Automated data abstraction for quality surveillance and outcome assessment in radiation oncology

Kapoor, Rishabh; Sleeman, William C.; Nalluri, Joseph J.; Turner, Patricia A.; Bose, Priyankar; Cherevko, Andrii; Srinivasan, Sriram; Syed, Khajamoinuddin; Ghosh, Preetam; Hagan, Michael P.; Palta, Jatinder

doi:10.1002/acm2.13308

Cited by 7 publications

(6 citation statements)

References 20 publications

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“…HINGE has employed the FHIR interfaces with the EHR to retrieve the required patient details such as demographics, list of allergies, prescribed active medications, vitals, lab results, surgery, radiology, pathology reports, active diagnosis, referral, encounter, and survival information. We have described the design and implementation HINGE in our previous publication 15 . In summary, HINGE is designed to automatically capture and abstract clinical, treatment planning, and delivery data for cancer patients receiving radiotherapy.…”

Section: Methodsmentioning

confidence: 99%

“…We have described the design and implementation HINGE in our previous publication. 15 In summary, HINGE is designed to automatically capture and abstract clinical, treatment planning, and delivery data for cancer patients receiving radiotherapy. The system uses disease site‐specific “smart” templates to facilitate the entry of relevant clinical information by physicians and clinical staff.…”

Section: Methodsmentioning

confidence: 99%

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Infrastructure tools to support an effective Radiation Oncology Learning Health System

Kapoor,

Sleeman,

Ghosh

et al. 2023

J Applied Clin Med Phys

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PurposeRadiation Oncology Learning Health System (RO‐LHS) is a promising approach to improve the quality of care by integrating clinical, dosimetry, treatment delivery, research data in real‐time. This paper describes a novel set of tools to support the development of a RO‐LHS and the current challenges they can address.MethodsWe present a knowledge graph‐based approach to map radiotherapy data from clinical databases to an ontology‐based data repository using FAIR concepts. 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 data analyses of valuable information to identify trends and patterns in patient outcomes. We designed a search engine that utilizes ontology‐based keyword searching, synonym‐based term matching that leverages the hierarchical nature of ontologies to retrieve patient records based on parent and children classes, 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.ResultsThe data pipeline and tool were tested with 1660 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.ConclusionsThe framework and tools described support the development of a 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 programs in radiation oncology.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Infrastructure tools to support an effective Radiation Oncology Learning Health System

Kapoor,

Sleeman,

Ghosh

et al. 2023

J Applied Clin Med Phys

Self Cite

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“… 6 TG‐263 has also been implemented in a collaborative quality initiative and data abstraction frameworks that collect and analyze practice patterns and patient outcomes. 7 , 8 , 9 , 10 However, there are still present challenges that prevent the complete adoption of the TG‐263 naming formalism. 11 …”

Section: Introductionmentioning

confidence: 99%

“…The TG‐263 naming convention improved data quality and sharing at the clinic and national level, and was adopted by the Global Quality Assurance of Radiation Therapy Clinical Trials Harmonization Group (GHG) for the consensus guidelines of organ‐at‐risk delineation in clinical trials 6 . TG‐263 has also been implemented in a collaborative quality initiative and data abstraction frameworks that collect and analyze practice patterns and patient outcomes 7–10 . However, there are still present challenges that prevent the complete adoption of the TG‐263 naming formalism 11 …”

Section: Introductionmentioning

confidence: 99%

Perceptions on and roadblocks to implementation of standardized nomenclature in radiation oncology: A survey from TG‐263U1

Covington,

Suresh,

Anderson

et al. 2024

J Applied Clin Med Phys

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PurposeAAPM Task Group No. 263U1 (Update to Report No. 263 – Standardizing Nomenclatures in Radiation Oncology) disseminated a survey to receive feedback on utilization, gaps, and means to facilitate further adoption.MethodsThe survey was created by TG‐263U1 members to solicit feedback from physicists, dosimetrists, and physicians working in radiation oncology. Questions on the adoption of the TG‐263 standard were coupled with demographic information, such as clinical role, place of primary employment (e.g., private hospital, academic center), and size of institution. The survey was emailed to all AAPM, AAMD, and ASTRO members.ResultsThe survey received 463 responses with 310 completed survey responses used for analysis, of whom most had the clinical role of medical physicist (73%) and the majority were from the United States (83%). There were 83% of respondents who indicated that they believe that having a nomenclature standard is important or very important and 61% had adopted all or portions of TG‐263 in their clinics. For those yet to adopt TG‐263, the staffing and implementation efforts were the main cause for delaying adoption. Fewer respondents had trouble adopting TG‐263 for organs at risk (29%) versus target (44%) nomenclature. Common themes in written feedback were lack of physician support and available resources, especially in vendor systems, to facilitate adoption.ConclusionsWhile there is strong support and belief in the benefit of standardized nomenclature, the widespread adoption of TG‐263 has been hindered by the effort needed by staff for implementation. Feedback from the survey is being utilized to drive the focus of the update efforts and create tools to facilitate easier adoption of TG‐263.

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“…To address the nested entities and ambiguity, there are efforts to standardize the nomenclature of clinical entities [53,54]. However, this is still in an initial phase, and to be successful, it needs to be widely adopted.…”

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confidence: 99%

A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

et al. 2021

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Significant growth in Electronic Health Records (EHR) over the last decade has provided an abundance of clinical text that is mostly unstructured and untapped. This huge amount of clinical text data has motivated the development of new information extraction and text mining techniques. Named Entity Recognition (NER) and Relationship Extraction (RE) are key components of information extraction tasks in the clinical domain. In this paper, we highlight the present status of clinical NER and RE techniques in detail by discussing the existing proposed NLP models for the two tasks and their performances and discuss the current challenges. Our comprehensive survey on clinical NER and RE encompass current challenges, state-of-the-art practices, and future directions in information extraction from clinical text. This is the first attempt to discuss both of these interrelated topics together in the clinical context. We identified many research articles published based on different approaches and looked at applications of these tasks. We also discuss the evaluation metrics that are used in the literature to measure the effectiveness of the two these NLP methods and future research directions.

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Automated data abstraction for quality surveillance and outcome assessment in radiation oncology

Cited by 7 publications

References 20 publications

Infrastructure tools to support an effective Radiation Oncology Learning Health System

Infrastructure tools to support an effective Radiation Oncology Learning Health System

Perceptions on and roadblocks to implementation of standardized nomenclature in radiation oncology: A survey from TG‐263U1

A Survey on Recent Named Entity Recognition and Relationship Extraction Techniques on Clinical Texts

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