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PURPOSE The field of oncology is expanding rapidly. New trials are opening as an increasing number of therapeutic agents are being investigated before they can become approved therapies. Aggregate views of these data, particularly data associated with diseases, biomarkers, and drugs, can be helpful in understanding the trends in current research as well as existing gaps in cancer care. METHODS In this paper, we performed a landscape analysis for breast cancer and acute myeloid leukemia related trials with structured, curated data from clinical trials using the My Cancer Genome clinical trial knowledgebase. RESULTS We have performed detailed analytics on breast cancer (N = 1,128) and acute myeloid leukemia trial sets (N = 483) to highlight the top biomarkers, drug classes, and drugs—thereby supporting a full view of biomarkers, biomarker groups, and drugs that are currently being explored in these respective diseases. CONCLUSION Analysis and data visualization of the cancer clinical trial landscape can inform strategic planning for new trial designs and trial activation at a particular site., Deep analysis of breast cancer & AML trials using the my cancer genome model

PURPOSE Tumor next-generation sequencing reports typically generate trial recommendations for patients based on their diagnosis and genomic profile. However, these require additional refinement and prescreening, which can add to physician burden. We wanted to use human prescreening efforts to efficiently refine these trial options and also elucidate the high-value parameters that have a major impact on efficient trial matching. METHODS Clinical trial recommendations were generated based on diagnosis and biomarker criteria using an informatics platform and were further refined by manual prescreening. The refined results were then compared with the initial trial recommendations and the reasons for false-positive matches were evaluated. RESULTS Manual prescreening significantly reduced the number of false positives from the informatics generated trial recommendations, as expected. We found that trial-specific criteria, especially recruiting status for individual trial arms, were a high value parameter and led to the largest number of automated false-positive matches. CONCLUSION Reflex clinical trial matching approaches that refine trial recommendations based on the clinical details as well as trial-specific criteria have the potential to help alleviate physician burden for selecting the most appropriate trial for their patient. Investing in publicly available resources that capture the recruiting status of a trial at the cohort or arm level would, therefore, allow us to make meaningful contributions to increase the clinical trial enrollments by eliminating false positives.

In this work, we present a conceptual framework to support clinical trial optimization and enrollment workflows and review the current state, limitations, and future trends in this space. This framework includes knowledge representation of clinical trials, clinical trial optimization, clinical trial design, enrollment workflows for prospective clinical trial matching, waitlist management, and, finally, evaluation strategies for assessing improvement.

Integrating different types of data, including electronic health records, imaging data, administrative and claims databases, large data repositories, the Internet of Things, genomics, and other omics data, is both a challenge and an opportunity that must be tackled head on. We explore some of the challenges and opportunities in optimizing data integration to accelerate breast cancer discovery and improve patient outcomes. Susan G. Komen convened three meetings (2015, 2017, and 2018) with various stakeholders to discuss challenges, opportunities, and next steps to enhance the use of big data in the field of breast cancer. Meeting participants agreed that big data approaches can enhance the identification of better therapies, improve outcomes, reduce disparities, and optimize precision medicine. One challenge is that databases must be shared, linked with each other, standardized, and interoperable. Patients want to be active participants in research and their own care, and to control how their data are used. Many patients have privacy concerns and do not understand how sharing their data can help to effectively drive discovery. Public education is essential, and breast cancer researchers who are skilled in using and analyzing big data are needed. Patient advocacy groups can play multiple roles to help maximize and leverage big data to better serve patients. Komen is committed to educating patients on big data issues, encouraging data sharing by all stakeholders, assisting in training the next generation of data science breast cancer researchers, and funding research projects that will use real-life data in real time to revolutionize the way breast cancer is understood and treated.

e19328 Background: Patients with breast cancer experience significant disruption to their daily lives when undergoing treatment. When treatment workload exceeds the capacity to manage that treatment, patients are less likely to adhere to care plans and likelier to have worse outcomes. The purpose of this study was to assess treatment workload in patients with operable breast cancer in the SEER-Medicare dataset. Methods: Operable (stage I-III) breast cancer patients diagnosed from 2010-2016, > 65 years old at diagnosis, and with 18 months of continuous Medicare part A, B, and D coverage after diagnosis were included in the cohort. We calculated the number of outpatient appointment days, total cumulative inpatient lengths of stay, and distance traveled for patients in the cohort in the 18 months following diagnosis. We compared treatment workload outcomes between patients with stage I and stage III disease and between patients with HER2+ and HER2-/ER+ disease. Results: 35,071 patients met the inclusion criteria. Compared to stage I patients, stage III patients had more median outpatient appointment days (71 vs 50), more median inpatient days (2 vs 0), and greater median distance traveled (1846 vs 1332 miles). Compared to patients with HER2-/ER+ disease, patients with HER2+ disease had more median outpatient appointment days (70 vs 51), and greater median distance traveled (1775 vs 1350 miles). All comparisons were with Mann Whitney U and were statistically significant with P < .001. Conclusions: Patients with operable breast cancer experience a high amount of treatment workload in the first 18 months after diagnosis. These workload measures derived from SEER-Medicare claims differentiate significantly by stage and subtype, with stage III and HER2+ patients experiencing greater treatment workload than stage I and HER2-/ER+ patients respectively. We suspect that greater treatment workload in stage III is attributed to higher intensity of treatment for later stage cancer and associated complications, while greater treatment workload in HER2+ patients is attributed to the use of trastuzumab. [Table: see text]

