D. Bailey¹, C. Paik², M. Gass², K. Thieman², M. C. Harris², E. Baumgarten², L. Stallings², S. H. Kirby², D. Kurbegov², W. Best³, R. Billups⁴, and A. S. Kennedy²; ¹Sarah Cannon/HCA Healthcare, Nashville, TN, United States, ²Sarah Cannon Cancer Institute, Nashville, TN, ³Belmont University, Nashville, TN, ⁴Sarah Cannon, Nashville, TN, United States

Purpose/Objective(s): Radiation oncology information systems (ROIS) are highly specialized and capture large amounts of data related to patient care and radiation oncology operations. We report our efforts to expand the utility of a ROIS system by simultaneously broadening and fine-tuning the use and analysis of this data. Materials/

Methods: Within one healthcare system, a majority of radiation oncology centers were consolidated into one ROIS that serve communities in urban and rural regions in 11 US states. All patient and treatment data from these centers are aggregated into a centralized data repository via a unified "gold standard" build shared by the group. This enables systematized workflows that capture the same discrete data from all sites, including diagnosis, tumor staging (AJCC and FIGO), standard toxicity definitions (CTCAE), physics and dosimetry data, delivered dose, radiation techniques, and time duration of operational and clerical tasks. A commercial analytics platform is used to abstract these data based on standardized clinical and operational parameters, facilitating the reporting of key performance and outcomes indicators on demand and in pre-defined, template reports.
Results: A total of 32 centers are part of a central ROIS platform. From January 2015 to January 2024, a total of 42,938 individual patients were entered having received radiotherapy treatment. The total number of identified toxicities (Grade 3 or higher, acute and late) are 237,873, following 990,000 treatments delivered. Operational and quality metrics in workflows numbered 68,113 from 277 unique care paths. ROIS data has been utilized to identify operational and clinical best practices and optimize opportunities. A partial list of metrics is listed in Table 1.
Conclusion: The ability to query a large dataset allowed identifying a median turnaround time to assist in program workflow improvements that can scale rapidly. Many predefined goals have been realized to date using the ROIS. Benchmarking of operational, quality, and clinical outcomes have been successful and meet expectations.

Abstract 3386 – Table 1

Report Name	Metrics Captured
Monthly Operating Report	Number of Consulted Patients and Treatments. Compared Month over Month, Year over Year, and by Diagnosis Site
Asset Utilization	Number of Treatments and Types of Treatment performed on each machine
Consult Trends and Top Referring Physicians	Referrals by Referring Physician, Diagnosis, and Geographic Footprint
Patient Throughput	Percentage of patients that start treatment in so many days after consultation
Course Intent	Number of Courses and Treatments by Intent