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PURPOSE: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. METHODS: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman's Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (phase I) and workflow (phase II and III) using electronic health data; qualitative evaluation with semistructured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. RESULTS: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. CONCLUSION: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.

351 Background: One key challenge of practice transformation activities, such as remote symptom monitoring (RSM) using electronic patient reported outcomes (ePROs), is identification of patients starting treatment. In real-world settings, reliance on referrals is likely to miss patients. We describe the difficulties encountered in patient identification and the subsequent changes implemented in protocol to remediate this. Methods: We conducted two PDSA cycles focused on identification and engagement of patients for RSM at the Mitchel Cancer Institute (MCI). Target patient capture was > 75%. Modifications to the patient identification process were documented. Schedules of physicians participating in the RSM program were reviewed from 6/2021 – 5/2022 to identify eligible patients. Patients were considered eligible if they were starting chemotherapy, targeted therapy, or immunotherapy. Patients seeking a second opinion were excluded. Patient demographics, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Initial clinic roll-out was conducted in gynecologic oncology, with expansion to breast and thoracic oncology in 10/2021 and 3/2022, respectively. The proportion of eligible patients approached per month was reported.Results: In the first PDSA cycle, the eligibility criteria was defined. Although clinical trials included advanced disease, non-clinical staff screening expressed concern about determining advanced vs. early-stage disease. Thus, inclusion criteria was broadened to include all patients starting treatments. From 6/2021 –8/2021, navigators identified patients by screening patients who presented for chemo-education visits. The navigation team approached 23 patients during this period. However, this process didn’t identify all eligible patients as not all patients beginning treatment received chemo-education visits. In PDSA Cycle 2, the process for new patient contact from initial call for appointment through treatment was reviewed. The implementation team screened all patients in a physician’s schedule a week prior to the office visit as well as on the day of visit. This updated process identified all eligible patients starting either intravenous or oral chemotherapy. The recruitment process was modified to screen the physician schedules rather than chemo educator visits. From 9/2022-5/22, the proportion of eligible patients identified and approached remained high at 100%. This methodological screening process helped the navigation team identify all eligible patients in an efficient manner and they reported comfort in expanding to additional disease teams. Conclusions: Systematic screening of physician schedules can be successfully leveraged for patient identification and reduce time spent manually screening for eligible patients by non-clinical navigators. Clinical trial information: NCT04809740.

272 Background: Despite evidence of clinical benefits, widespread implementation of remote symptom monitoring has been limited. We describe a process of adapting a remote symptom monitoring intervention developed in a research setting to a real-world clinical setting at two cancer centers. Methods: This formative evaluation assessed core components and adaptations to improve acceptability and fit of remote symptom monitoring using Stirman’s Framework for Modifications and Adaptations. Implementation outcomes were evaluated in pilot studies at the two cancer centers testing technology (Phase I) and workflow (Phase II and III) using electronic health data; qualitative evaluation with semi-structured interviews of clinical team members; and capture of field notes from clinical teams and administrators regarding barriers and recommended adaptations for future implementation. Results: Core components of remote symptom monitoring included electronic delivery of surveys with actionable symptoms, patient education on the intervention, a system to monitor survey compliance in real-time, the capacity to generate alerts, training nurses to manage alerts, and identification of personnel responsible for managing symptoms. In the pilot studies, while most patients completed > 50% of expected surveys, adaptations were identified to address barriers related to workflow challenges, patient and clinician access to technology, digital health literacy, survey fatigue, alert fatigue, and data visibility. Conclusions: Using an implementation science approach, we facilitated adaptation of remote symptom monitoring interventions from the research setting to clinical practice and identified key areas to promote effective uptake and sustainability.

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.

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.

