面向智能化转型的审方药师胜任力评价指标体系构建与实证研究——基于Delphi-AHP-EWM综合模型

    Construction and Empirical Research on Competency Evaluation Index System for Prescription Review Pharmacists Oriented to Intelligent Transformation: Based on the Delphi-AHP-EWM Integrated Model

    • 摘要:
      目的  构建智能审方背景下的药师胜任力评价指标体系,以推动审方药师队伍专业化与规范化发展。
      方法  以《药师药学服务胜任力评价标准(试行)》一级指标为基准,结合智能审方实际工作,通过内容分析初步筛选指标;运用德尔菲法对来自全国10所三级综合性医院的32名专家开展2轮咨询,优化指标;采用主观层次分析法结合客观熵权法确定指标权重。
      结果  最终确立了4个一级指标、12个二级指标、39个三级指标的权重系数。各指标权重分布合理,其中“岗位技能”(0.415)与“教研”(0.212)维度权重凸显了专业技能与科研创新并重的趋势;“机器审核结果复核能力”(0.040)、“参与审方规则逻辑的设计与编写能力”(0.032)等高权重三级指标体现了智能化转型的核心能力需求。实证应用表明,该体系有效提升了审方合格率与药师科研产出。
      结论  本研究构建的指标体系科学可靠,兼具主客观赋权优势,紧密结合当前药学服务智能化转型痛点,为审方药师的选拔、考核与培养提供了量化工具与理论支持,具有重要的实践推广价值。

       

      Abstract:
      OBJECTIVE To establish a pharmacist competency evaluation index system in the context of intelligent prescription review, promoting the professionalization and standardization of the prescription review pharmacist team.
      METHODS Based on the first-level indicators of the “Pharmacist Pharmaceutical Service Competency Evaluation Standard(Trial)”, and in combination with the actual work of intelligent prescription review, the indicators were initially screened through content analysis. The Delphi method was used to conduct two rounds of consultations with 32 experts from 10 tertiary general hospitals across the country to optimize the indicators. The subjective analytic hierarchy process combined with the objective entropy weight method was adopted to determine the weights of the indicators.
      RESULTS A total of 4 first-level indicators, 12 second-level indicators, and 39 third-level indicators with their weight coefficients were finally established. The distribution of the weights of each indicator was reasonable. Among them, the weights of the “job skills”(0.415) and “teaching and research”(0.212) dimensions highlighted the trend of equal emphasis on professional skills and scientific research innovation. The high-weight third-level indicators such as “ability to review machine review results”(0.040) and “ability to participate in the design and writing of prescription review rules and logic”(0.032) reflected the core ability requirements of the intelligent transformation. Empirical application showed that this system effectively improved the prescription review pass rate and the scientific research output of pharmacists.
      CONCLUSION The index system constructed in this study is scientifically reliable, with the advantages of both subjective and objective weighting, closely combined with the current pain points of the intelligent transformation of pharmaceutical services, providing a quantitative tool and theoretical support for the selection, assessment, and training of prescription review pharmacists, and has important practical promotion value.

       

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