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267 Background: Clinical information management is a burdensome process for oncology providers owing to the complexity of modern cancer data. The results that a clinician needs to review are often spread across several areas within the EHR, making it cumbersome to sense a broad overview of patient status. The purpose of this study is to describe a framework for oncologist-driven development of a data visualization tool to trend cancer biomarkers and assess the feasibility of this tool to query and display EHR data relevant for the treatment of prostate cancer. Methods: A clinical sponsor is selected to identify data elements necessary to make treatment decisions for patients receiving therapy for prostate cancer and to provide rapid feedback for the clinical tool interface. The commercial EHR database is queried to determine identifying codes for relevant laboratory tests, medications, and procedures. Data elements are assembled using the EHR platform for clinical synopsis and the clinical tool is made available to five genitourinary medical oncologists for initial pilot. Results: Oral prostate cancer medications were queried based on medication therapeutic and pharmaceutical class. In addition, androgen deprivation therapy (ADT) injections were separately identified based on route of administration. Prostate specific antigen (PSA) and testosterone result values were queried using laboratory base and/or common name codes. However, many duplicate entities are found varying by hospital/laboratory site and test assay. The assembled clinical data visualization tool can overlay temporal trends in PSA and testosterone over medication start/stop dates to convey treatment response and signs of early medication resistance. The clinician also has the option to overlay vital signs or other laboratory information to visualize treatment related adverse events (ex. weight gain related to ADT, anemia related to PARP inhibitor therapy, etc.) Lastly, the tool can also highlight the dates of the patient’s last imaging tests to allow clinicians to determine if the patient is due for any follow up imaging. Conclusions: The EHR can support novel data visualization tools for cancer biomarkers that can reasonably support clinical workflows. Development requires an intimate knowledge of EHR data but may still be limited by duplicate or erroneous codes for laboratory results. This issue may be addressed by using standard nomenclature for laboratory results such as LOINC codes but is not currently supported by the commercial EHR. Future work in this area will include formal usability testing from the perspective of oncology providers and patients.

20 Background: The cost of cancer care is an enormous healthcare burden. Most inpatient chemotherapy is not reimbursed because of diagnosis-related group codes. We have previously reported inpatient chemotherapy and immunotherapy (IC) is associated with poorer outcomes for patients with advanced stage solid tumor (ST) vs hematologic malignancy (HM) patients. 1 We piloted the use of a novel objective scoring rubric to guide and automate IC stewardship at an academic cancer center to decrease the inappropriate use of inpatient administration of costly therapies in patients especially at the end of life. Methods: Using an iterative process, an interdisciplinary group of physicians, nurses and pharmacists developed objective criteria of patient, cancer and treatment factors to guide chemotherapy stewardship. IC that is on formulary and being given as standard of care (i.e., induction of leukemia) are automatically approved. IC that is non-formulary requires evaluation using the developed criteria. Treatment factors include information on the level of existing evidence to support use: type and phase of trial, FDA and NCCN approvals. Patient factors include: performance status, line and goal of therapy. The scoring rubric positively weights regimens with strong levels of evidence or positive patient factors and negatively weights regimens with poor levels of evidence and adverse patient factors. Clinicians must complete the criteria via a form in RedCap. Upon completion, a score is automatically calculated by the tool and 2 disease specific physicians and a clinical pharmacist review for accuracy. If the threshold score is met, IC is approved for inpatient administration and if it is not met, IC is not approved for administration. Results: From January 2022 until May 2022 there have been 30 cases reviewed. 50% were ST requests and 50% were for HM requests. 20 cases (67%) were approved and 8 cases (26%) were not. Two cases were retracted by the requestor. This resulted in cost savings of $63,920. Table illustrates clinical outcomes and characteristics of the approved cases. Conclusions: This pilot illustrates that 67% of the time our cancer physicians chose the administration of inpatient chemotherapy that aligned with objective criteria which is reassuring and serves to validate the use of this tool. Alternatively, this objective rubric prevented inappropriate administration of chemotherapy 26% of the time. Our pilot indicates that there is a role for an objective tool for automated inpatient chemotherapy stewardship. Reference: Evaluation of inpatient chemotherapy among patients with cancer. Petrone G et al. JCO.2022.40.16_suppl.6566.[Table: see text]

