Literature DB >> 28196607

Development of a model web-based system to support a statewide quality consortium in radiation oncology.

Jean M Moran1, Mary Feng2, Lisa A Benedetti3, Robin Marsh2, Kent A Griffith4, Martha M Matuszak2, Michael Hess5, Matthew McMullen6, Jennifer H Fisher7, Teamour Nurushev8, Margaret Grubb2, Stephen Gardner9, Daniel Nielsen2, Reshma Jagsi2, James A Hayman2, Lori J Pierce2.   

Abstract

PURPOSE: A database in which patient data are compiled allows analytic opportunities for continuous improvements in treatment quality and comparative effectiveness research. We describe the development of a novel, web-based system that supports the collection of complex radiation treatment planning information from centers that use diverse techniques, software, and hardware for radiation oncology care in a statewide quality collaborative, the Michigan Radiation Oncology Quality Consortium (MROQC). METHODS AND MATERIALS: The MROQC database seeks to enable assessment of physician- and patient-reported outcomes and quality improvement as a function of treatment planning and delivery techniques for breast and lung cancer patients. We created tools to collect anonymized data based on all plans.
RESULTS: The MROQC system representing 24 institutions has been successfully deployed in the state of Michigan. Since 2012, dose-volume histogram and Digital Imaging and Communications in Medicine-radiation therapy plan data and information on simulation, planning, and delivery techniques have been collected. Audits indicated >90% accurate data submission and spurred refinements to data collection methodology.
CONCLUSIONS: This model web-based system captures detailed, high-quality radiation therapy dosimetry data along with patient- and physician-reported outcomes and clinical data for a radiation therapy collaborative quality initiative. The collaborative nature of the project has been integral to its success. Our methodology can be applied to setting up analogous consortiums and databases.
Copyright © 2016 American Society for Radiation Oncology. Published by Elsevier Inc. All rights reserved.

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Year:  2016        PMID: 28196607     DOI: 10.1016/j.prro.2016.10.002

Source DB:  PubMed          Journal:  Pract Radiat Oncol        ISSN: 1879-8500


  4 in total

1.  Dosimetric predictors for acute esophagitis during radiation therapy for lung cancer: Results of a large statewide observational study.

Authors:  Peter Paximadis; Matthew Schipper; Martha Matuszak; Mary Feng; Shruti Jolly; Thomas Boike; Inga Grills; Larry Kestin; Benjamin Movsas; Kent Griffith; Gregory Gustafson; Jean Moran; Teamour Nurushev; Jeffrey Radawski; Lori Pierce; James Hayman
Journal:  Pract Radiat Oncol       Date:  2017-07-19

2.  Identifying Patients Whose Symptoms Are Underrecognized During Treatment With Breast Radiotherapy.

Authors:  Reshma Jagsi; Kent A Griffith; Frank Vicini; Thomas Boike; Michael Dominello; Gregory Gustafson; James A Hayman; Jean M Moran; Jeffrey D Radawski; Eleanor Walker; Lori Pierce
Journal:  JAMA Oncol       Date:  2022-06-01       Impact factor: 33.006

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

Authors:  Rishabh Kapoor; William C Sleeman; Joseph J Nalluri; Paul Turner; Priyankar Bose; Andrii Cherevko; Sriram Srinivasan; Khajamoinuddin Syed; Preetam Ghosh; Michael Hagan; Jatinder R Palta
Journal:  J Appl Clin Med Phys       Date:  2021-06-08       Impact factor: 2.102

4.  Machine Learning in Radiation Oncology: Opportunities, Requirements, and Needs.

Authors:  Mary Feng; Gilmer Valdes; Nayha Dixit; Timothy D Solberg
Journal:  Front Oncol       Date:  2018-04-17       Impact factor: 6.244

  4 in total

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