268 Background: Remote symptom monitoring (RSM) using patient-reported outcomes has been shown to reduce symptom burden and hospitalizations in clinical trials. However, little is known about how willing patients are to participate in remote symptom monitoring in real-world settings, particularly for vulnerable patient populations. This study aims to compare characteristics of cancer patients enrolled vs. patients who declined enrollment into RSM. Methods: This prospective study used data that assessed the characteristics of patients who enrolled vs. patients who declined enrollment into RSM. Inclusion criteria included participants’ age ≥18 with cancer who received chemotherapy, targeted therapy, or immunotherapy at the University of Alabama at Birmingham. Race and ethnicity (Black or African American, White, Asian, other and unknown), sex, cancer type (breast, gastrointestinal [GI], genitourinary [GU], gynecological [GYNX], head and neck, leukemia, lymphoma, melanoma, myeloma and other), urban/rural residence, Area Deprivation Index (ADI), and insurance type (Medicaid, Medicare, none, other and private) were abstracted from electronic medical records (EMR) and PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and medians and interquartile ranges for continuous variables. Differences in enrollment status characteristics were calculated using measures of effect size such as Cramer’s V. Results: Of the 307 patients, two thirds of patients were female (71%); 25% were Black or African American and 66% were White patients; 15% lived in an area of higher disadvantage. For insurance, 46%, 26%, 10%, 8%, and 9% of patients had Private, Medicare, Medicaid, other insurance, and no insurance, respectively. The proportion of patients who declined enrollment was higher for males than females (22% vs. 10%), Black or African American than White (18% vs 13%); and having Medicare than private insurance (22% vs. 10%). Compared to those who enrolled, patients who declined enrollment were more often to be male (V:0.2), Black or African American (V:0.1); and have Medicare insurance (V:0.2). Patients enrolled vs. declined in RSM had similar ADI scores (V:0.01). Conclusions: This study demonstrates that potentially vulnerable patients, including Black patients and those with public insurance, have lower RSM engagement. Future analysis is needed to understand participation barriers and how to better engage diverse populations to ensure optimal healthcare delivery to all patients.

270 Background: Patients now have the ability to utilize electronic patient reported outcomes (ePROs) for remote symptom monitoring (RSM). This analysis seeks to better understand trajectory of reported symptoms during treatment for patients with gynecologic cancer participating in RSM. Methods: We approached patients with gynecological cancer initiating treatment at the Mitchell Cancer Institute (MCI) between 7/1/21-4/30/2022. Patients were eligible if they were starting chemotherapy, targeted therapy, or immunotherapy for a new cancer. Patients seeking a second opinion were excluded. Enrolled patients received symptom survey (PRO-CTCAE questions) via text or email once per week. Initially, only severe alerts were forwarded to the clinical care team; moderate alerts were forwarded to clinical teams once they were comfortable with alert management. Patients completed symptom assessments for 24 weeks or until withdrawal. Patient age at enrollment, race, sex, cancer type, cancer stage, and PROs were abstracted from electronic health records and the PRO platform (Carevive). Descriptive statistics were calculated using frequencies and percentages for categorical variables and median and interquartile ranges (IQR) for continuous variables. Results: A total of 60 female patients with gynecological cancer were enrolled; 33% were Black or African American and 67% were White; median age was 61 years (IQR 53-68). Seventy-eight percent (47/60) of patients reported 379 symptoms with at least one moderate or severe alert during this time period; 32% considered moderate and 68% considered severe. Overall, the most frequently reported symptom was pain (29%). At baseline (week 0), 14% and 41% of 56 patients reported moderate symptoms and severe symptoms, respectively. Symptom burden decreased over time with 4% and 7% of 27 patients who completed a survey at 12 weeks reporting moderate and severe symptoms. Specific symptom trajectories followed similar patterns. Conclusions: In our sample, patients reported the majority of symptoms during the first three months of treatment. Symptom trajectory decreased with time, suggesting symptoms are being effectively monitored and addressed by the clinical teams engaging in RSM. Future research is needed to understand if symptom improvement translates to increased quality of life, decreased hospitalizations, and increased survival for patients, as well as lessen the burden of call volume on the clinical team.