Interpretation and Implementation Prospects of the Expert Consensus on Clinical Application for Patient-controlled Analgesia Based on Intelligent Analgesia Technology

The transition from conventional patient-controlled analgesia (PCA) to artificial?intelligence-assisted patient-controlled analgesia (Ai-PCA) represents an innovative model for analgesia management. Clinicians utilize AI-enabled PCA devices to deliver individualized analgesia according to patients’ pain intensity and physical status. This review interprets the 2024 Chinese Expert Consensus on Clinical Application of Patient-controlled Analgesia, a landmark document for innovative analgesic strategies. We summarize the historical evolution of postoperative PCA and Ai-PCA, the rationale for consensus development, clinical implementation details, quality management frameworks, and future directions of Ai-PCA. Measures to improve Ai-PCA performance and perioperative pain management are analyzed, highlighting priorities for further clinical translation. As a core clinical specialty reflecting comprehensive hospital service capacity, anesthesiology benefits greatly from artificial intelligence. Integrating AI into pain medicine ushers in an era of big-data-driven preventive analgesia, which can improve patient comfort and satisfaction, enhance enhanced recovery after surgery (ERAS), and guide clinicians toward standardized, rational pain therapy.

Authors

Institutions

Publication Details

Journal
International Journal of Pain Research
Published
2026-08-27
DOI
https://doi.org/10.11648/j.ijpr.20260203.16
Primary Topic
Enhanced Recovery After Surgery
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Interpretation and Implementation Prospects of the Expert Consensus on Clinical Application for Patient-controlled Analgesia Based on Intelligent Analgesia Technology

Qinjun Chu, Bin Zheng, 佘守章, Hanzhong Cao et al.
International Journal of Pain Research
Enhanced Recovery After Surgery
article

Interpretation and Implementation Prospects of the Expert Consensus on Clinical Application for Patient-controlled Analgesia Based on Intelligent Analgesia Technology

Qinjun Chu, Bin Zheng, 佘守章, Hanzhong Cao, Weifeng Yu, Wenqi Huang
article en

Abstract

The transition from conventional patient-controlled analgesia (PCA) to artificial?intelligence-assisted patient-controlled analgesia (Ai-PCA) represents an innovative model for analgesia management. Clinicians utilize AI-enabled PCA devices to deliver individualized analgesia according to patients’ pain intensity and physical status. This review interprets the 2024 Chinese Expert Consensus on Clinical Application of Patient-controlled Analgesia, a landmark document for innovative analgesic strategies. We summarize the historical evolution of postoperative PCA and Ai-PCA, the rationale for consensus development, clinical implementation details, quality management frameworks, and future directions of Ai-PCA. Measures to improve Ai-PCA performance and perioperative pain management are analyzed, highlighting priorities for further clinical translation. As a core clinical specialty reflecting comprehensive hospital service capacity, anesthesiology benefits greatly from artificial intelligence. Integrating AI into pain medicine ushers in an era of big-data-driven preventive analgesia, which can improve patient comfort and satisfaction, enhance enhanced recovery after surgery (ERAS), and guide clinicians toward standardized, rational pain therapy.

International Journal of Pain ResearchVol. 2(3)
Sun Yat-sen University (CN), Nantong University (CN), Wenzhou Medical University (CN), First Affiliated Hospital of Wenzhou Medical University (CN), Nantong Tumor Hospital (CN), Guangzhou First People's Hospital (CN), The First Affiliated Hospital, Sun Yat-sen University (CN), Zhengzhou Central Hospital (CN), Guangzhou Medical University (CN)
Openalex Percentile: Top 7%
Enhanced Recovery After Surgery
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.