Abstract：
Due to nursing staff shortage and growing nursing care demand, resource allocation and optimal task distribution have become primary concerns of nursing management. Grade mix analysis based on nursing interventions and nurse qualifications from routine patient documentation can support this. Case complexity is a key linking factor of nursing interventions, workload, and grade mix. This study determined case complexity predictors based on one year of routine patient documentation (n = 3,373 cases) from a Swiss hospital and predicted the patient clinical complexity level via weighted cumulative logistic regression models. Significant predictors were sex, age, pre-admission residence, admission type, self- care index, pneumonia risk, and number of nursing interventions. The models\' accuracy is limited yet appropriate for applications such as needs- and competence- based staff-planning. After calibration via in-hospital data it could support nursing management in these tasks. The next step is now to test the model in a clinical setting.
摘要：
由于护理人员短缺和日益增长的护理需求，资源配置和优化任务分配已成为护理管理的首要问题。基于护理干预措施和常规患者文档中的护士资格的等级混合分析可以支持这一点。病例复杂性是护理干预的一个关键联系因素，工作量,和等级混合。这项研究基于瑞士医院一年的常规患者文档（n=3,373例）确定了病例复杂性预测因子，并通过加权累积逻辑回归模型预测了患者的临床复杂性水平。重要的预测因素是性别，年龄,入院前居留，入学类型,自我护理指数，肺炎风险，以及护理干预措施的数量。模型的准确性有限，但适用于基于需求和能力的员工计划等应用。通过院内数据校准后，它可以支持这些任务中的护理管理。下一步是在临床环境中测试模型。
