PURPOSE Remote symptom monitoring (RSM) using electronic patient-reported outcomes enables patients with cancer to communicate symptoms between in-person visits. A better understanding of key RSM implementation outcomes is crucial to optimize efficiency and guide implementation efforts. This analysis evaluated the association between the severity of patient-reported symptom alerts and time to response by the health care team. METHODS This secondary analysis included women with stage I-IV breast cancer who received care at a large academic medical center in the Southeastern United States (October 2020-September 2022). Symptom surveys with at least one severe symptom alert were categorized as severe. Response time was categorized as optimal if the alert was closed by a health care team member within 48 hours. Odds ratios (ORs), predicted probabilities, and 95% CIs were estimated using a patient-nested logistic regression model. RESULTS Of 178 patients with breast cancer included in this analysis, 63% of patients identified as White and 85% of patients had a stage I-III or early-stage cancer. The median age at diagnosis was 55 years (IQR, 42-65). Of 1,087 surveys included, 36% reported at least one severe symptom alert and 77% had an optimal response time by the health care team. When compared with surveys that had no severe symptom alerts, surveys with at least one severe symptom alert had similar odds of having an optimal response time (OR, 0.97; 95% CI, 0.68 to 1.38). The results were similar when stratified by cancer stage. CONCLUSION Response times to symptom alerts were similar for alerts with at least one severe symptom compared with alerts with no severe symptoms. This suggests that alert management is being incorporated into routine workflows and not prioritized based on disease or symptom alert severity.

421 Background: For successful remote symptom monitoring using patient-reported outcomes, nurses should respond to alerts in a timely fashion. Where clinical trials utilized research staff for alert management, the shift to standard-of-care delivery necessitates that this responsibility be added as a task to an already strained nursing workforce. Little is known about strategies to engage nurses to improve timeliness of alert management. Methods: In this quality improvement initiative, we aimed to improve timeliness of alert closures generated by moderate or severe symptoms within a remote symptom monitoring program. Optimal closure was defined as < 48 hours, which was consistent with institutional requirements for response to patient phone calls. A continuous quality improvement approach, with multiple Plan Do Study Act (PDSA) cycles was conducted. Data was captured from the electronic medical record and PRO platform (Carevive). Descriptive statistics included frequencies and percentages. The proportion of alerts closed each month < 48 hours, 48-72 hours, 3-7 days, and > 7 days were reported overall and by disease team (i.e., major cancer types). Surveys not closed were considered > 7 days. The timing of strategies to improve nursing engagement were documented and evaluated for impact on alert closure. Results: From June 1, 2021-May 31, 2022, 1121 moderate or severe alerts were generated from 234 patients. Disease teams had variable remote symptom monitoring start dates: breast, leukemia, and limited gynecologic (prior to 6/2021); myeloma and gastrointestinal (7/2021); genitourinary (10/2021); head and neck (12/2021); melanoma (2/2022); and Lymphoma (4/2022). In 6/2021, the overall alert closure at < 48 hours, 48-72 hours, 3-7 days, and > 7 days was 57%, 4%, 14%, and 25% respectively (n = 28). To improve alert closures, several key strategies were deployed to improve alert closure times including disease-specific reporting and meetings with nursing leadership (10/2021); identification of a nurse champion, creation of “cheat sheets” to remind nurses how to close alerts, and individualized calls with nurses with open alerts (1/2022), and inclusions of requirement to close alerts in nursing newsletters (2/2022). Overall, alert closure less than 48 hours improved to 61% by 12/2021 (n = 97) and to 69% by 5/2022 (n = 167). Disease group alert closure varied, with higher closure more commonly in teams with greater duration of use, such as breast cancer team with an alert closure of 85% < 48 hours in May 2022. Conclusions: Key nursing engagement strategies improve alert closure for remote symptom monitoring programs implemented in real-world settings.

341 Background: Remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs) allow for patients with cancer to communicate symptoms to their clinical team between clinic visits. Prior randomized control trials of RSM focused on advanced cancer, and less data are available for patient with early stage cancers. The University of Alabama at Birmingham (UAB) implemented RSM for early stage (I-III) and advanced stage (IV) patients on active treatment. This study evaluates nurses’ real-world response time to alerts by varying severity and by patients cancer stages. Methods: This study included women with stage I-IV breast cancer who received care at UAB from October 2020 through May 2022. The program was first implemented in the breast clinic allowing for larger patient numbers with early and advanced stage breast cancer. A composite score for symptom severity is automatically calculated in the Carevive® platform for moderate, severe, or worsening symptoms using patient responses for frequency, severity, and interference. The nurse receives an alert if a symptom is moderate or severe. Surveys with at least one severe alert were categorized as severe and response time was categorized as optimal if the survey was closed within 48 hours (goal time for phone message follow-up). Odds ratios (OR), predicted probabilities, and 95% confidence intervals (CI) were estimated using a patient nested logistic regression evaluating time to response comparing surveys with at least one severe alert notification to those with no severe, adjusting for age at enrollment, race, cancer stage, provider who closed the surveys, and quarter from study start and date. An interaction between severity and cancer stage was evaluated. Results: Of 137 patients included in this study, 64% were White; 86% were diagnosed with early-stage breast cancer. The median age at diagnosis was 54 (27-79). Of 802 surveys included, 38% reported at least one severe symptom and 70% had an optimal response time. Similar results were seen when stratified by early vs. advanced stage with 39% and 38% reporting at least one severe alert and 68% and 71% an optimal response time, respectively. In our adjusted analysis, when compared with surveys that had no severe alerts, surveys with at least one severe alert had similar odds of having an optimal response time (OR, 1.29; 95%CI, 0.88, 1.89). No significant interaction between severity and stage was observed on the odds of optimal response time. Conclusions: Response times to alerts were similar regardless of the severity of the alert and cancer stage, suggesting alert management is incorporated into routine workflows and not prioritized based on disease or alert severity. Additional research is needed to understand factors contributing to non-optimal response times.