e24043 Background: Oncology practices face multiple challenges when trying to implement the Commission on Cancer (CoC) Survivorship Care Plan (SCP) documentation standard. First, it is difficult to systematically identify patients who qualify for a SCP from the electronic medical record (EMR). Second, SCP documentation must be completed within a specific timeframe that does not always align with clinical encounters for routine care. Finally, as practices participate in more regulatory programs, such as the Oncology Care Model (OCM), the burden of meeting the unique but often overlapping documentation standards becomes burdensome. Our goal was to improve adherence to SCP documentation through enhanced clinical documentation workflows and clinical decision support. Methods: To meet CoC SCP standards, SCP templates were built into the EPIC EMR at a single multi-centered institution. These templates allowed the user to indicate on the problem list if the SCP was needed. Likewise, Institute of Medicine (IOM) care plan documentation templates and reminders were built to meet OCM standards. Population dashboards were developed in Tableau to track SCP documentation adherence and provide support regarding eligible patients who required SCP documentation. Patients were eligible who had a cancer diagnosis codes on the problem list, and MC in the EMR. Patients greater than 12 months from being marked curative were considered eligible to complete SCP. Results: Between 2015-2019, 2763 cancer patients were MC by 31 providers, where 806 (30%) were OCM patients. Overall, 1372 (70%) non-OCM patients required an SCP, where 563 (41%) were completed. When examining the OCM patients, 689 (85%) required a SCP, where 552 (80%) were completed. For patients MC in 2018, 163/439 (37%) and 137/190 (72%) of patients received a SCP for the non-OCM and OCM group respectively. For patient MC in 2019 after implementation of the decision support dashboard, 115/145 (79%) and 144/148 (97%) of eligible patients had completed SCP for the non-OCM and OCM group respectively (Table). Conclusions: During the study period, adherence to SCP documentation doubled concordant with CoC standards at that time. Integration of IOM and SCP documentation improved adherence among OMC compared to non-OCM patients. The decision support dashboard helped prioritize cases for SCP documentation for care teams. This is an example of a decision support framework that could be used for other standards driven documentation requirements in cancer.

10520 Background: Female underrepresentation in academic medicine leadership is well-documented; however, oncology specific data are scarce. This study evaluates female leadership representation in academic medical oncology (MO), radiation oncology (RO) and surgical oncology (SO) programs. Furthermore, we examine the impact of female leadership on overall female faculty representation. Methods: A total of 264 (96%) Accreditation Council for Graduate Medical Education actively accredited MO [144 of 153], RO [93 of 94] and SO [27 of 27] training programs were included. The gender of overall faculty and those in leadership positions (program director and departmental chair/division chief) of each program was determined using hospital websites from 10/01/18 to 01/27/19. The chi-squared goodness-of-fit test was used to examine whether the observed proportion of females in leadership positions deviates significantly from the expected proportion based on the actual proportion of overall female faculty in MO, RO and SO. Two-sample t-tests were used to compare rates of female faculty representation across each program based on the presence/absence of female in a leadership position for MO, RO and SO. Results: Female faculty representation in MO, RO and SO was 37.1% (1,554/4,191), 30.7% (389/1,269) and 38.8% (212/546), respectively. Female representation in leadership positions was 31.5% (82/260), 17.4% (31/178) and 11.1% (5/45), respectively. The observed proportion of females in leadership positions was significantly lower than the expected proportion of females in leadership positions for RO (17.4% vs. 30.7%, p = .0001) and SO (11.1% vs. 38.8%, p = .0001), and demonstrated a trend towards significance for MO (31.5% vs. 37.1%, p = .063). 47.9%, 33% and 18.5% of MO, RO and SO programs had ≥1 female in a leadership position, respectively. Programs that had a female in a leadership position had a higher mean percentage of overall female faculty than those that did not: 41.0% vs 35.0% (p = .0006), 36.0% vs 26.0% (p = .0002) and 39.0% vs 32.0% (p = .348) for MO, RO and SO, respectively. Conclusions: Gender disparity exists in academic MO, RO and SO faculty and is magnified at the leadership level. Programs with a female physician in a leadership position are associated with a higher percentage of female faculty.