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LetterVis: a letter-space view of clinic letters

Qiru Wang Orcid Logo, Bob Laramee Orcid Logo, Arron Lacey Orcid Logo, Owen Pickrell Orcid Logo

The Visual Computer, Volume: 37, Issue: 9-11, Pages: 2643 - 2656

Swansea University Authors: Bob Laramee Orcid Logo, Arron Lacey Orcid Logo, Owen Pickrell Orcid Logo

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DOI (Published version): 10.1007/s00371-021-02171-w

Abstract

The number of electronic health records (EHRs) collected by healthcare providers is growing at an unprecedented pace. Clinicians often compose detailed clinic letters to record as much essential information during consultations as they can. This increases the workload of analyzing these letters, per...

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Published in: The Visual Computer
ISSN: 0178-2789 1432-2315
Published: Springer Science and Business Media LLC 2021
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URI: https://cronfa.swan.ac.uk/Record/cronfa57136
first_indexed 2021-06-25T11:06:52Z
last_indexed 2023-01-11T14:36:51Z
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spelling 2022-08-17T09:44:57.9997392 v2 57136 2021-06-16 LetterVis: a letter-space view of clinic letters 7737f06e2186278a925f6119c48db8b1 0000-0002-3874-6145 Bob Laramee Bob Laramee true false b69d245574e754d2637cc9e76379fe11 0000-0001-7983-8073 Arron Lacey Arron Lacey true false 1c3044b5ff7a6552ff5e8c9e3901c807 0000-0003-4396-5657 Owen Pickrell Owen Pickrell true false 2021-06-16 MACS The number of electronic health records (EHRs) collected by healthcare providers is growing at an unprecedented pace. Clinicians often compose detailed clinic letters to record as much essential information during consultations as they can. This increases the workload of analyzing these letters, performing individual and collective analysis, and clinical decision making. This paper presents a novel visualization tool, LetterVis, to support the analysis of clinic letters through advanced interactive visual designs and queries. We describe a letter-space that facilities the visual exploration of content and patterns inside a letter. Letters are processed using natural language processing techniques and explored in multiple linked interactive views providing different levels of abstraction. The tool includes customized visual designs and views for visualizing antiepileptic drugs (AEDs). We provide a range of filtering and selection options to assist pattern finding and outlier detection. We demonstrate LetterVis with three case studies using anonymized clinic letters, revealing insight that is normally either time-consuming or impossible to observe. Domain expert partners from EHR analysis review the software and are involved in every phase from the initial design to evaluation. Journal Article The Visual Computer 37 9-11 2643 2656 Springer Science and Business Media LLC 0178-2789 1432-2315 Information Visualization; Electronic Health Records; Visual Analytics 1 9 2021 2021-09-01 10.1007/s00371-021-02171-w COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University 2022-08-17T09:44:57.9997392 2021-06-16T09:41:31.6557542 Faculty of Medicine, Health and Life Sciences Swansea University Medical School - Medicine Qiru Wang 0000-0003-3397-308x 1 Bob Laramee 0000-0002-3874-6145 2 Arron Lacey 0000-0001-7983-8073 3 Owen Pickrell 0000-0003-4396-5657 4 57136__20274__9e3f85f40bfa43f8842fce96725fd49b.pdf 57136.pdf 2021-06-28T11:00:36.7236944 Output 1024456 application/pdf Accepted Manuscript true 2022-06-12T00:00:00.0000000 true eng
title LetterVis: a letter-space view of clinic letters
spellingShingle LetterVis: a letter-space view of clinic letters
Bob Laramee
Arron Lacey
Owen Pickrell
title_short LetterVis: a letter-space view of clinic letters
title_full LetterVis: a letter-space view of clinic letters
title_fullStr LetterVis: a letter-space view of clinic letters
title_full_unstemmed LetterVis: a letter-space view of clinic letters
title_sort LetterVis: a letter-space view of clinic letters
author_id_str_mv 7737f06e2186278a925f6119c48db8b1
b69d245574e754d2637cc9e76379fe11
1c3044b5ff7a6552ff5e8c9e3901c807
author_id_fullname_str_mv 7737f06e2186278a925f6119c48db8b1_***_Bob Laramee
b69d245574e754d2637cc9e76379fe11_***_Arron Lacey
1c3044b5ff7a6552ff5e8c9e3901c807_***_Owen Pickrell
author Bob Laramee
Arron Lacey
Owen Pickrell
author2 Qiru Wang
Bob Laramee
Arron Lacey
Owen Pickrell
format Journal article
container_title The Visual Computer
container_volume 37
container_issue 9-11
container_start_page 2643
publishDate 2021
institution Swansea University
issn 0178-2789
1432-2315
doi_str_mv 10.1007/s00371-021-02171-w
publisher Springer Science and Business Media LLC
college_str Faculty of Medicine, Health and Life Sciences
hierarchytype
hierarchy_top_id facultyofmedicinehealthandlifesciences
hierarchy_top_title Faculty of Medicine, Health and Life Sciences
hierarchy_parent_id facultyofmedicinehealthandlifesciences
hierarchy_parent_title Faculty of Medicine, Health and Life Sciences
department_str Swansea University Medical School - Medicine{{{_:::_}}}Faculty of Medicine, Health and Life Sciences{{{_:::_}}}Swansea University Medical School - Medicine
document_store_str 1
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description The number of electronic health records (EHRs) collected by healthcare providers is growing at an unprecedented pace. Clinicians often compose detailed clinic letters to record as much essential information during consultations as they can. This increases the workload of analyzing these letters, performing individual and collective analysis, and clinical decision making. This paper presents a novel visualization tool, LetterVis, to support the analysis of clinic letters through advanced interactive visual designs and queries. We describe a letter-space that facilities the visual exploration of content and patterns inside a letter. Letters are processed using natural language processing techniques and explored in multiple linked interactive views providing different levels of abstraction. The tool includes customized visual designs and views for visualizing antiepileptic drugs (AEDs). We provide a range of filtering and selection options to assist pattern finding and outlier detection. We demonstrate LetterVis with three case studies using anonymized clinic letters, revealing insight that is normally either time-consuming or impossible to observe. Domain expert partners from EHR analysis review the software and are involved in every phase from the initial design to evaluation.
published_date 2021-09-01T05:15:59Z
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score 11.048302

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