428 Background: Prior authorizations (PAs) for chemotherapy are increasingly becoming a barrier to timely and quality care delivery for cancer patients. There is an extraordinary administrative burden placed on clinical teams to participate in peer to peers (P2P) and file appeals when authorization specialists are unable to efficiently find the necessary clinical data in the EHR. 1 This leads to delays in care, staff burnout driven by decreased job satisfaction, loss of revenue from decreased clinician productivity and from chemotherapy given emergently but not retroactively reimbursed. 2 In partnership with the cancer registry team and Epic, we propose a workflow that shifts the clerical work required for PAs from the clinical teams to the certified tumor registrars (CTRs) using smart form optimization so the finance team has all the required information in one place to successfully and efficiently process the PAs. Methods: A list of common data elements including performance status, cancer biomarkers, line of treatment, goals of treatment and stage were identified. We are leveraging the staging smart form in EPIC to auto-populate these elements when available and then use CTRs to enter cancer stage, fill in any gaps and validate the data. Once approved by the CTR, the chemotherapy plan will be available in the authorization work queue for efficient processing. In 2020, we had 1,389 chemotherapy plans entered and about 38% of these required clinical intervention for financial clearance. We will track the number of P2Ps being conducted post-implementation and compare this to numbers prior to implementation. We will use descriptive statistics and t-test to compare these values. We are anticipating a 25% decrease in P2Ps. We will survey oncology physicians in June 2022 to measure baseline perceived physician impact of PAs and burnout and then again in January 2023 to measure the impact post-implementation. Results: A wellness survey was administered to Medical Oncology faculty within the Mount Sinai Health System pre-pandemic in 2020 and in 2021 and among respondents, approximately 1/3 have moderate to severe burnout risk pre- and post-pandemic. Coordination of care and PAs were rated as the largest barriers to wellbeing, and this increased from 43% pre- to 71% post-pandemic indicating a significant area of opportunity to improve wellbeing among medical oncology faculty. Conclusions: By leveraging technology and non-clinical staff workflows to improve the processing of insurance PAs of ambulatory intravenous chemotherapy, we aim to improve timeliness of quality care delivery to patients and clinical staff wellness. West CP et.al. Physician burnout: contributors, consequences and solutions. J Intern Med. Jun 2018;283(6):516-529. Lin NU et al. Increasing Burden of Prior Authorizations in the Delivery of Oncology Care in the United States. J Oncol Pract. Sep 2018;14(9):525-528.

325 Background: The Mount Sinai Health System oncology service line initiated a patient safety solution (PSS) committee to comprehensively review reported safety events in order to provide safe and high-quality care. This multi- and interdisciplinary committee creates a safe space to report events that caused harm, near misses and empowers all staff to be part of a just culture of safety. Here we describe the process and report on outcomes. Methods: The committee consists of nurses, advanced practice providers, physicians, pharmacists, social workers, IT analysts, and administrative staff, representing the oncology service line across inpatient and outpatient. Any staff member can enter incident reports that they believe warrant review into a software application. Our quality team reviews all events and then selects cases in which harm or near-harm was involved, for in-depth investigation to be presented at bi-weekly PSS meetings where cases are discussed, root causes are evaluated, and solutions are proposed. For each case, the committee votes on standard of care using the Continuous Quality Improvement (CQI) Classifications tool. Often, smaller workgroups are formed to carry out quality improvement (QI) projects and corrective action plans (CAPs). All case data, including patient demographics, case summaries, outcomes, and ongoing plans are tracked using a custom REDCap survey. A question about the role of bias was added in December 2020 (yes/no/not enough information). Results: From January 2021 until March 2022, a total of 115 events were reviewed with an average of 23 events reviewed each quarter. Sociodemographic information on patients reviewed is in table. 41% of cases involved the inpatient setting. Issues with medication comprised 47% of cases, followed by patient identification (11%) and lack of escalation (10%). The most common CAPs involved education and counselling (47%), development of new policies (22%) and escalation to leadership (8%). In total, we have developed 29 new workflows, policies, and guidelines. Bias was felt to be involved 25% of the time, no bias 5% and not enough information 43% of the time. Conclusions: This process illustrates the importance of a multi- and interdisciplinary and transparent approach to clinical case and peer review to ensure the highest level of care. The outcomes of this committee have led us to optimize current policies, create new policies and procedures, new rounding structures and place a renewed focus on bias and discrimination.[Table: see text]

433 Background: We previously reported the implementation of a machine learning (ML) model for mortality prediction that was integrated into a CDSS encouraging clinicians to have a SIC with at-risk cancer patients. The clinical utility of a ML model can change after implementation due to fluctuations in the organization’s patient population and clinical practices. It is important to establish a workflow to monitor and continually reinforce ML-powered CDSS to ensure that it continues to benefit patients. We report a workgroup structure that incorporates data driven evaluation of ML model performance and feedback from CDSS end users to optimize the acceptability of the CDSS. Methods: The workflow was piloted in the gastrointestinal (GI) oncology clinic from 11/2021-5/2022. A workgroup including members of the implementation team and end-users of the CDSS met monthly to review 1) a dashboard that displays model performance, 2) an electronic health record (EHR) report that summarizes use of the CDSS, 3) feedback from end users regarding their opinion of the CDSS and any barriers to implementation. We evaluated the accuracy of model predictions among subgroups as defined by mortality and unplanned hospital admissions or ED visit rates. Fisher’s Exact Test was used to identify differences between categorical variables. Numeric values including incidence rate ratios (IRRs) adjusted for age, sex, race, and gender with 95% confidence intervals (CIs) were calculated using Poisson regression. Results: 119 patients were evaluated by the model and 50 (42%) were assessed as high-risk. In the high-risk group, the oncology team evaluated 39 (78%) patients for appropriateness of a SIC; SIC was completed with 5 (10%) patients. During workgroup meetings, physicians shared that some of the high-risk predictions were for patients undergoing curative intent therapy. 0 out of 24 patients who received curative treatment died and 5 out of 26 patients who receive palliative treatment died. The log-rank p-value of 0.03 indicates that the survival distribution differs significantly over time between two groups. The adjusted IRR for unplanned hospital visits (palliative vs curative) was 2.55 (1.3-5.0). Adjusted mean hospital visits per month were 0.34 (0.21-0.51) vs 0.13 (0.06-0.21). Conclusions: The workgroup format is a feasible method to continuously review acceptability of a ML-powered CDSS. It may evaluate critical feedback from end users in a holistic manner that can augment a data driven evaluation of the model performance. The data implies that patients undergoing curative therapy have a decreased risk for mortality and unplanned hospital admissions or ED visits. The CDSS may be optimized by excluding these patients; however, longer follow up of this sub-population is needed to confirm that they have no additional risk factors.

