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Establishment of Traditional Chinese Medicine Clinical Pharmacy Work Model Based on Information Technology

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登记号:G20251402

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学科分类:

关键词: Information technology clinical pharmacy of traditional Chinese medicine working mode

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成果名称: Establishment of Traditional Chinese Medicine Clinical Pharmacy Work Model Based on Information Technology
成果登记号: G20251402 学科分类:
绿色分类: 项目关键词: Information technology   clinical pharmacy of traditional Chinese medicine   working mode    
推荐单位:

Shanghai University of Traditional Chinese Medicine Affiliated Longhua Hospital

成果所处阶段:
合作方式: face-to-facemeeting 成果所属行业:
国家/地区: China 知识产权: Academic Achievement
简介: 点击查看
This project is an interdisciplinary application research involving traditional Chinese medicine, clinical pharmacy, and information technology. It is based in a traditional Chinese medicine hospital, centered on patients, and integrates information technology throughout various aspects of clinical practice related to the use of traditional Chinese medicine, such as drug supply, quality monitoring of traditional Chinese medicines, pre-prescription review, dispensing of drugs, execution of prescriptions, medication education, post-prescription analysis and summarization, and continuous improvement. This approach lays a solid foundation for ensuring the rational use of traditional Chinese medicine and improving the level of treatment with it. Ensuring the supply of traditional Chinese medicine: When purchasing decoction pieces, an Excel import system is developed in collaboration with the Information Department to improve procurement efficiency; bar code scanning is used for outpatient prescription formulation and verification to ensure the quality of dispensing; a decoction software is developed to make the entire decoction process controllable and traceable, ensuring the quality of decocted medicine. Quality monitoring of traditional Chinese medicine: Utilizing infrared and ultraviolet fingerprinting techniques, animal models such as zebrafish and rats are employed. As well as experimental technical means such as western and qPCR, the research team uses information technologies such as principal component analysis, multi-index comprehensive evaluation, artificial neural networks, and genetic algorithms to study the quality, efficacy, and stability of commonly used Chinese herbal slices and hospital formulations. This provides references for the selection of Chinese herbal slice suppliers and inventory maintenance conditions, as well as a basis for the development of hospital formulations. Pre-review, dispensing, and execution of medical orders: In accordance with JCI requirements, the research team has comprehensively upgraded the hospital HIS system and 360 view. On the pharmacist's prescription review interface, they can see the patient's entire treatment information and conduct pre-prescription reviews. Only after clinical pharmacists have approved the prescription can the pharmacist scan and dispense the medication, and then the nurse can execute the order. Chinese medicine education: By utilizing systems such as traditional and Western medicine information query and WeChat public accounts, better pharmaceutical services are provided to patients and doctors. Prescription evaluation: The research team independently developed a prescription analysis and review system software. With the help of this software, post-prescription evaluations can be conducted in conjunction with patients' disease conditions, medication plans, treatment outcomes, and test results. At the same time, the research team has attempted to use information technology methods to mine for core prescriptions in the prescriptions of famous traditional Chinese medicine (TCM) practitioners. Currently, they have formed a complete set of mining methods from core drugs → core drug pairs → core prescriptions → core effective prescriptions, providing an effective technical means for clinical medication summaries and the inheritance of experience from famous TCM practitioners. This also provides a new platform for clinical pharmacists to quickly and effectively prescribe medication. TCM efficacy, adverse reactions assessment, and continuous improvement: The research team applied partial least squares discriminant analysis, random forest regression models, Meta-analysis and other information technologies are used to evaluate the efficacy of real-world data on traditional Chinese medicine (TCM) decoctions and preparations, providing a basis for optimizing treatment plans. With the aid of PA software, large datasets on toxic TCM decoctions such as raw Polygonatum and raw Arisaema are collected in clinical settings. By combining patient information with test indicators, medication safety is assessed, and the incidence of adverse reactions is determined, providing evidence for the clinical application of toxic TCMs. A comprehensive analysis of the current status of combined use of Chinese and Western medicine in our hospital identifies potential interactions. Based on this analysis, key drugs requiring attention are analyzed in detail and subjected to real-time intervention. Through this project, a preliminary model for clinical pharmacy work with TCMs has been established. More than 20 people have participated in various talent training programs, over 10 graduate students have been trained, forming an experienced team of clinical pharmacists specializing in TCMs. The project has received funding from 14 sources, published 2 textbooks, and contributed to 81 papers. Among them, there are 21 SCI papers with a total impact factor of 48, cited 667 times, and more than 10 academic exchanges. This has formed the characteristic of interdisciplinary research in traditional Chinese medicine, clinical pharmacy, and information technology. It has become a key specialty construction unit for clinical pharmacy in Shanghai, won the third prize for pharmaceutical science and technology in Shanghai, and the third prize for integrated traditional Chinese and Western medical sciences and technologies. The working model formed has been promoted to related TCM hospitals and received good feedback.
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