Appl Clin Inform 2014; 05(01): 249-263
DOI: 10.4338/ACI-2013-11-RA-0095
Research Article
Schattauer GmbH

Text Prediction on Structured Data Entry in Healthcare

A Two-group Randomized Usability Study Measuring the Prediction Impact on User Performance
L. Hua
1  School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA
2  Informatics Institute, University of Missouri, Columbia, MO, USA
,
S. Wang
3  Department of Nursing, Tianjin First Central Hospital, Tianjin, China
,
Y. Gong
1  School of Biomedical Informatics, University of Texas Health Science Center, Houston, TX, USA
› Author Affiliations
Further Information

Correspondence to:

Yang Gong MD, PhD
7000 Fannin St Ste 600
Houston, TX 77030
Phone: 713–500–3547   

Publication History

received: 13 November 2013

accepted: 18 January 2014

Publication Date:
20 December 2017 (online)

 

Summary

Background: Structured data entry pervades computerized patient safety event reporting systems and serves as a key component in collecting patient-related information in electronic health records. Clinicians would spend more time being with patients and arrive at a high probability of proper diagnosis and treatment, if data entry can be completed efficiently and effectively. Historically it has been proven text prediction holds potential for human performance regarding data entry in a variety of research areas.

Objective: This study aimed at examining a function of text prediction proposed for increasing efficiency and data quality in structured data entry.

Methods: We employed a two-group randomized design with fifty-two nurses in this usability study. Each participant was assigned the task of reporting patient falls by answering multiple choice questions either with or without the text prediction function. t-test statistics and linear regression model were applied to analyzing the results of the two groups.

Results: While both groups of participants exhibited a good capacity of accomplishing the assigned task, the results were an overall 13.0% time reduction and 3.9% increase of response accuracy for the group utilizing the prediction function.

Conclusion: As a primary attempt investigating the effectiveness of text prediction in healthcare, study findings validated the necessity of text prediction to structured date entry, and laid the ground for further research improving the effectiveness of text prediction in clinical settings.

Citation: Hua L, Wang S, Gong Y. Text prediction on structured data entry in healthcare: A two-group randomized usability study measuring the prediction impact on user performance. Appl Clin Inf 2014; 5: 249–263 http://dx.doi.org/10.4338/ACI-2013-11-RA-0095


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Conflicts of Interest

The authors of this paper certify that they have no affiliations with or involvement in any organization or entity with any financial interest or non-financial interest in the subject matter or materials discussed in this manuscript.


Correspondence to:

Yang Gong MD, PhD
7000 Fannin St Ste 600
Houston, TX 77030
Phone: 713–500–3547