Journal:Infrastructure tools to support an effective radiation oncology learning health system

Full article title	Infrastructure tools to support an effective radiation oncology learning health system
Journal	Journal of Applied Clinical Medical Physics
Author(s)	Kapoor, Rishabh; Sleeman IV, William C.; Ghosh, Preetam; Palta, Jatinder
Author affiliation(s)	Virginia Commonwealth University
Primary contact	rishabh dot kapoor at vcuhealth dot org
Year published	2023
Volume and issue	24(10)
Article #	e14127
DOI	10.1002/acm2.14127
ISSN	1526-9914
Distribution license	Creative Commons Attribution 4.0 International
Website	https://aapm.onlinelibrary.wiley.com/doi/10.1002/acm2.14127
Download	https://aapm.onlinelibrary.wiley.com/doi/pdfdirect/10.1002/acm2.14127 (PDF)

Abstract

Purpose: The concept of the radiation oncology learning health system (RO‐LHS) represents a promising approach to improving the quality of care by integrating clinical, dosimetry, treatment delivery, and research data in real‐time. This paper describes a novel set of tools to support the development of an RO‐LHS and the current challenges they can address.

Methods: We present a knowledge graph‐based approach to map radiotherapy data from clinical databases to an ontology‐based data repository using FAIR principles. This strategy ensures that the data are easily discoverable, accessible, and can be used by other clinical decision support systems. It allows for visualization, presentation, and analysis of valuable data and information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology‐based keyword searching and synonym‐based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, as well as connects to the Bioportal database for relevant clinical attributes retrieval. To identify similar patients, a method involving text corpus creation and vector embedding models (Word2Vec, Doc2Vec, GloVe, and FastText) are employed, using cosine similarity and distance metrics.

Results: The data pipeline and tool were tested with 1,660 patient clinical and dosimetry records, resulting in 504,180 RDF (Resource Description Framework) tuples and visualized data relationships using graph‐based representations. Patient similarity analysis using embedding models showed that the Word2Vec model had the highest mean cosine similarity, while the GloVe model exhibited more compact embeddings with lower Euclidean and Manhattan distances.

Conclusions: The framework and tools described support the development of an RO‐LHS. By integrating diverse data sources and facilitating data discovery and analysis, they contribute to continuous learning and improvement in patient care. The tools enhance the quality of care by enabling the identification of cohorts, clinical decision support, and the development of clinical studies and machine learning (ML) programs in radiation oncology.

Keywords: FAIR, learning health system infrastructure, ontology, Radiation Oncology Ontology, Semantic Web

Background and significance

For the past three decades, there is a growing interest in building learning organizations to address the most pressing and complex business, social, and economic challenges facing society today. [1] For healthcare, the National Academy of Medicine has defined the concept of a learning health system (LHS) as an entity where science, incentive, culture, and informatics are aligned for continuous innovation, with new knowledge capture and discovery as an integral part for practicing evidence-based medicine. [2] The current dependency on randomized controlled clinical trials that use a controlled environment for scientific evidence creation with only a small percent (<3%) of patient samples is inadequate now and may be irrelevant in the future since these trials take too much time, are too expensive, and are fraught with questions of generalizability. The Agency for Healthcare Research and Quality has also been promoting the development of LHSs as part of a key strategy for healthcare organizations to make transformational changes to improve healthcare quality and value. Large-scale healthcare systems are now recognizing the need to build infrastructure capable of continuous learning and improvement in delivering care to patients and address critical population health issues. [3] In an LHS, data collection should be performed from various sources such as electronic health records (EHRs), treatment delivery records, imaging records, patient-generated data records, and administrative and claims data, which then allows for this aggregated data to be analyzed for generating new insights and knowledge that can be used to improve patient care and outcomes.

Notes

This presentation is faithful to the original, with only a few minor changes to presentation, though grammar and word usage was substantially updated for improved readability. In some cases important information was missing from the references, and that information was added.