e24074 Background: Early integration of palliative care (PC) into advanced cancer care has been shown to improve quality of life and prognostic understanding. However, there is a paucity of data on utilization of inpatient PC consultation and survival outcomes in patients (pts) receiving inpatient chemotherapy (IC). Methods: A retrospective review was performed at a single academic center of pts receiving IC between Jan 2016 and Dec 2017. We evaluated utilization of PC services, reasons for consult, code status, disposition, and 60-day mortality. Descriptive statistics and odds ratios (OR) were estimated from logistic regression models with mixed-effect, taking into account correlations from multiple admissions per patient. Cumulative incidence plot and Cox proportional hazard regression models were used to assess the association between mortality and study covariates. Results: Of 880 admissions, 733 (83%) were hematologic malignancies (HM) and 147 (17%) were solid tumors (ST). PC consults were more likely in ST than HM (OR 3.19, 95% CI 1.85 - 5.50) and for KPS ≤50% (OR 22.20, 95% CI 11.51- 42.79). Of 159 PC consults, 91 (57%) were for pain and 25 (16%) for goals of care. 66 pts (10%) who received IC died within 60 days of admission, 44 (67%) HM and 22 (33%) ST (p = 0.002). In pts who died within 60 days, 63% had PC consult. Median time from admission to PC consult was 2 days for ST and 9 for HM. Of those with PC consult, 40% had a change from full code to DNR/DNI and were more likely to have a health care proxy (HCP) assigned (OR 7.31, p = 0.001). PC consults were also associated with significantly higher odds of discharge to hospice (OR 10.52, 95% CI: 4.3-25.6; p = < 0.0001; Table). Mortality risk was higher in those admitted for symptoms/complications related to their disease or with progression (HR 3.24, 95% CI (2.50-4.19), p < 0.001) and in those with advanced stage disease: Stage 3 (p = 0.033); Stage 4 (p = 0.0003). Of the pts who died within 60 days, 33 (50%) died during the admission and 24 (36%) in hospice. Conclusions: Significant 60-day mortality after receiving IC is consistent with aggressive end-of-life care. Pts with ST and those with poor performance status more frequently utilized inpatient PC services; however, there is opportunity to increase utilization amongst pts with HM and introduce PC earlier in the inpatient clinical course. PC consultations improve advanced care planning with appropriate transitions in code status, HCP assignments, and discharge to hospice.[Table: see text]

6566 Background: Administration of inpatient chemotherapy (IC) is associated with more aggressive end of life care, reduced use of palliative care (PC) and decreased quality of life (QOL). This study aims to identify risk factors associated with overutilization of IC. Methods: We conducted a retrospective chart review of all admissions where IC was administered at an academic center between January 2016 and December 2017. Patients (pts) were stratified by solid tumors (ST) versus hematologic malignancies (HM) and urgency for IC was assessed. We evaluated other variables which can impact patient care such as length of stay (LOS), reason for admission and for IC. Descriptive statistics and odds ratios (OR) were estimated from logistic regression models with mixed-effect taking into account correlations from multiple admissions per patient. All tests were two-sided and statistical significance was considered when p65 years (p=0.004) and pts with KPS ≤50% (p7 days) was significantly longer in HM admissions (p=0.0001), among pts with stage 4 cancer (p=0.014) and KPS ≤50% (p=0.0001). ST (p=0.006) and pts with KPS ≤50% (p=0.0001) received IC for a non-urgent indication significantly more often than HM. In 20% of ST admissions, pts received IC because the admission coincided with a non-urgent planned cycle compared to 3% of HM. In the adjusted analysis, tumor type was the most important factor correlated with urgency of IC (OR 0.42, 95% CI: 0.25-0.72; p=0.001). ST pts (p=0.0001), older pts (p=0.004) and pts with KPS ≤50% (p=0.0001) were less likely to respond to chemotherapy. Only 15% of HM admissions and 46% of ST admissions had a PC consult. 60-day mortality was significantly higher in ST pts than HM (p=0.002). Conclusions: IC is associated with poorer outcomes for pts with advanced stage ST, pts with poor functional status and pts admitted for acute indications. Additionally, ST pts have a higher mortality after IC compared to HM. Utilization of IC should be standardized to account for different patient characteristics and to reduce the incidence of non-urgent administration. Based on this data, we created a standardized protocol to better assess the appropriateness of IC to improve patient care, QOL, and reduce chemotherapy and healthcare utilization at the end of life. [Table: see text]

