Clinical evaluation of an intelligent venipuncture robotic system: performance, patient acceptance, and pre-analytical outcomes.

IF 4.4 2区 医学 Q1 MEDICAL LABORATORY TECHNOLOGY
Bo Wang, Yao Yao, Liangping Ye, Dan Wu, Hong Zhao, Meijuan Zheng, Yuanhong Xu, Min Zhang, Jinxing Xia, Anyong Wang
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Abstract

Objectives: In high-throughput outpatient settings, manual venipuncture is frequently constrained by extreme time pressure and inherent human operator variability, which collectively exacerbate pre-analytical errors and diminish operational efficiency. To address these clinical challenges, we conducted a retrospective study of an intelligent venipuncture robot using 13,803 clinical encounters collected over 18 months in a high-volume outpatient clinic.

Methods: We analyzed performance metrics and patient feedback across three consecutive phases to evaluate both the system's technical maturation and patient acceptance. Furthermore, we validated its clinical utility by comparing procedural success, failure causes, and specimen rejection rates against historical manual practice records.

Results: The results demonstrated that the robotic success rate increased steadily over time (from 95.02 % to 97.57 %) alongside sustained high patient acceptance (up to 96.70 %). Most crucially, the robotic system demonstrated a remarkable full-period zero specimen rejection advantage, effectively eliminating the pre-analytical sample rejections that typically compromise manual phlebotomy. During the same observation window, the primary cause of failure shifted chronologically from device puncture errors (47.58 % in Phase I) to patient movement (57.14 % in Phase III).

Conclusions: This study provides robust real-world data supporting the integration of intelligent venipuncture robotics. Given the evident shift in failure mechanisms toward human factors, future advancements must prioritize optimizing human-machine interaction design and implementing targeted pre-procedure psychological interventions to mitigate involuntary patient movements.

智能静脉穿刺机器人系统的临床评估:性能、患者接受度和分析前结果。
目的:在高通量门诊设置中,手动静脉穿刺经常受到极端时间压力和固有的人为操作人员可变性的限制,这些因素共同加剧了分析前错误并降低了操作效率。为了解决这些临床挑战,我们对智能静脉穿刺机器人进行了回顾性研究,使用了18个月来在一个大容量门诊诊所收集的13803例临床病例。方法:我们分析了连续三个阶段的性能指标和患者反馈,以评估系统的技术成熟度和患者接受程度。此外,我们通过比较手术成功率、失败原因和标本拒绝率与历史手工实践记录来验证其临床实用性。结果:结果表明,随着时间的推移,机器人成功率稳步增加(从95.02 %增加到97.57 %),患者接受度持续高(高达96.70 %)。最关键的是,机器人系统展示了一个显著的全周期零样本排斥优势,有效地消除了通常危及人工采血的分析前样本排斥。在同一观察期内,失败的主要原因按时间顺序从器械穿刺错误(I期为47.58 %)转移到患者运动(III期为57.14 %)。结论:本研究为智能静脉穿刺机器人的集成提供了可靠的现实数据。鉴于失效机制向人为因素的明显转变,未来的进展必须优先优化人机交互设计,并实施有针对性的术前心理干预,以减轻患者的非自愿运动。
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来源期刊
Clinical chemistry and laboratory medicine
Clinical chemistry and laboratory medicine 医学-医学实验技术
CiteScore
11.30
自引率
16.20%
发文量
306
审稿时长
3 months
期刊介绍: Clinical Chemistry and Laboratory Medicine (CCLM) publishes articles on novel teaching and training methods applicable to laboratory medicine. CCLM welcomes contributions on the progress in fundamental and applied research and cutting-edge clinical laboratory medicine. It is one of the leading journals in the field, with an impact factor over 3. CCLM is issued monthly, and it is published in print and electronically. CCLM is the official journal of the European Federation of Clinical Chemistry and Laboratory Medicine (EFLM) and publishes regularly EFLM recommendations and news. CCLM is the official journal of the National Societies from Austria (ÖGLMKC); Belgium (RBSLM); Germany (DGKL); Hungary (MLDT); Ireland (ACBI); Italy (SIBioC); Portugal (SPML); and Slovenia (SZKK); and it is affiliated to AACB (Australia) and SFBC (France). Topics: - clinical biochemistry - clinical genomics and molecular biology - clinical haematology and coagulation - clinical immunology and autoimmunity - clinical microbiology - drug monitoring and analysis - evaluation of diagnostic biomarkers - disease-oriented topics (cardiovascular disease, cancer diagnostics, diabetes) - new reagents, instrumentation and technologies - new methodologies - reference materials and methods - reference values and decision limits - quality and safety in laboratory medicine - translational laboratory medicine - clinical metrology Follow @cclm_degruyter on Twitter!
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