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Automated data abstraction for quality surveillance and outcome assessment in radiation oncology
Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and st...
Autores principales: | , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292697/ https://www.ncbi.nlm.nih.gov/pubmed/34101349 http://dx.doi.org/10.1002/acm2.13308 |
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author | Kapoor, Rishabh Sleeman, William C. Nalluri, Joseph J. Turner, Paul Bose, Priyankar Cherevko, Andrii Srinivasan, Sriram Syed, Khajamoinuddin Ghosh, Preetam Hagan, Michael Palta, Jatinder R. |
author_facet | Kapoor, Rishabh Sleeman, William C. Nalluri, Joseph J. Turner, Paul Bose, Priyankar Cherevko, Andrii Srinivasan, Sriram Syed, Khajamoinuddin Ghosh, Preetam Hagan, Michael Palta, Jatinder R. |
author_sort | Kapoor, Rishabh |
collection | PubMed |
description | Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider. |
format | Online Article Text |
id | pubmed-8292697 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-82926972021-07-22 Automated data abstraction for quality surveillance and outcome assessment in radiation oncology Kapoor, Rishabh Sleeman, William C. Nalluri, Joseph J. Turner, Paul Bose, Priyankar Cherevko, Andrii Srinivasan, Sriram Syed, Khajamoinuddin Ghosh, Preetam Hagan, Michael Palta, Jatinder R. J Appl Clin Med Phys Radiation Oncology Physics Rigorous radiotherapy quality surveillance and comprehensive outcome assessment require electronic capture and automatic abstraction of clinical, radiation treatment planning, and delivery data. We present the design and implementation framework of an integrated data abstraction, aggregation, and storage, curation, and analytics software: the Health Information Gateway and Exchange (HINGE), which collates data for cancer patients receiving radiotherapy. The HINGE software abstracts structured DICOM‐RT data from the treatment planning system (TPS), treatment data from the treatment management system (TMS), and clinical data from the electronic health records (EHRs). HINGE software has disease site‐specific “Smart” templates that facilitate the entry of relevant clinical information by physicians and clinical staff in a discrete manner as part of the routine clinical documentation. Radiotherapy data abstracted from these disparate sources and the smart templates are processed for quality and outcome assessment. The predictive data analyses are done on using well‐defined clinical and dosimetry quality measures defined by disease site experts in radiation oncology. HINGE application software connects seamlessly to the local IT/medical infrastructure via interfaces and cloud services and performs data extraction and aggregation functions without human intervention. It provides tools to assess variations in radiation oncology practices and outcomes and determines gaps in radiotherapy quality delivered by each provider. John Wiley and Sons Inc. 2021-06-08 /pmc/articles/PMC8292697/ /pubmed/34101349 http://dx.doi.org/10.1002/acm2.13308 Text en © 2021 The Authors. Journal of Applied Clinical Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine. https://creativecommons.org/licenses/by/4.0/This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | Radiation Oncology Physics Kapoor, Rishabh Sleeman, William C. Nalluri, Joseph J. Turner, Paul Bose, Priyankar Cherevko, Andrii Srinivasan, Sriram Syed, Khajamoinuddin Ghosh, Preetam Hagan, Michael Palta, Jatinder R. Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title_full | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title_fullStr | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title_full_unstemmed | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title_short | Automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
title_sort | automated data abstraction for quality surveillance and outcome assessment in radiation oncology |
topic | Radiation Oncology Physics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8292697/ https://www.ncbi.nlm.nih.gov/pubmed/34101349 http://dx.doi.org/10.1002/acm2.13308 |
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