6562 Background: Patients with advanced cancer that utilize end of life planning see benefits including better quality of life and medical care that is more consistent with their values. We developed a 30-day mortality predictive model using a machine learning algorithm and integrated it into a clinical decision support system (CDSS) that encourages clinicians to use the serious illness conversation (SIC) guide—a standardized questionnaire and conversational tool that facilitates end-of-life planning. The CDSS was piloted in the thoracic oncology clinic. We evaluated clinicians’ use of this system and its impact on patient outcomes. Methods: Between 4/14/21-1/15/22, information about patients identified by the model was sent to clinical teams via the electronic health record (EHR) to assess eligibility for a SIC. We reviewed the EHR for patients identified, SIC completion, and level of agreement by oncologists with the model. We evaluated the SIC guide responses using descriptive statistics and assessed differences in rates of hospice referral, hospital visits, and 30-day mortality by SIC completion status. Chi-squared test was used for testing association. Results: 94 patients were evaluated for SIC eligibility. Of these, oncologists agreed with 48 (51%) model predictions and SIC was completed for 28 (58%) of those patients. A median of 2.5 SIC eligibility assessments per week were completed, with a median time of 4 days from prediction to assessment. Likewise, a median of 1 SIC per week was completed, with a median time of 20 days from SIC eligibility assessment to conversation. Regarding the responses to the SIC guide, out of 28 patients, 75% have an appropriate understanding of their illness; 64% want to be fully informed of their medical information while 21% prefer information to be limited. Common patient goals were “being comfortable” (54%), “being at home” (29%) and “being independent” (25%). The most prevalent patient fears were “family concerns” (29%) or “physical suffering” (25%). The clinician who performs the SIC most often recommended an “additional conversation with physician” (39%), “conversation with family” (36%), or “referral to palliative care” (18%). SIC completion was associated with an increased rate of enrollment in hospice (33% vs 14%, P= 0.03) on univariate analysis. SIC was not associated with a difference in 30-day mortality or hospital visits. Multivariable analysis is ongoing. Conclusions: The machine-learning powered CDSS was adopted by the oncology care team within a reasonable timeframe. However, even if an oncologist used and agreed with the CDSS, the rate of eventual completion of SIC was not 100%. Additional barriers to SIC will be studied to optimize the CDSS. SIC completion may lead to increased enrollment in hospice and should continue to be studied as a standard component of comprehensive cancer care.

97 Background: The COVID-19 pandemic caused sudden changes in healthcare delivery, and new policies were rapidly implemented to ensure safety for patients and staff. However, COVID-19 testing requirements presented a barrier for many patients. Outdoor testing in New York City became less feasible during colder months, and oncology patients have additional concerns, such as limited mobility and immunosuppression. To address these barriers, we created an in-home COVID-19 testing program through a partnership between Community Paramedicine and Oncology: SWABBER (SWABS by Emergency Responders). We evaluated patient use of and satisfaction with SWABBER. Methods: SWABBER began in September 2020 as an interdisciplinary initiative to offer in-home, asymptomatic COVID-19 PCR testing for patients on active treatment in an effort to provide more coordinated care and improve patient experience. Tests were performed prior to the first day of each treatment cycle at no cost to patients. Randomly selected patients completed a brief survey about their experiences with the program, with questions on a seven-point Likert scale. Sociodemographic data was collected from the EMR, and we used a chi-square test to identify differences in patient use of SWABBER by race. Results: From September 8, 2020–April 1, 2021, we saw 7,204 patients for infusion, of whom 993 (14%) participated in SWABBER. The cohort of all patients receiving treatment was 45% White, 19% Black, 6% Asian, 29% Other, and 1% Unknown race. The SWABBER cohort was 36% White, 21% Black, 12% Asian, 29% Other, and 1% Unknown race. There was a significant difference in patient race between these two groups (P < 0.00001), with more Black and Asian patients in SWABBER compared to all patients receiving treatment. A total of 406 (41%) SWABBER patients completed the patient experience survey. The mean scores for overall experience and likelihood of recommending the program were 6.9, with standard deviations of 0.56 and 0.44, respectively. Conclusions: SWABBER enabled us to deliver care directly to patients’ homes, mitigating COVID-19 exposure while promoting accessible care and providing an increased benefit for minority patients. Through SWABBER, we achieved near-perfect patient experience ratings, reduced the burden of testing, created a safer environment for patients and staff, and kept cancer care on track. Future work will evaluate ways to maintain elevated patient experience and continue striving for inclusive care beyond the pandemic.[Table: see text]

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.

Detecting chemotherapy-related cardiac dysfunction (CRCD) before a decrease in left ventricular ejection fraction (LVEF) or an enzyme leak may help employ strategies to preserve ventricular function. Although the mechanisms of CRCD are not fully elucidated, myocyte apoptosis has been proposed to

A novel Drosophila-based approach resulted in a significant response by a patient with KRAS-mutant colorectal cancer., Colorectal cancer remains a leading source of cancer mortality worldwide. Initial response is often followed by emergent resistance that is poorly responsive to targeted therapies, reflecting currently undruggable cancer drivers such as KRAS and overall genomic complexity. Here, we report a novel approach to developing a personalized therapy for a patient with treatment-resistant metastatic KRAS-mutant colorectal cancer. An extensive genomic analysis of the tumor’s genomic landscape identified nine key drivers. A transgenic model that altered orthologs of these nine genes in the Drosophila hindgut was developed; a robotics-based screen using this platform identified trametinib plus zoledronate as a candidate treatment combination. Treating the patient led to a significant response: Target and nontarget lesions displayed a strong partial response and remained stable for 11 months. By addressing a disease’s genomic complexity, this personalized approach may provide an alternative treatment option for recalcitrant disease such as KRAS-mutant colorectal cancer.

