通过在叙述性数据录入中利用文本预测来利用用户报告患者安全事件的表现。
Leveraging user's performance in reporting patient safety events by utilizing text prediction in narrative data entry.
作者信息
Gong Yang, Hua Lei, Wang Shen
机构信息
School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA.
School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA; Informatics Institute, University of Missouri, Columbia, MO, USA.
出版信息
Comput Methods Programs Biomed. 2016 Jul;131:181-9. doi: 10.1016/j.cmpb.2016.03.031. Epub 2016 Apr 8.
BACKGROUND
Narrative data entry pervades computerized health information systems and serves as a key component in collecting patient-related information in electronic health records and patient safety event reporting systems. The quality and efficiency of clinical data entry are critical in arriving at an optimal diagnosis and treatment. The application of text prediction holds potential for enhancing human performance of data entry in reporting patient safety events.
OBJECTIVE
This study examined two functions of text prediction intended for increasing efficiency and data quality of text data entry reporting patient safety events.
METHODS
The study employed a two-group randomized design with 52 nurses. The nurses were randomly assigned into a treatment group or a control group with a task of reporting five patient fall cases in Chinese using a web-based test system, with or without the prediction functions. T-test, Chi-square and linear regression model were applied to evaluating the outcome differences in free-text data entry between the groups.
RESULTS
While both groups of participants exhibited a good capacity for accomplishing the assigned task of reporting patient falls, the results from the treatment group showed an overall increase of 70.5% in text generation rate, an increase of 34.1% in reporting comprehensiveness score and a reduction of 14.5% in the non-adherence of the comment fields. The treatment group also showed an increasing text generation rate over time, whereas no such an effect was observed in the control group.
CONCLUSION
As an attempt investigating the effectiveness of text prediction functions in reporting patient safety events, the study findings proved an effective strategy for assisting reporters in generating complementary free text when reporting a patient safety event. The application of the strategy may be effective in other clinical areas when free text entries are required.
背景
叙述性数据录入在计算机化健康信息系统中普遍存在，并且是在电子健康记录和患者安全事件报告系统中收集患者相关信息的关键组成部分。临床数据录入的质量和效率对于实现最佳诊断和治疗至关重要。文本预测的应用在提高报告患者安全事件时数据录入的人员绩效方面具有潜力。
目的
本研究考察了文本预测的两种功能，旨在提高报告患者安全事件的文本数据录入的效率和数据质量。
方法
该研究采用两组随机设计，共有52名护士参与。护士们被随机分配到治疗组或对照组，使用基于网络的测试系统以中文报告五例患者跌倒病例，该系统有或没有预测功能。应用t检验、卡方检验和线性回归模型来评估两组之间自由文本数据录入的结果差异。
结果
虽然两组参与者在完成报告患者跌倒的指定任务方面都表现出良好的能力，但治疗组的结果显示文本生成率总体提高了70.5%，报告全面性得分提高了34.1%，评论字段不依从性降低了14.5%。治疗组的文本生成率也随时间增加，而对照组未观察到这种效果。
结论
作为一项调查文本预测功能在报告患者安全事件中的有效性的尝试，研究结果证明了一种有效的策略，可协助报告者在报告患者安全事件时生成补充性自由文本。当需要自由文本录入时，该策略的应用在其他临床领域可能也是有效的。
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