Trial reportComputer methods and programs in biomedicine2016
Leveraging user's performance in reporting patient safety events by utilizing text prediction in narrative data entry.
Trial report in Computer methods and programs in biomedicine, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
3 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Routine Health Information System (RHIS) improvements for strengthened health system management.The Cochrane database of systematic reviews · 2020Pooled it
- Enhancing Patient Safety Event Reporting. A Systematic Review of System Design Features.Applied clinical informatics · 2017Pooled it
- Words prediction based on N-gram model for free-text entry in electronic health records.Health information science and systems · 2019Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
backgroundNarrative 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.
objectiveThis study examined two functions of text prediction intended for increasing efficiency and data quality of text data entry reporting patient safety events.
methodsThe 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.
resultsWhile 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.
conclusionAs 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.
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