Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer.

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.

Ineligible, Unaware, or Uninterested? Associations Between Underrepresented Patient Populations and Retention in the Pathway to Cancer Clinical Trial Enrollment.

PURPOSE: Cancer clinical trials can benefit current and future patients; however, Black patients, rural residents, and patients living in disadvantaged areas are often underrepresented. Using an adapted version of Unger and colleagues' model of the process of clinical trial enrollment, we evaluated the relationship between underrepresented patient populations and trial end points. METHODS: This retrospective study included 512 patients with breast or ovarian cancer who were prescribed a therapeutic drug at the University of Alabama at Birmingham from January 2017 to February 2020. Patient eligibility was assessed using open clinical trials. We estimated odds ratios and 95% CIs using logistic regression models to examine the relationship between underrepresented patient populations and trial enrollment end points: eligibility, interest, offer, enrollment, and declining enrollment. RESULTS: Of the patients in our sample, 27% were Black, 18% were rural residents, and 19% lived in higher disadvantaged neighborhoods. In adjusted models, each comparison group had similar odds of being eligible for a clinical trial. Black versus White patients had 0.40 times the odds of interest in clinical trials and 0.56 times the odds of enrollment. Patients living in areas of higher versus lower disadvantage had 0.46 times the odds of enrolling and 3.40 times the odds of declining enrollment when offered. CONCLUSION: Eligibility did not drive clinical trial enrollment disparities in our sample; however, retention in the clinical trial enrollment process appears to vary by group. Additional work is needed to understand how interventions can be tailored to each population's specific needs.

Adaptation of Remote Symptom Monitoring Using Electronic Patient-Reported Outcomes for Implementation in Real-World Settings.

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.