Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven
- VernacularTitle:基于数智驱动的中药房调剂取药流程优化研究
- Author:
Qi WANG
1
;
Panke ZENG
1
;
Haoxin SONG
1
;
Yonggang FENG
1
;
Lili SUN
1
;
Jingting FENG
1
;
Weiqing NIU
1
;
Haiyan DONG
1
;
Feng WANG
1
Author Information
1. Dept. of Pharmacy，the First Affiliated Hospital of Xi’an Jiaotong University，Xi’an 710061，China
- Publication Type:Journal Article
- Keywords:
traditional Chinese medicine pharmacy;
intelligent dispensing and drug pickup system;
data-intelligence-driven
- From:
China Pharmacy
2026;37(5):660-664
- CountryChina
- Language:Chinese
-
Abstract:
OBJECTIVE To explore the transformation of the dispensing and drug pickup process in traditional Chinese medicine pharmacy （TCM Pharmacy） in our hospital based on data-intelligence-driven， aiming to improve pharmacists’ work efficiency and patients’ drug pickup experience. METHODS Value stream mapping and journey mapping were used to systematically identify non-value-added links in pharmacists’ dispensing process and key pain points in patients’ drug pickup under the traditional process. An intelligent dispensing and drug pickup system for the TCM Pharmacy was developed based on the C# and Android television platforms， and a machine-learning model was adopted to predict patients’ drug pickup waiting time. A comprehensive evaluation was performed from three perspectives： system performance， prediction accuracy， and satisfaction of pharmacists and patients. RESULTS The system successfully streamlined non-value-added links such as “waiting for writing on the board” and “searching for drugs”， and realized multimodal dynamic prompts of dispensing status through auditory （number calling） and visual （television terminal） channels. The constructed model for predicting drug pickup waiting time exhibited good fitting degree and generalization ability （mean absolute error＝4.28 min， R 2 ＝0.882）. The comprehensive satisfaction scores of pharmacists and patients in the traditional mode were significantly increased from （70.99±1.74） and （73.58±1.98） to （90.02±1.30） and （88.61±2.08） in the new system， respectively （ P ＜0.01）. CONCLUSIONS The transformation of the intelligent drug dispensing and pickup system for TCM pharmacy based on data-intelligence-driven effectively improves the efficiency of pharmacists’ dispensing work， realizes process transparency and waiting time predictability， and significantly enhances patients’ drug pickup experience.