308 Background: Reducing variation in care can improve outcomes and decrease costs. Evidence based medicine drives cancer guidelines and adherence promotes quality cancer care. Value based programs are based on adherence to pathways. Most institutions adopt costly cloud based clinical pathways products but none are mature products that fully integrate with the EHR and they require additional data entry. We present our simple Clinical Decision Support (CDS) tool for identifying best practice treatment protocols driven by the cancer diagnosis in the EMR for our large, multi-site, mixed academic and community cancer system. Methods: Our chemotherapy council must approve all protocols that are published in the system’s Epic Beacon library using a rigorous scoring system based on level of evidence and FDA or NCCN approval. Then each protocol is “tagged” appropriately: “Tier 1A”: Preferred Regimens/NCCN Approved; “Tier 1B”: Preferred Regimens/Chemo Council Approved (but not NCCN Approved); Tier 2: Specific Disease Management Team approved regimens; and finally “Other” or research protocols. When the oncologist enters the treatment plan in EPIC, a list of protocols are suggested, ordered by level of evidence, based on the cancer diagnosis and with the easily visible level of evidence or “tag” to allow data driven decision making. Results: We implemented our CDS tool December 12, 2019. As of mid-June, 2020 a total of 1637 treatment plans have been implemented. Of those, 1323 (81%) are Tier 1A, 310 (2%) are Tier 1B and 4 (.2%) are Tier 2. Thus demonstrating 81% adherence to NCCN approved regimens across the system, regardless of the line of treatment. GI and breast cancers were responsible for the most plans with the highest adherence to Tier 1A plans, specifically 92% among the breast cancer group. Multiple Myeloma and Sarcoma were tied for the lowest adherence rate of 58%. This data can be further stratified by medical oncologist. Interestingly, Multiple Myeloma had the highest utilization of Tier 1B protocols perhaps reflecting the rapidly changing literature that is ahead of the guidelines. Conclusions: We demonstrated adherence to NCCN protocols 81% of the time over a 6 month period and over multiple cancer types. Protocol tagging and reporting utilizing the EMR alone could be used as a powerful model for value based care. We identified disease areas that will require further education regarding evidence based treatment and can consider interventions including real time feedback to clinicians and/or best practice advisories or quality based incentives.

6 Background: Cancer patients are frequently admitted to the hospital requiring medical oncologists to take an active role in coordinating with multiple teams. In an effort to redesign care to put patients at the center and address increasing demands on our medical oncologist’s time, we created the Oncology Coordinator (OC) role focused on care setting transitions. We aimed to evaluate whether the OC would improve quality of care and decrease healthcare utilization. Methods: The OCs, are non-clinical and serve as a single point of contact for disease-based teams as patients prepare for elective admissions or discharge from the hospital. The 3 OCs received specialized training in systems and processes in both settings. They coordinate outpatient appointments, prescription delivery, transportation while also providing clinical support. Additionally, they facilitate two interdisciplinary rounds per day across three dedicated oncology units and assist with patients off-unit. We evaluated all patient discharges facilitated by the OCs during 1/1/19-2/29/20 and compared that to non-OC facilitated discharges. Using descriptive statistics, we evaluated the OCs impact on 7- and 30-day readmissions, discharge before noon rate (DBN), average time from admission to chemotherapy start and patient experience as measured by Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). Results: We had a total of 2,818 discharges between 1/1/19-2/29/20; 1,032 (36.6%) facilitated by the OCs. For those OC facilitated discharges we observed a 5.07% reduction in 7-day readmissions and 30-day readmissions (2.6%). We observed an overall higher average monthly rate of DBN (4.85%) compared to non-OC facilitated discharges. In addition, the average time from admission to chemotherapy administration decreased by 1 hour 31 minutes (6.8%) for the OC facilitated admissions. In the HCAHPS survey, there were improvements in Discharge Information and Care Transitions on the inpatient units where OCs were most active. Conclusions: At our academic medical center, the OCs have contributed to reduction in readmissions, time from admission to chemotherapy administration as well as improvements in discharges before noon and patient experience. This pilot demonstrates that investment in dedicated lay staff to facilitate admissions and discharges for cancer patients across care settings could lead to meaningful improvements in healthcare utilization, quality and the patient experience. Future work will evaluate the sustainability of this program and evaluate association with healthcare costs.

