Radiation therapy needs big data

Healthcare is still in its infancy when it comes to being able to access metrics to monitor quality in terms of patient outcomes and satisfaction, as well as business policies.

To address this lack of data in one sector, the Radiation Oncology Institute (ROI) has announced it will offer researchers a chance to apply for competitive grants totaling $200,000 over two years for projects that leverage advances in big data analytics in radiation oncology.

“Big data approaches may advance quality improvement in our field by helping identify links between factors such as patient or tumor characteristics and outcomes, ranging from positive treatment responses to dosimetric uncertainties or outright errors,” said ROI President Deborah A. Kuban, MD, FASTRO. “These grants are designed to support innovative research with the potential for real-world impact on patient care.”

Eligible projects will focus on improving quality in radiation therapy (RT) by impacting any aspect of the radiation oncology process that could benefit from a big data approach, with preference for proposals that address the highest impact research questions for the field, inform policy development or enhance outcomes in cancer treatment.

Special consideration will be given to proposals with an emphasis on identifying and analyzing data sources from the multiple sectors that provide care for patients receiving RT, with the goal of developing measures of quality assurance and quality improvement that span a variety of care providers. To this end, applicant teams that include a combination of physicists, dosimetrists, doctors, nurses, allied medical personnel and/or other employees are highly encouraged.

Sample projects illustrated include big data analyses of the measures most important to cancer patients receiving radiation therapy (RT), such as toxicities and patient satisfaction, and large-scale examinations of patient records to determine, for example, potential benefactors of more active symptom management approaches or increased image guidance. Projects must draw on existing datasets, such as CMS Medicare databases, rather than use the funding to create or develop new registries or datasets.

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