Clinical evaluation of an intelligent venipuncture robotic system: performance, patient acceptance, and pre-analytical outcomes
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.
Authors
- Hong Zhao (ORCID: https://orcid.org/0000-0002-8069-9901)
- 叶良平
- Min Zhang (ORCID: https://orcid.org/0000-0001-7055-6790)
- Yuanhong Xu
- Anyong Wang
- Bo Wang
- Meijuan Zheng
- Jinxing Xia
- Yao Yao
- Dan Wu
Institutions
- Anhui Medical University (CN)
- First Affiliated Hospital of Anhui Medical University (CN)
Publication Details
- Journal
- Clinical Chemistry and Laboratory Medicine (CCLM)
- Published
- 2026-09-28
- DOI
- https://doi.org/10.1515/cclm-2026-0833
- Primary Topic
- Soft Robotics and Applications
- Type
- article
- Field-Weighted Citation Impact
- 0.00