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Sensor-Based Architecture for Medical Imaging Workflow Analysis

  • Systems-Level Quality Improvement
  • Published:
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Abstract

The growing use of computer systems in medical institutions has been generating a tremendous quantity of data. While these data have a critical role in assisting physicians in the clinical practice, the information that can be extracted goes far beyond this utilization. This article proposes a platform capable of assembling multiple data sources within a medical imaging laboratory, through a network of intelligent sensors. The proposed integration framework follows a SOA hybrid architecture based on an information sensor network, capable of collecting information from several sources in medical imaging laboratories. Currently, the system supports three types of sensors: DICOM repository meta-data, network workflows and examination reports. Each sensor is responsible for converting unstructured information from data sources into a common format that will then be semantically indexed in the framework engine. The platform was deployed in the Cardiology department of a central hospital, allowing identification of processes’ characteristics and users’ behaviours that were unknown before the utilization of this solution.

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Acknowledgments

LBS is funded by the FCT (Fundação para a Ciência e a Tecnologia) under the grant SFRH/BD/79389/2011. This work has also received support from the EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant n° 115372).

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Authors and Affiliations

  1. Universidade de Aveiro – DETI/IEETA, Aveiro, Portugal

    Luís A. Bastião Silva, Samuel Campos, Carlos Costa & José Luis Oliveira

Authors
  1. Luís A. Bastião Silva
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  2. Samuel Campos
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  3. Carlos Costa
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  4. José Luis Oliveira
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Corresponding author

Correspondence to Luís A. Bastião Silva.

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This article is part of the Topical Collection on Systems-Level Quality Improvement

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Silva, L.A.B., Campos, S., Costa, C. et al. Sensor-Based Architecture for Medical Imaging Workflow Analysis. J Med Syst 38, 63 (2014). https://doi.org/10.1007/s10916-014-0063-8

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  • DOI: https://doi.org/10.1007/s10916-014-0063-8

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