Cancer is the second leading cause of death in the United States. Radiation therapy (RT), together with surgery and chemotherapy, is one of the most important modalities for cancer treatments. Patient safety is a paramount issue with radiation therapy. The safety concerns in radiation therapy have been recognized by many different national and international organizations including IAEA (International Atomic Energy Agency), ICRP (International Commission on Radiological Protection), NRC (Nuclear Regulatory Commission), WHO (World Health Organization), ASTRO (American Society for Radiation Oncology) and AAPM (American Association of Physicists in Medicine) in the past few decades. RT error was ranked as the #1 health technology hazard by ECRI (Emergency Care Research Institute). The field of radiation therapy as a whole has been attempting to improve patient safety over the past decades through the adoption of rigorous clinical workflows following the lessons learned from the previous incidents and following the guidelines published by the aforementioned organizations. However, as technologies become increasingly reliant on digital communication and workflows, the manual-auditing based safety measures continue to exhibit their limitations and antiquatedness. In this project, we propose to develop an automated health information technology (HIT) system to improve patient safety, treatment quality and working efficiency for radiation therapy. This system will continuously monitor errors and traces of inconsistencies in the patient data and documents in the clinical patient treatment systems, to make users aware of any patient safety issues. It is expected that the treatment quality will be improved by automatic performance of the standard-of-care quality checks and minimization of human errors in the clinical workflow. It is also expected that the working efficiency will be significantly improved due to the automatic safety and quality checks which when performed manually often account for approximately 50% of human worker workload. Over the past three years, we have been building such a HIT system and have demonstrated the clinical safety and quality significance and potential efficiency gains in our preliminary results. In this new project we plan to fundamentally overhaul our current efforts by making the computer-based safety and quality checks more accurate, responsive, user-friendly, powerful and fully automated. We also plan to integrate human factors into the design features, which were identified from the preliminary data, thereby maximizing the impacts of the system on safety and quality.

Public Health Relevance

In this research project, a health information system will be developed to automatically verify patient data and documents in radiation therapy departments. The goals are to significantly reduce human errors, improve patient safety, treatment quality and work efficiency, and to reduce the overall cost of care for cancer patients receiving radiation therapy.

Agency
National Institute of Health (NIH)
Institute
Agency for Healthcare Research and Quality (AHRQ)
Type
Research Project (R01)
Project #
5R01HS022888-03
Application #
9144343
Study Section
Healthcare Patient Safety and Quality Improvement Research (HSQR)
Program Officer
Gray, Darryl T
Project Start
2014-09-30
Project End
2019-09-29
Budget Start
2016-09-30
Budget End
2017-09-29
Support Year
3
Fiscal Year
2016
Total Cost
Indirect Cost
Name
Washington University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
Cai, Bin; Green, Olga L; Kashani, Rojano et al. (2018) A practical implementation of physics quality assurance for photon adaptive radiotherapy. Z Med Phys 28:211-223
Fu, Yabo; Liu, Shi; Li, H Harold et al. (2018) An adaptive motion regularization technique to support sliding motion in deformable image registration. Med Phys 45:735-747
Rudra, Soumon; Hui, Caressa; Rao, Yuan J et al. (2018) Effect of Radiation Treatment Volume Reduction on Lymphopenia in Patients Receiving Chemoradiotherapy for Glioblastoma. Int J Radiat Oncol Biol Phys 101:217-225
Sun, Baozhou; Lam, Dao; Yang, Deshan et al. (2018) A machine learning approach to the accurate prediction of monitor units for a compact proton machine. Med Phys 45:2243-2251
Wang, Yuhe; Mazur, Thomas R; Park, Justin C et al. (2017) Development of a fast Monte Carlo dose calculation system for online adaptive radiation therapy quality assurance. Phys Med Biol 62:4970-4990
Yang, Deshan; Zhang, Miao; Chang, Xiao et al. (2017) A method to detect landmark pairs accurately between intra-patient volumetric medical images. Med Phys 44:5859-5872
Qiu, Jianfeng; Harold Li, H; Zhang, Tiezhi et al. (2017) Automatic x-ray image contrast enhancement based on parameter auto-optimization. J Appl Clin Med Phys 18:218-223
Maitree, Rapeepan; Perez-Carrillo, Gloria J Guzman; Shimony, Joshua S et al. (2017) Adaptive anatomical preservation optimal denoising for radiation therapy daily MRI. J Med Imaging (Bellingham) 4:034004
Cai, Bin; Li, Harold; Yang, Deshan et al. (2017) Performance of a multi leaf collimator system for MR-guided radiation therapy. Med Phys 44:6504-6514
Liu, Shi; Mazur, Thomas R; Li, Harold et al. (2017) A method to reconstruct and apply 3D primary fluence for treatment delivery verification. J Appl Clin Med Phys 18:128-138

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