Development of Medical Imaging Data Standardization for Imaging-Based Observational Research: OMOP Common Data Model Extension

The rapid growth of artificial intelligence (AI) and deep learning techniques require access to large inter-institutional cohorts of data to enable the development of robust models, e.g., targeting the identification of disease biomarkers and quantifying disease progression and treatment efficacy. The Observational Medical Outcomes Partnership Common Data Model (OMOP CDM) has been designed to accommodate a harmonized representation of observational healthcare data. This study proposes the Medical Imaging CDM (MI-CDM) extension, adding two new tables and two vocabularies to the OMOP CDM to address the structural and semantic requirements to support imaging research. The tables provide the capabilities of linking DICOM data sources as well as tracking the provenance of imaging features derived from those images. The implementation of the extension enables phenotype definitions using imaging features and expanding standardized computable imaging biomarkers. This proposal offers a comprehensive and unified approach for conducting imaging research and outcome studies utilizing imaging features.

Addressing the Challenges of Implementing Artificial Intelligence Tools in Clinical Practice: Principles From Experience

The multitude of artificial intelligence (AI)-based solutions, vendors, and platforms poses a challenging proposition to an already complex clinical radiology practice. Apart from assessing and ensuring acceptable local performance and workflow fit to improve imaging services, AI tools require multiple stakeholders, including clinical, technical, and financial, who collaborate to move potential deployable applications to full clinical deployment in a structured and efficient manner. Postdeployment monitoring and surveillance of such tools require an infrastructure that ensures proper and safe use. Herein, the authors describe their experience and framework for implementing and supporting the use of AI applications in radiology workflow.

Implementation of Clinical Artificial Intelligence in Radiology: Who Decides and How?

As the role of artificial intelligence (AI) in clinical practice evolves, governance structures oversee the implementation, maintenance, and monitoring of clinical AI algorithms to enhance quality, manage resources, and ensure patient safety. In this article, a framework is established for the infrastructure required for clinical AI implementation and presents a road map for governance. The road map answers four key questions: Who decides which tools to implement? What factors should be considered when assessing an application for implementation? How should applications be implemented in clinical practice? Finally, how should tools be monitored and maintained after clinical implementation? Among the many challenges for the implementation of AI in clinical practice, devising flexible governance structures that can quickly adapt to a changing environment will be essential to ensure quality patient care and practice improvement objectives.

Importance of clinical decision support system response time monitoring: a case report

Clinical decision support (CDS) systems are prevalent in electronic health records and drive many safety advantages. However, CDS systems can also cause unintended consequences. Monitoring programs focused on alert firing rates are important to detect anomalies and ensure systems are working as intended. Monitoring efforts do not generally include system load and time to generate decision support, which is becoming increasingly important as more CDS systems rely on external, web-based content and algorithms. We report a case in which a web-based service caused significant increase in the time to generate decision support, in turn leading to marked delays in electronic health record system responsiveness, which could have led to patient safety events. Given this, it is critical to consider adding decision support-time generation to ongoing CDS system monitoring programs.

Tufts Health Sciences Database: lessons, issues, and opportunities

The authors present their seven-year experience with developing the Tufts Health Sciences Database (Tufts HSDB), a database-driven information management system that combines the strengths of a digital library, content delivery tools, and curriculum management. They describe a future where online tools will provide a health sciences learning infrastructure that fosters the work of an increasingly interdisciplinary community of learners and allows content to be shared across institutions as well as with academic and commercial information repositories. The authors note the key partners in Tufts HSDB's success--the close collaboration of the health sciences library, educational affairs, and information technology staff. Tufts HSDB moved quickly from serving the medical curriculum to supporting Tufts' veterinary, dental, biomedical sciences, and nutrition schools, thus leveraging Tufts HSDB research and development with university-wide efforts including Internet2 middleware, wireless access, information security, and digital libraries. The authors identify major effects on teaching and learning, e.g., what is better taught with multimedia, how faculty preparation and student learning time can be more efficient and effective, how content integration for interdisciplinary teaching and learning is promoted, and how continuous improvement methods can be integrated. Also addressed are issues of faculty development, copyright and intellectual property, budgetary concerns, and coordinating IT across schools and hospitals. The authors describe Tufts' recent experience with sharing its infrastructure with other schools, and welcome inquiries from those wishing to explore national and international partnerships to create a truly open and integrated infrastructure for education across the health sciences.