Leveraging user's performance in reporting patient safety events by utilizing text prediction in narrative data entry.
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.


CONCLUSIONS
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.
School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA. Electronic address: Yang.Gong@uth.tmc.edu.
Yang
Y
Gong
School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA; Informatics Institute, University of Missouri, Columbia, MO, USA.
Lei
L
Hua
Department of Nursing, Tianjin First Central Hospital, Tianjin, China.
Shen
S
Wang
D002681:China; D006801:Humans; D061214:Patient Safety; D014584:User-Computer Interface
Data entry; Incident reporting; Patient safety; Text prediction; Two-group randomized design; Usability evaluation