87 Background: New York City was the epicenter of the COVID-19 pandemic. Public concern about exposure and policies to “flatten the curve” led to abrupt curtailment of health care service use, including cancer. Innovative strategies to mitigate the disruption, including telemedicine, was utilized. However, this may be less accessible to minority populations. We evaluated the differences in telehealth use among minorities and non-minorities with cancer. Methods: Our health system includes an NCI designated cancer center and 8 ambulatory sites across New York City. Utilizing the electronic medical record (EMR) we collected data on all cancer patients with an in-person or telehealth visit during the peak of the pandemic from March 1, 2020 to June 1, 2020. Race/ethnicity and visit type data was collected from the EMR. Telehealth includes video visits and telephone encounters. We used ANOVA and-chi square where appropriate to identify differences between the racial and ethnic groups in terms of use and type of telehealth and compared any differences to baseline cancer center demographics. Results: There were a total of 7,681 telehealth visits during 3/1/2020-6/1/2020; 76% were video visits. At baseline in 2019, < 1% of all patient visits were conducted via telehealth. Within our cancer center our demographic breakdown for patients seen in 2019 includes 42% patients were white, 23% Black, 14% Hispanic and 7% Asian. Among those patients utilizing video visits, 50% patients were white, 17% Black, 8% Asian, and 5% Hispanic. Among those patients utilizing phone encounters, 43% patients were white, 23% Black, 7% Hispanic and 6% Asian. Conclusions: During the COVID-19 pandemic our utilization of telehealth increased exponentially. There were significant disparities observed in the use of telehealth with Black, Hispanic and Asian patients having less utilization. These findings are important as telehealth use will now become more integrated into standard oncologic care, and it is likely that we will have a second or third wave of COVID-19 infections. Future work to understand the determinants of these disparities and interventions are needed. [Table: see text]

249 Background: The Mount Sinai Health System was in the epicenter of the COVID-19 pandemic in NYC. We implemented dynamic testing, isolation and treatment policies in order to continue delivering necessary cancer treatments and ensure the safety of our patients and our staff. Here, we describe the rapid rollout of IT optimizations to enhance the delivery of quality cancer care during the COVID-19 pandemic. Methods: We developed a cancer center incident command structure that involved integration of a health informaticist, IT analyst, and data analyst along with cancer center leadership to help create and optimize electronic health record (EHR)-based tools to support the clinical mission. Results: We developed and implemented EHR-based COVID-19 screening protocols, clinical decision support, reporting and analytics tools, and telehealth technology related to COVID-19 (Table). Conclusions: Dynamic EHR optimizations were essential to continue our cancer care delivery services during the pandemic. [Table: see text]

At times we encounter clinical problems for which there are no directly applicable evidence-based solutions, but we are compelled by circumstances to act. When doing so we rely on related evidence, general principles of best medical practice, and our experience. Each “Current Clinical Practice” feature article in Seminars in Oncology describes such a challenging presentation and offers treatment approaches from selected specialists. We invite readers' comments and questions, which, with your approval, will be published in subsequent issues of the Journal. It is hoped that sharing our views and experiences will better inform our management decisions when we next encounter similar challenging patients. Please send your comments on the articles, your challenging cases, and your treatment successes to me at dr.gjmor ris@gmail.com. I look forward to a lively discussion. Gloria J. Morris, MD, PhD Current Clinical Practice Feature Editor

298 Background: A comprehensive chemotherapy informed consent process improves shared decision-making. Additionally, the Oncology Care Model (OCM) emphasizes providing patients with a documented care plan that contains the 13 components in the Institute of Medicine Care Management Plan. Within our health system, we incorporated the care plan into our existing chemotherapy consent process and utilized technology to increase compliance and reduce administrative burden. Methods: Our 2 phase PDSA included: 1) updating our existing paper chemotherapy form with the 13 components of the IOM care plan and then 2) piloting an electronic version of the chemotherapy consent form. We updated our new chemotherapy consent with the addition of Prognosis, Expected Response to Treatment, Potential Effect on Quality of Life, Potential Benefits/Goals of Treatment, and added more options for potential side effects/harm. Given the increased administrative burden, we created and piloted the use of an electronic version of the consent form in our breast oncology program. Results: Baseline, monthly random chart audit of 20-40 patients revealed compliance with completing every question of the paper chemotherapy consent at 30% of all patients receiving IV chemotherapy at our cancer center that month- 7/2018. When the new chemotherapy consent incorporating the IOM elements was rolled out, compliance initially rose to 50% (8/2018), however the following months dropped to 10-20% (9-11/2018) and then back up to 41% briefly (12/2018). The results were low and inconsistent. A pareto chart confirmed that redundancy and too many questions were the reasons for under-completion. By auto-populating fields for certain questions on the paper consent, compliance increased to 75% (2/2019). Finally within our breast cancer pilot group our compliance rose to 100% (3-4/2019), by converting to an electronic form with the maximum options for auto-population and drop-down selections for certain fields. Conclusions: Incorporating regulatory requirements into an existing workflow can reduce administrative burden. The use of innovative technology can further increase clinician and OCM compliance while delivering value to patients.

