A deep profiling and visualization framework to audit clinical assessment variation

Andrew Wen, Feichen Shen, Sungrim Moon, Hongfang D Liu, Jungwei Fan

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Clinical assessment variation (CAV) has a profound impact on patient outcomes, and appropriate tooling is critically needed to help understand and guide necessary interventions. In this study, we propose an intuitive approach to visualizing CAV and summarizing the contexts pertinent to decision-making. By superimposing the response variable and clusters learned according to the explanatory variables, a color-coded 2D scatter plot can be rendered to show the spatial proximity and semantic composition of the clusters. Without loss of generality, an example application on preoperative patient assessment demonstrated the approach can assist in auditing inconsistent human decisions and informing the reconciliation process. The methods will also benefit refining of clinical assessment guidelines by systematically eliciting practice-based knowledge.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE 33rd International Symposium on Computer-Based Medical Systems, CBMS 2020
EditorsAlba Garcia Seco de Herrera, Alejandro Rodriguez Gonzalez, KC Santosh, Zelalem Temesgen, Bridget Kane, Paolo Soda
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages546-551
Number of pages6
ISBN (Electronic)9781728194295
DOIs
StatePublished - Jul 2020
Event33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020 - Virtual, Online, United States
Duration: Jul 28 2020Jul 30 2020

Publication series

NameProceedings - IEEE Symposium on Computer-Based Medical Systems
Volume2020-July
ISSN (Print)1063-7125

Conference

Conference33rd IEEE International Symposium on Computer-Based Medical Systems, CBMS 2020
CountryUnited States
CityVirtual, Online
Period7/28/207/30/20

Keywords

  • Clinical practice variation
  • Decision-support system
  • Deep learning
  • Information visualization
  • Patient similarity

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging
  • Computer Science Applications

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