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Related Concept Videos

Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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The case management model is a multidisciplinary approach that involves healthcare professionals from diverse disciplines, such as physicians, nurses, therapists, social workers, and pharmacists, working collaboratively to address the various needs of patients. Each healthcare professional brings unique expertise and perspectives, contributing to a more comprehensive understanding of the patient's condition and tailoring treatment plans accordingly.
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Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on

Ali Ganjizadeh1,2, Stephanie J Zawada1,3, Steve G Langer1,2

  • 1Mayo Clinic Artificial Intelligence Laboratory, 200 1st Street SW, Rochester, MN, 55902, USA.

Journal of Imaging Informatics in Medicine
|February 17, 2024
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Summary
This summary is machine-generated.

A new RIL-workflow application streamlines data integration for artificial intelligence (AI) in healthcare. This tool efficiently retrieves and stores patient data from diverse sources, improving AI model training datasets.

Keywords:
Clinical researchHealthcare artificial intelligenceModular applicationMultimodal dataProcess automationWorkflow orchestration

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Area of Science:

  • Medical Informatics
  • Artificial Intelligence in Healthcare
  • Data Science

Background:

  • Data curation and integration are significant challenges in developing AI models for healthcare.
  • Existing tools struggle to efficiently integrate heterogeneous clinical data from various sources.
  • There is a critical need for improved methods for retrieving and storing curated patient data.

Purpose of the Study:

  • To describe a customizable, modular data retrieval application (RIL-workflow) for integrating diverse clinical data.
  • To demonstrate the feasibility of RIL-workflow for creating multimodal databases for AI research.
  • To evaluate user feedback on the RIL-workflow's usability and effectiveness.

Main Methods:

  • Developed a modular data retrieval application (RIL-workflow) using the Camunda workflow automation platform.
  • Integrated clinical notes, images, and prescription data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources.
  • Utilized a web-based graphical user interface (GUI) for workflow automation, error segregation, and data retrieval.

Main Results:

  • Successfully validated the RIL-workflow's capability to aggregate, curate, and manage errors from multiple data sources.
  • Generated a multimodal database suitable for clinical AI research.
  • Collected user feedback highlighting the strengths and weaknesses of the RIL-workflow.

Conclusions:

  • RIL-workflow offers an efficient solution for integrating heterogeneous clinical data, addressing a key bottleneck in AI research.
  • The modular design and GUI facilitate customizable data retrieval and curation for multimodal AI training datasets.
  • The application's source code is publicly available, promoting further development and adoption in clinical AI research.