TPS1109 Background: Triple negative breast cancer (TNBC) is an aggressive disease with unmet clinical needs. Women with TNBC tend to be younger and demonstrate early recurrence, higher histological grade, higher rate of visceral metastasis and increased mortality rates when compared to hormone positive breast cancer. Prognosis for metastatic TNBC is especially poor. Due to lack of targeted therapies, there is no standard treatment of choice for triple negative breast cancer and chemotherapy remains the accepted standard. Many chemotherapeutic agents have been reported to have clinical activity either as single agent or in combination. Seventy percent of breast cancers with BRCA-1 germline mutations are triple negative, which suggests a shared carcinogenic pathway between them. In preoperative and metastatic settings, both TNBC and BRCA-1 associated breast cancers are particularly sensitive to DNA cross-linking agents such as platinum compounds due to defective DNA repair by homologous recombination. The recent TNT trial showed in patients with triple negative metastatic or recurrent locally advanced breast cancer with BRCA1/2 mutations, carboplatin resulted in a significantly higher overall response rate versus docetaxel (68% versus 33.3%; p=0.03). Triple negative breast cancers are associated with a high frequency of PTEN loss, which leads to mTOR activation. Moreover, it has been reported that mTOR activation may confer resistance to platinum agents such as cisplatin, a phenomenon that may be reversible by the addition of an mTOR inhibitor such as everolimus. There are reports of synergy between mTOR inhibitors and platinum compounds in pre-clinical and clinical data. Methods: We have opened a multi-centered randomized phase II trial comparing carboplatin AUC 4 q 3 weeks vs carboplatin AUC 4 q 3 weeks combined with daily 5 mg everolimus. 41 of planned 72 patients from the Mount Sinai Health System have been enrolled and are randomized in a 2:1 allocation. The primary objective is to compare progression-free survival in patients treated with carboplatin+everolimus to patients treated with carboplatin alone. Patients may have had up to 3 prior regimens for metastatic disease. Exploratory biomarker assessment is being done to identify markers of response. Clinical trial information: NCT02531932.

Fever occurs at high rates in patients with chemotherapy-induced neutropenia and is considered an oncologic emergency. Numerous algorithms have been developed to guide treatment decisions. Prompt care and the initiation of empiric antibiotic therapy are critically important universal aspects of these treatment-decision schemata. Fever may be the only sign of infection, as in patients with cancer who are undergoing chemotherapy, the immune response is attenuated. In the majority of cases, no etiology for neutropenic fever is uncovered; nonetheless, a thorough workup is essential. The workup allows practitioners to risk stratify patients as being at low or high risk for infectious complications so that appropriate care can be administered. Although it is important to note that there are management algorithms to follow, every patient may present and respond differently. We generally start with broad-spectrum monotherapy for Gram-negative bacteria and then consider whether Gram-positive or antifungal coverage is necessary based on the clinical picture, including factors such as duration and degree of neutropenia. It is important for all practitioners to understand how to care for patients with neutropenic fever because it is a common and treatable condition.

46 Background: As the number of patients living with cancer increases, a growing proportion of hospital inpatients will have an advanced cancer diagnosis. Data suggests that an unscheduled hospitalization for a patient with advanced cancer strongly predicts a median survival of less than 6 months. As hospitalists more frequently become the primary team taking care of admitted cancer patients, communication between a patient's oncologic care team and inpatient primary providers is crucial. We sought to implement and assess the impact of improved oncology consult documentation of patient prognosis on outcomes of advanced cancer patients admitted to our hospitalist medicine teaching services. Methods: We implemented an EMR-based oncology consult note template which required documentation of prognosis, potential future treatment options (if available), and advance care planning. We reviewed all patients with stage IV solid tumors admitted to the hospitalist teaching service for 8 weeks prior and 8 weeks post-template implementation for comparison. We utilized descriptive statistics and chi-squared testing as appropriate for analysis. Results: We evaluated 51 patients in the pre- and 36 patients in the post-intervention groups. Post-intervention, there was an improvement in documentation of prognosis (29.4% vs. 52.8%, p = 0.03), advanced care planning (37.2% vs. 83.3%, p < 0.0001), and in number of palliative care consults (58.8% vs. 83.3%, p = 0.02). On average, goals of care conversations occurred 2 days earlier in the post-intervention group (11 vs 9 days). Similarly, there was a decrease in inpatient chemotherapy administration (3 cases vs. 0), unit codes (2 vs. 0) and in-hospital death (23.5% vs. 11.1%). Conclusions: Creation of an oncology consult note template which incorporates current oncologic prognostic information improved documentation of prognosis and advance care planning as well as outcomes for advanced solid tumor oncology patients and enhanced inter-service communication. Based on these results, continued and targeted interventions are planned to further improve interservice communication.

