Literature DB >> 26432354

Exploring methods for identifying related patient safety events using structured and unstructured data.

Allan Fong1, A Zachary Hettinger2, Raj M Ratwani2.   

Abstract

Most healthcare systems have implemented patient safety event reporting systems to identify safety hazards. Searching the safety event data to find related patient safety reports and identify trends is challenging given the complexity and quantity of these reports. Structured data elements selected by the event reporter may be inaccurate and the free-text narrative descriptions are difficult to analyze. In this paper we present and explore methods for utilizing both the unstructured free-text and structured data elements in safety event reports to identify and rank similar events. We evaluate the results of three different free-text search methods, including a unique topic modeling adaptation, and structured element weights, using a patient fall use case. The various search techniques and weight combinations tended to prioritize different aspects of the event reports leading to different search and ranking results. These search and prioritization methods have the potential to greatly improve patient safety officers, and other healthcare workers, understanding of which safety event reports are related.
Copyright © 2015 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Patient safety event reports; Relevance; Search; Structured data; Topic modeling; Unstructured data

Mesh:

Year:  2015        PMID: 26432354     DOI: 10.1016/j.jbi.2015.09.011

Source DB:  PubMed          Journal:  J Biomed Inform        ISSN: 1532-0464            Impact factor:   6.317


  6 in total

1.  Merging Data Diversity of Clinical Medical Records to Improve Effectiveness.

Authors:  Berit I Helgheim; Rui Maia; Joao C Ferreira; Ana Lucia Martins
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2.  Using convolutional neural networks to identify patient safety incident reports by type and severity.

Authors:  Ying Wang; Enrico Coiera; Farah Magrabi
Journal:  J Am Med Inform Assoc       Date:  2019-12-01       Impact factor: 4.497

3.  Latent Patient Cluster Discovery for Robust Future Forecasting and New-Patient Generalization.

Authors:  Ting Qian; Aaron J Masino
Journal:  PLoS One       Date:  2016-09-16       Impact factor: 3.240

4.  Using multiclass classification to automate the identification of patient safety incident reports by type and severity.

Authors:  Ying Wang; Enrico Coiera; William Runciman; Farah Magrabi
Journal:  BMC Med Inform Decis Mak       Date:  2017-06-12       Impact factor: 2.796

Review 5.  Utilizing Advanced Technologies to Augment Pharmacovigilance Systems: Challenges and Opportunities.

Authors:  David John Lewis; John Fraser McCallum
Journal:  Ther Innov Regul Sci       Date:  2019-12-28       Impact factor: 1.778

6.  Innovation in Pharmacovigilance: Use of Artificial Intelligence in Adverse Event Case Processing.

Authors:  Juergen Schmider; Krishan Kumar; Chantal LaForest; Brian Swankoski; Karen Naim; Patrick M Caubel
Journal:  Clin Pharmacol Ther       Date:  2018-12-11       Impact factor: 6.875

  6 in total

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