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Quantifying the Risk of Technology-Driven Health Disparities in Radiation Oncology.
Moncion, Alexander; Bryant, Alex K; Cardenas, Carlos E; Dess, Kathryn J; Ditman, Maria N; Mayo, Charles S; Mierzwa, Michelle L; Paradis, Kelly C; Stanley, Dennis N; Covington, Elizabeth L.
Affiliation
  • Moncion A; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
  • Bryant AK; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, VA Ann Arbor Health System, Ann Arbor, Michigan.
  • Cardenas CE; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Albama.
  • Dess KJ; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, VA Ann Arbor Health System, Ann Arbor, Michigan.
  • Ditman MN; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan; Department of Radiation Oncology, VA Ann Arbor Health System, Ann Arbor, Michigan.
  • Mayo CS; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
  • Mierzwa ML; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
  • Paradis KC; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan.
  • Stanley DN; Department of Radiation Oncology, The University of Alabama at Birmingham, Birmingham, Albama.
  • Covington EL; Department of Radiation Oncology, University of Michigan, Ann Arbor, Michigan. Electronic address: ecoving@med.umich.edu.
Pract Radiat Oncol ; 2024 Jul 09.
Article in En | MEDLINE | ID: mdl-38992491
ABSTRACT

PURPOSE:

New technologies are continuously emerging in radiation oncology. Inherent technological limitations can result in health care disparities in vulnerable patient populations. These limitations must be considered for existing and new technologies in the clinic to provide equitable care. MATERIALS AND

METHODS:

We created a health disparity risk assessment metric inspired by failure mode and effects analysis. We provide sample patient populations and their potential associated disparities, guidelines for clinics and vendors, and example applications of the methodology.

RESULTS:

A disparity risk priority number can be calculated from the product of 3 quantifiable metrics the percentage of patients impacted, the severity of the impact of dosimetric uncertainty or quality of the radiation plan, and the clinical dependence on the evaluated technology. The disparity risk priority number can be used to rank the risk of suboptimal care due to technical limitations when comparing technologies and to plan interventions when technology is shown to have inequitable performance in the patient population of a clinic.

CONCLUSIONS:

The proposed methodology may simplify the evaluation of how new technology impacts vulnerable populations, help clinics quantify the limitations of their technological resources, and plan appropriate interventions to improve equity in radiation treatments.

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Pract Radiat Oncol Year: 2024 Document type: Article

Full text: 1 Collection: 01-internacional Database: MEDLINE Language: En Journal: Pract Radiat Oncol Year: 2024 Document type: Article