Difference between revisions of "Visualizing unstructured patient data for assessing diagnostic and therapeutic history"
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==Abstract<ref name="Deng2014">Deng Y, Denecke K. Visualizing unstructured patient data for assessing diagnostic and therapeutic history. Stud Health Technol Inform. 2014;205:1158-62. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/25160371.</ref>== | ==Abstract<ref name="Deng2014">Deng Y, Denecke K. Visualizing unstructured patient data for assessing diagnostic and therapeutic history. Stud Health Technol Inform. 2014;205:1158-62. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/25160371.</ref>== | ||
===Background=== | ===Background=== | ||
| − | Information stored in an electronic health record | + | Information is stored in an electronic health record in various data types, many of which are unstructured. As a result, clinicians may find it difficult to gain a holistic impression of a patient's current medical condition when data collected over the course of many years includes a significant amount of lab work, many diagnostic exams, numerous procedures, and even hospitalizations. Review of this data can require a significant amount of time if no substantive overview of all of the available data is present. |
| − | + | ||
| − | + | ||
===Objective=== | ===Objective=== | ||
| + | In this work, the authors address the question of representing the current status of a patient that is described in form of unstructured text to enable physicians to get an overview quickly. The authors introduce an approach that visualizes patient information extracted from the documents of the EHR in an easy understandable manner, namely by means of tag cloud visualization. Tag cloud is a common visualization method in the Web 2.0 community. Studies showed that they enhance the perception of (Web) documents [6] and they support an explorative search when it is difficult to specify a concrete query. These benefits perfectly address the challenges of accessing and monitoring patient data documented in unstructured documents. In this context, using tag cloud for information visualization is still relatively unexplored. | ||
| + | |||
| + | ===Study Design and Method=== | ||
| + | For the study, tags are selected from a text (1) just by frequency (Bag of words), or (2) based on their part of speech (POS). A first type of tags (Bag of words) is generated using all the words of a document except stop words. All the tags are rendered with same color. | ||
| + | |||
| + | To generate the second type of tags, the tokens of a text are annotated with their part of speech labels. The lexical category of the remaining words of a document is after removal of stop words. The authors intuitively highlight the nouns, verbs, and adjectives with three primary colors (red, yellow and blue) due to the decisive roles of these lexical categories in the meaning delivery. Their applicability and correctness of the authors' intuition from meaning representation need to be analyzed in experiments. | ||
| + | |||
| + | In order to figure out the effectiveness of the tag clouds, a user study has been performed in the neurosurgical department of a university hospital. Three residents were asked to assess the tag clouds generated for different texts and to judge whether (1) the tag clouds are useful to get an overview on the patient status, (2) shown words are relevant and (3) visualization of relevant aspects is clear. Six medical reports in German (three surgical operation reports, two pathological reports and one radiological report) were used to generate the tag clouds. | ||
| + | Before starting to answer the questions, the physicians were introduced to imagine the following simulated task scenario: | ||
| − | + | In the outpatient department, facing a patient who has never seen before, but he/she was treated in the hospital already, you have the complete patient documentation in computer. You are expected to grasp the basic status of this patient and start the treatment in very short period of time. | |
| + | Based on this scenario, all six medical reports visualized by the two types of tag cloud (generated through bag of words approach or POS tagger) were judged on a rating scale from 1 (bad) to 5 (excellent). | ||
===Results=== | ===Results=== | ||
Revision as of 00:02, 25 October 2015
Having access to relevant patient data is crucial for clinical decision making. The data is often documented in unstructured texts and collected in the electronic health record. In this paper, the authors evaluated an approach to visualize information extracted from clinical documents by means of tag cloud. [1]
Contents
Abstract[1]
Background
Information is stored in an electronic health record in various data types, many of which are unstructured. As a result, clinicians may find it difficult to gain a holistic impression of a patient's current medical condition when data collected over the course of many years includes a significant amount of lab work, many diagnostic exams, numerous procedures, and even hospitalizations. Review of this data can require a significant amount of time if no substantive overview of all of the available data is present.
Objective
In this work, the authors address the question of representing the current status of a patient that is described in form of unstructured text to enable physicians to get an overview quickly. The authors introduce an approach that visualizes patient information extracted from the documents of the EHR in an easy understandable manner, namely by means of tag cloud visualization. Tag cloud is a common visualization method in the Web 2.0 community. Studies showed that they enhance the perception of (Web) documents [6] and they support an explorative search when it is difficult to specify a concrete query. These benefits perfectly address the challenges of accessing and monitoring patient data documented in unstructured documents. In this context, using tag cloud for information visualization is still relatively unexplored.
Study Design and Method
For the study, tags are selected from a text (1) just by frequency (Bag of words), or (2) based on their part of speech (POS). A first type of tags (Bag of words) is generated using all the words of a document except stop words. All the tags are rendered with same color.
To generate the second type of tags, the tokens of a text are annotated with their part of speech labels. The lexical category of the remaining words of a document is after removal of stop words. The authors intuitively highlight the nouns, verbs, and adjectives with three primary colors (red, yellow and blue) due to the decisive roles of these lexical categories in the meaning delivery. Their applicability and correctness of the authors' intuition from meaning representation need to be analyzed in experiments.
In order to figure out the effectiveness of the tag clouds, a user study has been performed in the neurosurgical department of a university hospital. Three residents were asked to assess the tag clouds generated for different texts and to judge whether (1) the tag clouds are useful to get an overview on the patient status, (2) shown words are relevant and (3) visualization of relevant aspects is clear. Six medical reports in German (three surgical operation reports, two pathological reports and one radiological report) were used to generate the tag clouds.
Before starting to answer the questions, the physicians were introduced to imagine the following simulated task scenario:
In the outpatient department, facing a patient who has never seen before, but he/she was treated in the hospital already, you have the complete patient documentation in computer. You are expected to grasp the basic status of this patient and start the treatment in very short period of time.
Based on this scenario, all six medical reports visualized by the two types of tag cloud (generated through bag of words approach or POS tagger) were judged on a rating scale from 1 (bad) to 5 (excellent).
Results
Conclusion
Comments
References
- ↑ 1.0 1.1 Deng Y, Denecke K. Visualizing unstructured patient data for assessing diagnostic and therapeutic history. Stud Health Technol Inform. 2014;205:1158-62. http://www-ncbi-nlm-nih-gov.ezproxyhost.library.tmc.edu/pubmed/25160371.
