Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven

WANG Qi; ZENG Panke; SONG Haoxin; FENG Yonggang; SUN Lili; FENG Jingting; NIU Weiqing; DONG Haiyan; WANG Feng

doi:10.6039/j.issn.1001-0408.2026.05.19

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Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven

更新时间：2026-03-10

- Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven
- ZHONGGUO YAOFANG Vol. 37, Issue 5, Pages: 660-664(2026)
- 作者机构：
  
  西安交通大学第一附属医院药学部，西安　710061
- 作者简介：
- 基金信息：
- DOI：10.6039/j.issn.1001-0408.2026.05.19
  CLC： R952
- Received：28 September 2025，
  
  Revised：2026-01-12，
  
  Accepted：13 January 2026，
  
  Published：15 March 2026
- 稿件说明：
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王琦,曾攀科,宋昊昕,等.基于数智驱动的中药房调剂取药流程优化研究 ^Δ[J].中国药房,2026,37(05):660-664.

WANG Qi,ZENG Panke,SONG Haoxin,et al.Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven[J].ZHONGGUO YAOFANG,2026,37(05):660-664.
王琦,曾攀科,宋昊昕,等.基于数智驱动的中药房调剂取药流程优化研究 ^Δ[J].中国药房,2026,37(05):660-664. DOI： 10.6039/j.issn.1001-0408.2026.05.19.

WANG Qi,ZENG Panke,SONG Haoxin,et al.Optimization of drug dispensing and pickup process in traditional Chinese medicine pharmacy based on data-intelligence-driven[J].ZHONGGUO YAOFANG,2026,37(05):660-664. DOI： 10.6039/j.issn.1001-0408.2026.05.19.

摘要

目的

基于数智驱动对我院中药房调剂取药流程进行改造，以提升药师工作效率与患者取药体验。

方法

运用价值流程图和旅程映射图，系统识别传统流程中药师调剂的非增值环节及患者取药的关键痛点；基于C#和Android电视平台开发中药房智能调剂取药系统，并采用机器学习模型预测患者取药等待时间，从系统性能、预测准确性及药师、患者满意度3个维度开展综合评价。

结果

该系统成功精简“待取写板”和“翻找药品”的非增值环节，实现调剂状态听觉（叫号）与视觉（电视端）的多模动态提示；所构建的取药等待时间预测模型拟合度与泛化性能良好（平均绝对误差为4.28 min，决定系数为0.882）；药师与患者综合满意度分别由传统模式的（70.99±1.74）分和（73.58±1.98）分显著提升至新建系统的（90.02±1.30）和（88.61±2.08）分（

＜0.01）。

结论

基于数智驱动改造的中药房智能调剂取药系统，有效提高了药师调剂工作效率，实现了流程透明化与等待时间可预测化，显著改善了患者取药体验。

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，

＝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 （

＜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.

关键词

Keywords

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