Cho InSook, Staggers Nancy, Park Ihnsook
Department of Nursing, Inha University, Incheon, South Korea.
Comput Inform Nurs. 2010 Mar-Apr;28(2):95-102. doi: 10.1097/NCN.0b013e3181cd8240.
Nurses often need to make quick decisions with incomplete diagnostic information while they are under time pressure. The use of a data-driven, computerized decision support approach in daily work activities has great potential to facilitate precise and context-sensitive use of the information implicit in nursing diagnoses. This study explored optimal information amounts and levels of information content for designing and implementing a diagnostic nursing decision support system. Specifically, the use of probability data for likely nursing problems and the preferred number of displayed nursing problems were presented to expert and novice nurses. The study used a counterbalanced, repeated-measures, and factorial design. The authors developed two scenarios: (1) a pneumonia patient with diabetes mellitus complications and (2) a patient with controlled diabetes who also had a bone fracture. A previously developed prototype for a diagnostic nursing decision support system was used to display the information. Eighteen novice and expert nurses from two hospitals in Korea participated. Results for the differing levels of content did not differ significantly with level of expertise, but the preferred amount of information was significant for the two groups of nurses. The differing information needs of novices and experts should be considered when designing future computer-based decision support.
护士在时间紧迫的情况下，常常需要依据不完整的诊断信息迅速做出决策。在日常工作中采用数据驱动的计算机化决策支持方法，极有可能促进对护理诊断中隐含信息的精确且符合具体情境的运用。本研究探索了设计与实施诊断性护理决策支持系统时的最佳信息量及信息内容水平。具体而言，向专家护士和新手护士呈现了可能的护理问题的概率数据以及显示的护理问题的优选数量。该研究采用了平衡、重复测量和析因设计。作者设计了两种情景：（1）一名患有糖尿病并发症的肺炎患者；（2）一名糖尿病得到控制但同时发生骨折的患者。使用一个先前开发的诊断性护理决策支持系统原型来展示信息。来自韩国两家医院的18名新手护士和专家护士参与了研究。不同内容水平的结果在专业水平方面并无显著差异，但两组护士对优选信息量的看法存在显著差异。在设计未来基于计算机的决策支持时，应考虑新手和专家不同的信息需求。
