Medical visual analytics as an integrated interaction system for primary care patient data exploration

Primary care physicians must build an overview of complex patient histories within short consultations. Clinical information is often distributed across systems, visits, and temporal events, while the physician still has to maintain situation awareness, patient communication, and clinical accountability. Medical Visual Analytics systems seek to reduce search effort, while their acceptance depends on more than the available visual modules. The interface has to fit clinical tasks, human perception, user preferences, and the workflow of the practice. This work presents a Medical Visual Analytics concept and prototype architecture for primary care. Building on HCI, visual perception, and visual analytics task abstraction, the publication structures the system around clinical tasks, interaction behavior, workflow context, patient data, and visual representation. The implemented prototype connects patient configuration, a clinical data model, service-based data access, an interactive dashboard, anatomical body-based views, and optional MediChat assistance into one physician-centered system. The focus is not autonomous diagnosis, but a source-verifiable environment that helps physicians find, relate, inspect, and correct patient information. The contribution is an HCI-oriented system design that treats interaction, visual prioritization, and source access as design mechanisms intended to support acceptance and clinical usability. Content, layout, visual variables, and interface orchestration are discussed as adaptation targets that respond to user role, task abstraction, interaction behavior, patient-data characteristics, and consultation phase. Building on prior conference work on visual decision support for primary care, this journal extension reframes the system as a user-centered Medical Visual Analytics environment and discusses how such adaptation is intended to reduce search time, support perception, and preserve physician control in everyday outpatient work.

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Publication Details

Journal
Information Visualization
Published
2026-10-17
DOI
https://doi.org/10.1177/14738716261496518
Primary Topic
Data Visualization and Analytics
Type
article
Field-Weighted Citation Impact
0.00
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article

Medical visual analytics as an integrated interaction system for primary care patient data exploration

Kawa Nazemi, Cristian A. Secco
Information Visualization
Data Visualization and Analytics
article

Medical visual analytics as an integrated interaction system for primary care patient data exploration

Kawa Nazemi, Cristian A. Secco
article en

Abstract

Primary care physicians must build an overview of complex patient histories within short consultations. Clinical information is often distributed across systems, visits, and temporal events, while the physician still has to maintain situation awareness, patient communication, and clinical accountability. Medical Visual Analytics systems seek to reduce search effort, while their acceptance depends on more than the available visual modules. The interface has to fit clinical tasks, human perception, user preferences, and the workflow of the practice. This work presents a Medical Visual Analytics concept and prototype architecture for primary care. Building on HCI, visual perception, and visual analytics task abstraction, the publication structures the system around clinical tasks, interaction behavior, workflow context, patient data, and visual representation. The implemented prototype connects patient configuration, a clinical data model, service-based data access, an interactive dashboard, anatomical body-based views, and optional MediChat assistance into one physician-centered system. The focus is not autonomous diagnosis, but a source-verifiable environment that helps physicians find, relate, inspect, and correct patient information. The contribution is an HCI-oriented system design that treats interaction, visual prioritization, and source access as design mechanisms intended to support acceptance and clinical usability. Content, layout, visual variables, and interface orchestration are discussed as adaptation targets that respond to user role, task abstraction, interaction behavior, patient-data characteristics, and consultation phase. Building on prior conference work on visual decision support for primary care, this journal extension reframes the system as a user-centered Medical Visual Analytics environment and discusses how such adaptation is intended to reduce search time, support perception, and preserve physician control in everyday outpatient work.

Information Visualization
Darmstadt University of Applied Sciences (DE)
Good health and well-being
Openalex Percentile: Top 16%
Data Visualization and Analytics
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