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 criticalin 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. Ttest, 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.Corresponding Author: Yang Gong, MD, PhD, Yang.Gong@uth.tmc.edu, 713-500-3547, 7000 Fannin St. Suite 165, Houston, TX 77030. Publisher's Disclaimer: This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final citable form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.
Authors' ContributionsYang Gong: Responsible for study design, data collection and analysis, and co-author with review and revision responsibilities Lei Hua: Responsible for the conceptualization, study design, prototype development, testing session observation, data collection and analysis, co-authored the manuscript. Shen Wang: Organized and coordinated with the study participants, assisted in preparing testing cases, data analysis and had review responsibilities.
Conflicts of InterestWe declare there are no conflicts of interest involved in the research.
HHS Public Access
Author Manuscript Author ManuscriptAuthor Manuscript
Author ManuscriptConclusion-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 tex...