171 Background: As the number of patients living with cancer increases, a growing proportion of hospital inpatients will have an advanced cancer diagnosis. Data suggests that an unscheduled hospitalization for a patient with advanced cancer strongly predicts a median survival of less than 6 months. As hospitalists more frequently become the primary team taking care of admitted cancer patients, communication between a patient's oncologic care team and inpatient primary providers is crucial. We sought to implement and assess the impact of improved oncology consult documentation of patient prognosis on outcomes of advanced cancer patients admitted to our hospitalist medicine teaching services. Methods: We implemented an EMR-based oncology consult note template which required documentation of prognosis, potential future treatment options (if available), and advance care planning. We reviewed all patients with stage IV solid tumors admitted to the hospitalist teaching service for 8 weeks prior and 8 weeks post-template implementation for comparison. We utilized descriptive statistics and chi-squared testing as appropriate for analysis. Results: We evaluated 51 patients in the pre- and 36 patients in the post-intervention groups. Post-intervention, there was an improvement in documentation of prognosis (29.4% vs. 52.8%, p = 0.03), advanced care planning (37.2% vs. 83.3%, p < 0.0001), and in number of palliative care consults (58.8% vs. 83.3%, p = 0.02). On average, goals of care conversations occurred 2 days earlier in the post-intervention group (11 vs 9 days). Similarly, there was a decrease in inpatient chemotherapy administration (3 cases vs. 0), unit codes (2 vs. 0) and in-hospital death (23.5% vs. 11.1%). Conclusions: Creation of an oncology consult note template which incorporates current oncologic prognostic information improved documentation of prognosis and advance care planning as well as outcomes for advanced solid tumor oncology patients and enhanced inter-service communication. Based on these results, continued and targeted interventions are planned to further improve inter-service communication.

TPS3116Background: Recent studies have revealed only modest responses to single-agent immune checkpoint blockade in metastatic breast cancer (MBC) and recurrent ovarian cancer (ROC) patients as com...

TPS1103Background: Overexpression or amplification of HER2 occurs in approximately 15 – 20% of patients and about half of these tumors are hormone receptor (HR) positive. Studies suggest that this ...

e12594Background: Obesity and weight gain are significant concerns for breast cancer survivors. Obesity at diagnosis of breast cancer is an established negative prognostic factor and post-diagnosis...

71 Background: The safe administration of oral chemotherapy is a challenging yet important process to ensure patient safety. Comprehensive initial and subsequent documentation especially regarding dosage and toxicities in the plan of care for patients receiving oral chemotherapy can be instrumental in improving patient safety by ensuring comprehensive health information is available to all health team members. Methods: We are undertaking a rapid cycle improvement project to improve initial and subsequent documentation of new oral chemotherapy care plans by increasing our compliance with the number of components of an oral chemotherapy care plan (as outlined by QOPI’s 13 standards). We performed a baseline chart audit to confirm our current low level of compliance. We then created a process to use smart phrases that include necessary QOPI elements for progress notes upon new prescription of oral chemotherapy as well as smart phrases for subsequent patient initiated telephone encounters regarding oral chemotherapy, that feed into a universal “med note” location. Post implementation, we will perform a chart audit to determine our updated compliance. Staff satisfaction with the old vs the new process will be compared to verify that we have created a more efficient system to answer patient questions regarding oral chemotherapy. Results: Baseline chart audit revealed the number of components of an oral chemotherapy care plan (as per QOPI standards) documented in the EMR was 43% (i.e contained 5/13 components). A Pareto chart confirmed that underutilization of current EMR resources and lack of knowledge regarding QOPI standards were common reasons for poor oral chemotherapy documentation. Implementation of smart phrases for documentation for initial patient encounters and subsequent telephone encounters is currently in process to determine our new level of compliance with QOPI standards for a sustained and meaningful improvement. Conclusions: This quality improvement initiative was designed to improve patient safety by enhancing documentation of an oral chemotherapy care plan in the EMR and also to create a more efficient system to answer and document patient phone calls regarding oral chemotherapy.

Background: Although the remission rate of cancer has increased, early and late cardiotoxicity due to chemotherapy remains a problem. Early detection of cardiotoxicity should allow preservation of normal ventricular function. The novel strategies of early detection have included genotypic (miRNA

237 Background: The 2005 ASCO surveillance guidelines for Stage II/III colon cancer recommended chest, abdomen (and pelvis) imaging annually for 3 years, checking a CEA every 3 months for the first 3 years and colonoscopy 1 year after surgery or preoperatively. Our primary aim was to determine Mount Sinai Hospital’s (MSH) compliance rate with ASCO surveillance guidelines, with a goal of achieving 80% compliance. We hypothesized a bias to over-imaging. A secondary aim was to determine the frequency at which high risk Stage II patients were referred to medical oncology. Methods: A retrospective study of 118 patients with newly diagnosed stage II or III colon cancer at MSH in 2011 and 2012 was undertaken. Results: Compliance rates for early surveillance measures included. 1. Checking CEA approximately every 3 months: Stage II - 84%; Stage III - 88%. 2. Colonoscopy guidelines: Stage II - 72% and Stage III - 94%. Imaging post surgery or adjuvant treatment: Stage II - 48% and Stage III - 70%. Subsequent imaging: Stage II - 44%; Stage III - 28%. (See Table.) Overall compliance with early surveillance guidelines was 55% for Stage II patients and 83% for Stage III patients. All of the non-compliance for subsequent imaging was due to overscanning. Only 62% of high risk Stage II patients were referred to medical oncology. Conclusions: Our baseline compliance to early surveillance measures for colon cancer did not consistently achieve our pre-specified benchmark. Specific areas of improvement include avoidance of overscanning during colon cancer surveillance after adjuvant chemotherapy and an improved rate of referral of high risk Stage II patients to medical oncology after surgery. [Table: see text]