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A flexible framework for visualizing and exploring patient misdiagnosis over time.
Widanagamaachchi, Wathsala; Peterson, Kelly; Chapman, Alec; Classen, David; Jones, Makoto.
Afiliação
  • Widanagamaachchi W; University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA. Electronic address: wathsala.widanagamaachchi@hsc.utah.edu.
  • Peterson K; University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA; Veterans Health Administration Office of Analytics and Performance Integration, 810 Vermont Ave., NW
  • Chapman A; University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA; Department of Population Health Sciences, University of Utah School of Medicine, Williams Building,
  • Classen D; University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA. Electronic address: david.classen@utah.edu.
  • Jones M; University of Utah School of Medicine Division of Epidemiology, 295 Chipeta Way, Salt Lake City, 84132, UT, USA; VA Salt Lake City Health Care System, 500 Foothill Dr, Salt Lake City, 84148, UT, USA. Electronic address: makoto.jones@hsc.utah.edu.
J Biomed Inform ; 134: 104178, 2022 10.
Article em En | MEDLINE | ID: mdl-36064112
ABSTRACT
Diagnosis is a complex and ambiguous process and yet, it is the critical hinge point for all subsequent clinical reasoning and decision-making. Tracking the quality of the patient diagnostic process has the potential to provide valuable insights in improving the diagnostic accuracy and to reduce downstream errors but needs to be informative, timely, and efficient at scale. However, due to the rate at which healthcare data are captured on a daily basis, manually reviewing the diagnostic history of each patient would be a severely taxing process without efficient data reduction and representation. Application of data visualization and visual analytics to healthcare data is one promising approach for addressing these challenges. This paper presents a novel flexible visualization and analysis framework for exploring the patient diagnostic process over time (i.e., patient diagnosis paths). Our framework allows users to select a specific set of patients, events and/or conditions, filter data based on different attributes, and view further details on the selected patient cohort while providing an interactive view of the resulting patient diagnosis paths. A practical demonstration of our system is presented with a case study exploring infection-based patient diagnosis paths.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Visualização de Dados Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Assunto principal: Visualização de Dados Tipo de estudo: Diagnostic_studies / Prognostic_studies Limite: Humans Idioma: En Revista: J Biomed Inform Assunto da revista: INFORMATICA MEDICA Ano de publicação: 2022 Tipo de documento: Article