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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 analys...

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Autores principales: Caston, Nicole E., Franks, Jeffrey A., Balas, Nora, Eltoum, Noon, Thigpen, Haley, Patterson, Megan, Azuero, Andres, Ojesina, Akinyemi I., Dent, D'Ambra N., Hildreth, Keyonsis, Lalor, Fallon R., McGowen, Chelsea, Huang, Chao-Hui S., Dionne-Odom, J. Nicholas, Weiner, Bryan J., Jackson, Bradford E., Basch, Ethan M., Stover, Angela M., Howell, Doris, Pierce, Jennifer Y., Rocque, Gabrielle B.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Wolters Kluwer Health 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530733/
https://www.ncbi.nlm.nih.gov/pubmed/37279409
http://dx.doi.org/10.1200/CCI.23.00015
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author Caston, Nicole E.
Franks, Jeffrey A.
Balas, Nora
Eltoum, Noon
Thigpen, Haley
Patterson, Megan
Azuero, Andres
Ojesina, Akinyemi I.
Dent, D'Ambra N.
Hildreth, Keyonsis
Lalor, Fallon R.
McGowen, Chelsea
Huang, Chao-Hui S.
Dionne-Odom, J. Nicholas
Weiner, Bryan J.
Jackson, Bradford E.
Basch, Ethan M.
Stover, Angela M.
Howell, Doris
Pierce, Jennifer Y.
Rocque, Gabrielle B.
author_facet Caston, Nicole E.
Franks, Jeffrey A.
Balas, Nora
Eltoum, Noon
Thigpen, Haley
Patterson, Megan
Azuero, Andres
Ojesina, Akinyemi I.
Dent, D'Ambra N.
Hildreth, Keyonsis
Lalor, Fallon R.
McGowen, Chelsea
Huang, Chao-Hui S.
Dionne-Odom, J. Nicholas
Weiner, Bryan J.
Jackson, Bradford E.
Basch, Ethan M.
Stover, Angela M.
Howell, Doris
Pierce, Jennifer Y.
Rocque, Gabrielle B.
author_sort Caston, Nicole E.
collection PubMed
description 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.
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spelling pubmed-105307332023-09-28 Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer Caston, Nicole E. Franks, Jeffrey A. Balas, Nora Eltoum, Noon Thigpen, Haley Patterson, Megan Azuero, Andres Ojesina, Akinyemi I. Dent, D'Ambra N. Hildreth, Keyonsis Lalor, Fallon R. McGowen, Chelsea Huang, Chao-Hui S. Dionne-Odom, J. Nicholas Weiner, Bryan J. Jackson, Bradford E. Basch, Ethan M. Stover, Angela M. Howell, Doris Pierce, Jennifer Y. Rocque, Gabrielle B. JCO Clin Cancer Inform ORIGINAL REPORTS 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. Wolters Kluwer Health 2023-06-06 /pmc/articles/PMC10530733/ /pubmed/37279409 http://dx.doi.org/10.1200/CCI.23.00015 Text en © 2023 by American Society of Clinical Oncology https://creativecommons.org/licenses/by-nc-nd/4.0/Creative Commons Attribution Non-Commercial No Derivatives 4.0 License: https://creativecommons.org/licenses/by-nc-nd/4.0/
spellingShingle ORIGINAL REPORTS
Caston, Nicole E.
Franks, Jeffrey A.
Balas, Nora
Eltoum, Noon
Thigpen, Haley
Patterson, Megan
Azuero, Andres
Ojesina, Akinyemi I.
Dent, D'Ambra N.
Hildreth, Keyonsis
Lalor, Fallon R.
McGowen, Chelsea
Huang, Chao-Hui S.
Dionne-Odom, J. Nicholas
Weiner, Bryan J.
Jackson, Bradford E.
Basch, Ethan M.
Stover, Angela M.
Howell, Doris
Pierce, Jennifer Y.
Rocque, Gabrielle B.
Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title_full Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title_fullStr Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title_full_unstemmed Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title_short Evaluating Nurses' Time to Response by Severity and Cancer Stage in a Remote Symptom Monitoring Program for Patients With Breast Cancer
title_sort evaluating nurses' time to response by severity and cancer stage in a remote symptom monitoring program for patients with breast cancer
topic ORIGINAL REPORTS
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10530733/
https://www.ncbi.nlm.nih.gov/pubmed/37279409
http://dx.doi.org/10.1200/CCI.23.00015
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