Vetter, Christopher D.

PURPOSE Incorporation of structured data elements within the electronic health record generates real-time data for many potential uses. However, this requires a change in documentation practices, which may increase documentation effort. The best strategies to support structured documentation interventions have not been thoroughly investigated. Herein, we report the results of extending a data capture tool across multiple disease sites and practice locations. METHODS A note-based data capture tool was developed to collect five data elements using minimum common oncology data elements language. Soft-stop close visit validation (CVV), offloading of data entry tasks to allied health staff, group education, individualized e-mails, and one-on-one assistance were used to support utilization. Baseline and follow-up surveys were collected. For each provider, we compared data capture in the 6 months before and after their CVV go-live. RESULTS Forty-five providers participated (31 physicians, 14 nurse pracationers and physician assistants [NPPAs]), resulting in 23,274 encounters pre-CVV go-live and 23,146 post-CVV go-live visits. Median percentage of all data elements captured increased from 69.5% to 82.6% across all providers. The effect of CVV was immediate and sustained. When surveyed, most respondents agreed with the value propositions of understanding practice population, outcomes, selecting trials to open, and identifying patients for trial enrollment. Most respondents (15/20, 75%) felt the soft stop was a helpful reminder. CONCLUSION Structured data can be successfully collected in provider notes with >80% data capture. Personalized assistance incorporating the structured data elements into users' preexisting templates and soft-stop CVV, along with monitoring the results of implementation and personalized follow-up, was used to support the structured data capture intervention.

e13661 Background: The process of data abstraction and curation represents a significant proportion of clinical trial costs, requiring up to $30,000 per patient depending on the data complexity. Research variables such as stage, performance status (PS), and cancer disease status are typically present in the electronic health record (EHR) in free text but require abstraction for research use. Structuring these real-world data (RWD) in an exportable format utilizing minimum common oncology data elements (mCODE) could reduce the need for abstraction and the potential for erroneous transcription that has been shown to be 5.51-7.72% in a meta-analysis. We assessed the feasibility, availability, and accuracy of RWD for use across Alliance for Clinical Trials in Oncology sites by comparing RWD to matched electronic case report form data (CRF data) at 2 sites where EHR-integrated tools collect structured RWD for all solid tumor patients. Methods: Stage, PS, and cancer disease status (CDS) were extracted from the EHR as RWD and from eCRFs for 100 patients enrolled in National Clinical Trials Network trials across multiple disease sites from 2022 through 2025. Data from baseline and day 1 of cycles 1-4 were compared (500 encounter timepoints). Results: Stage, PS, and CDS were available from RWD more often than CRFs (85 vs 70 / 100 patients, 436 vs 416 / 500 encounters, and 345 vs 83 / 500 encounters respectively). When stage was present in both (n = 58), there were 5 mismatches (8.6%). PS had 22 mismatches (4%) and CDS had 45 mismatches (11.4%). After correcting for differences in CRF vs RWD definitions, CDS mismatches dropped to 1.2% (n = 4). Discordance between RWD and CRF data was related to differences in the timepoint of data collection (e.g., systemic staging scans completed after the initial consult), CRF data obtained from other EHR notes (e.g., oncology research nurse notes containing PS), inherent discordance between RWD and CRF data choices (e.g., responding vs complete or partial response), and failure to update EHR data capture tool fields. RWD were available 21 days (median) before CRF data. Conclusions: Our results suggest that RWD captured directly within clinical notes match CRF data, are more often present, and are available sooner. RWD could serve as a data source for trials, decreasing the need for manual curation and cost. Although some discordance exists, overall rates are low and close to the published error rate for abstraction.

e13660 Background: We previously showed success with collection of structured data elements within the electronic health record using the minimum common oncology data elements (henceforth, mCODE tool) supported by soft-stop close visit validation (CVV) and staff support interventions in a limited cohort. Since the additional interventions are resource-intensive and difficult to apply across an entire department, we therefore implemented a department-wide roll-out supported by the least resource-intensive interventions while monitoring uptake. Methods: The mCODE tool collects five data elements: stage, disease status, performance status, intent of treatment, and intent to change therapy. The Department of Oncology providers received education on the mCODE tool and were asked to implement use at a faculty meeting; soft-stop CVV was enabled at that time. The percentage of data captured for each data element was collected from the notes of providers who did not participate in prior pilots for the 4 months post-implementation. mCODE tool usage was defined as entry of at least one data element except stage (as stage has long been a structured data element in Epic, can be entered independently of the mCODE tool, and many providers were entering stage prior to mCODE tool introduction). Results: 86 MDs and 20 APPs for a total of 106 providers were included, resulting in 14,973 visits and 8,745 unique patients. 73.6% of providers (n =78) utilized the mCODE tool. Of those who utilized the tool, median overall data capture across the four data elements was 24.0%. 22.6% of providers (n =24) achieved > 80% overall data capture and 32.1% (n =34) had over 50% overall data capture despite minimal intervention. 75.6% of MDs (n = 65) utilized the tool with a median data capture of 16.3% compared to 65% of APPs (n = 13) with a median data capture of 90.3%. Rates of data capture for individual data elements were similar. Conclusions: Use of soft-stop CVV appears to drive utilization of the mCODE tool but is insufficient to promote > 80% data capture for most providers without more intervention. Supportive interventions such as individualized assistance with incorporation into existing templates and directed communication from leadership remain necessary to promote use of structured data elements and will be implemented in sequence to understand how we achieve the target data capture ( > 80%) at scale.