How Image Exchange Breaks Down: the Image Library Perspective

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow.

Certified Imaging Informatics Professionals (CIIP) Demonstrate Value to the Healthcare Industry and Focus on Quality Through the ABII 10-Year Requirements Practice Option

Strengthening the field of imaging informatics by further defining standards and advocating for continuous education are the cornerstones of the American Board of Imaging Informatics (ABII). ABII is the non-profit organization that governs the Imaging Informatics Professional certification program. ABII is responsible for awarding the Certified Imaging Informatics Professional (CIIP) designation to candidates who meet specified educational and experience-based criteria and pass a qualifying exam (1). For this paper, we analyzed Quality Improvement (QI) projects submitted to ABII for satisfaction of the 10-year requirements in 2017-2021. The project reports demonstrated a variety of interventions undertaken to ultimately improve patient care. A retrospective review of these reports exemplifies the critical role the Certified Imaging Informatics Professionals have in delivery of high quality, safe healthcare and their vital contributions to the healthcare industry and practice of medicine.