Jeffery Alvin D, Kennedy Betsy, Dietrich Mary S, Mion Lorraine C, Novak Laurie L
Appl Clin Inform. 2017 Jul;8(3):763-778. doi: 10.4338/ACI-2017-02-RA-0033. Epub 2017 Dec 29.
Large and readily-available clinical datasets combined with improved computational resources have permitted the exploration of many new research and clinical questions. Predictive analytics, especially for adverse events, has surfaced as one promising application of big data, and although statistical results can be highly accurate, little is known about how nurses perceive this new information and how they might act upon it.
Within the context of recognizing patients at risk for cardiopulmonary arrest, this study explored the possibility of incorporating predictive analytics into clinical workflows by identifying nurses' current information gathering activities and perceptions of probability-related terms.
We used a qualitative description approach for data collection and analysis in order to understand participants' information gathering behaviors and term perceptions in their own words. We conducted one-on-one interviews and a focus group with a total of 10 direct care bedside nurses and 8 charge nurses.
Participants collected information from many sources that we categorized as: Patient, Other People, and Technology. The process by which they gathered information was conducted in an inconsistent order and differed by role. Major themes comprised: (a) attempts to find information from additional sources during uncertainty, (b) always being prepared for the worst-case scenario, and (c) the desire to review more detailed predictions. Use of the words probability, risk, and uncertainty were inconsistent.
In an effort to successfully incorporate predictive analytics into clinical workflows, we have described nurses' perceived work practices for gathering information related to clinical deterioration and nurses' beliefs related to probability-based information. Findings from our study could guide design and implementation efforts of predictive analytics in the clinical arena.Jeffery AD, Kennedy B, Dietrich MS, Mion LC, Novak LL. A Qualitative Exploration of Nurses' Information-Gathering Behaviors Prior to Decision Support Tool Design. Appl Clin Inform 2017; 8: 763-778 https://doi.org/10.4338/ACI-2017-02-RA-0033.
大规模且易于获取的临床数据集,再加上不断改进的计算资源,使得人们能够探索许多新的研究和临床问题。预测分析,尤其是针对不良事件的预测分析,已成为大数据的一项有前景的应用。尽管统计结果可能高度准确,但对于护士如何看待这些新信息以及他们可能如何据此采取行动,我们却知之甚少。
在识别有心脏骤停风险患者的背景下,本研究通过确定护士当前收集信息活动以及对概率相关术语的认知,探索将预测分析纳入临床工作流程的可能性。
我们采用定性描述方法进行数据收集和分析,以便用参与者自己的语言理解他们的信息收集行为和术语认知。我们对总共10名直接护理床边护士和8名护士长进行了一对一访谈和焦点小组讨论。
参与者从多个来源收集信息,我们将这些来源分类为:患者、其他人以及技术。他们收集信息的过程顺序不一致,且因角色不同而有所差异。主要主题包括:(a) 在不确定情况下试图从其他来源获取信息;(b) 始终为最坏情况做好准备;(c) 希望查看更详细的预测。概率、风险和不确定性等词的使用并不一致。
为了成功将预测分析纳入临床工作流程,我们描述了护士在收集与临床病情恶化相关信息时的感知工作实践以及与基于概率的信息相关的信念。我们研究的结果可为临床领域预测分析的设计和实施工作提供指导。杰弗里·A·D、肯尼迪·B、迪特里希·M·S、米昂·L·C、诺瓦克·L·L。决策支持工具设计前护士信息收集行为的定性探索。《应用临床信息学》2017年;8:763 - 778 https://doi.org/10.4338/ACI - 2017 - 02 - RA - 0033