesics are only administered when pain occurs, leaving behind a realm of possibilities for better postoperative pain management. With the rapid development of machinery and deep learning algorithms, artificial intelligence (AI) is changing the mode of clinical decision making. Integrating the big data collected by state-of-the-art monitoring sensors, the Internet of Things and AI algorithms, an AI-assisted PCA (Ai-PCA) may be a promising future direction for postoperative pain management.https://www.frontiersin.org/article/10.3389/fmed.2020.00145/fullAi-PCAWi-PCApatient-controlled analgesiaartificial intelligencepostoperative pain
collection DOAJ
language English
format Article
sources DOAJ
author Rui Wang
Shaoshuang Wang
Na Duan
Qiang Wang
spellingShingle Rui Wang
Shaoshuang Wang
Na Duan
Qiang Wang
From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
Frontiers in Medicine
Ai-PCA
Wi-PCA
patient-controlled analgesia
artificial intelligence
postoperative pain
author_facet Rui Wang
Shaoshuang Wang
Na Duan
Qiang Wang
author_sort Rui Wang
title From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
title_short From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
title_full From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
title_fullStr From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
title_full_unstemmed From Patient-Controlled Analgesia to Artificial Intelligence-Assisted Patient-Controlled Analgesia: Practices and Perspectives
title_sort from patient-controlled analgesia to artificial intelligence-assisted patient-controlled analgesia: practices and perspectives
publisher Frontiers Media S.A.
series Frontiers in Medicine
issn 2296-858X
publishDate 2020-05-01
description Pain relief is a major concern for patients who have undergone surgery, and it is an eternal pursuit for anesthesiologists. However, postoperative pain management is far from satisfactory, though the past decades have witnessed great progress in the development of novel analgesics and analgesic techniques. A Cochrane systematic review showed that patient-controlled analgesia (PCA) achieved better pain relief and greater patient satisfaction than traditional “on-demand” parenteral analgesia, suggesting that it might be the manner of analgesia implementation that matters for effective postoperative pain management. A wireless intelligent PCA (Wi-PCA) system that incorporated remote monitoring, an intelligent alarm, intelligent analysis and assessment of the PCA equipment, as well as automatically recording and reserving key information functions under a wireless environment was introduced in our department in 2018. The practice showed that the Wi-PCA system significantly reduced the incidence of moderate to severe postoperative pain and relevant adverse effects, shortened hospital stays, and improved patient satisfaction with postoperative pain relief. Nevertheless, for both traditional and Wi-PCA, analgesics are only administered when pain occurs, leaving behind a realm of possibilities for better postoperative pain management. With the rapid development of machinery and deep learning algorithms, artificial intelligence (AI) is changing the mode of clinical decision making. Integrating the big data collected by state-of-the-art monitoring sensors, the Internet of Things and AI algorithms, an AI-assisted PCA (Ai-PCA) may be a promising future direction for postoperative pain management.
topic Ai-PCA
Wi-PCA
patient-controlled analgesia
artificial intelligence
postoperative pain
url https://www.frontiersin.org/article/10.3389/fmed.2020.00145/full
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