Abstract
Electronic medical records (EMRs) have been used extensively in most medical institutions for more than a decade in Taiwan. However, information overload associated with rapid accumulation of large amounts of clinical narratives has threatened the effective use of EMRs. This situation is further worsened by the use of “copying and pasting”, leading to lots of redundant information in clinical notes. This study aimed to apply natural language processing techniques to address this problem. New information in longitudinal clinical notes was identified based on a bigram language model. The accuracy of automated identification of new information was evaluated using expert annotations as the reference standard. A two-stage cross-over user experiment was conducted to evaluate the impact of highlighting of new information on task demands, task performance, and perceived workload. The automated method identified new information with an F1 score of 0.833. The user experiment found a significant decrease in perceived workload associated with a significantly higher task performance. In conclusion, automated identification of new information in clinical notes is feasible and practical. Highlighting of new information enables healthcare professionals to grasp key information from clinical notes with less perceived workload.
Highlights
Electronic medical records (EMRs) have been developed in Taiwan for more than a decade [1].They have been implemented in most of the larger medical institutions to store information about encounters and events between patients and healthcare systems [2]
The proportion of lines with new information was negatively correlated with the average number of lines per progress note (Figure 2B)
This study found that a substantial proportion of clinical notes contained redundant information instead of new information
Summary
Electronic medical records (EMRs) have been developed in Taiwan for more than a decade [1]. They have been implemented in most of the larger medical institutions to store information about encounters and events between patients and healthcare systems [2]. The implementation of EMRs enables large-scale storage and collection of patient data and makes the exchange of healthcare information between healthcare facilities possible through the Electronic Medical Record Exchange. EMRs. The immediate accessibility of patient information is likely to help healthcare professionals improve patient care delivery and enhance the quality of medical decision-making [3,4].
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