Evaluating nurses' time to response by severity and cancer stage in a remote symptom monitoring program.
Authors: Franks Jeffrey , Caston Nicole E. , Balas Nora , Lalor Fallon , Eltoum Noon , Dent D'Ambra , Hildreth Keyonsis , Patterson Megan , Azuero Andres , Jackson Bradford E. , Dionne-Odom James Nicholas , Huang Chao-Hui , Stover Angela M. , Howell Doris , Weiner Bryan J , McGowen Chelsea L. , Basch Ethan , Young Pierce Jennifer , Ojesina Akinyemi I , Rocque Gabrielle Betty 
Published: September 2022

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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.