Women's decision satisfaction and psychological distress following early breast cancer treatment: a treatment decision support role for nurses

2014 ◽  
Vol 20 (1) ◽  
pp. 8-16 ◽  
Author(s):  
Lea M Budden ◽  
Barbara A Hayes ◽  
Petra G Buettner
Keyword(s):  
Breast Cancer ◽  
Psychological Distress ◽  
Decision Support ◽  
Cancer Treatment ◽  
Early Breast Cancer ◽  
Treatment Decision ◽  
Breast Cancer Treatment ◽  
Decision Satisfaction

scholarly journals Decision support networks of women newly diagnosed with breast cancer.

2017 ◽  
Vol 35 (8_suppl) ◽  
pp. 157-157
Author(s):  
Lauren P. Wallner ◽  
Yun Li ◽  
Chandler McLeod ◽  
Ann S. Hamilton ◽  
Kevin C. Ward ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Cancer Treatment ◽  
Treatment Decision ◽  
Breast Cancer Treatment ◽  
Network Size ◽  
Support Networks ◽  
Newly Diagnosed ◽  
Treatment Decision Making

157 Background: Prior studies suggest that women often involve a network of family and friends in their cancer treatment decision making, yet very little is known about the size and characteristics of these decision support networks and whether their involvement leads to high-quality breast cancer treatment decisions. Methods: A weighted random sample of patients newly diagnosed with breast cancer in 2014-15 as reported to the Georgia and Los Angeles SEER registries were surveyed approximately 6 months after diagnosis (N = 2,502, 70% response rate). Network size was estimated by asking women to list up to 3 of the most important decision support people (DSP) who helped them make their locoregional therapy decisions. Decision deliberation was measured using 4-items assessing degree to which patients thought through the decision, with higher scores reflecting more deliberative breast cancer treatment decisions. We compared the size of the network (0-3+ people) across patient-level characteristics and estimated the adjusted mean deliberation scores across levels of network size using multivariable linear regression. Results: Of the 2,502 women included in this analysis, 51% reported having at least 3 DSPs, 20% reported 2, 18% reported 1, and 10% reported not having any DSPs. Among women who were not partnered (N = 961), 51% had 3 DSPs, 18% had 2, 16% had 1 and 16% had 0 DSPs. Of the DSPs that the respondents identified, the majority were children (30%), followed by partners/spouses (23%), friends (15%), siblings (10%), other family members (6%), and parents (5%). Married/partnered women (p < 0.001), those younger than 45 years old (p < 0.001), those with more than 1 comorbidity (p < 0.001), and black women (p = 0.02) were all more likely to report larger networks on average. Larger support networks were associated with more deliberative surgical decisions (p < 0.001). Conclusions: In this population-based sample, the majority of women engaged DSPs in their treatment decision making and for non-partnered patients, DSPs still played a key role in decision making. Larger size decision support networks were associated with higher quality decisions, underscoring the importance of efforts to identify and engage DSPs in the breast cancer decision making process.

scholarly journals Data-Driven Treatment Pathways Mining for Early Breast Cancer Using cSPADE Algorithm and System Clustering

2020 ◽  
Author(s):  
Qing Yang ◽  
Ting Luo ◽  
Wei Zhang ◽  
Xiaorong Zhong ◽  
Ping He ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Cancer Treatment ◽  
Early Breast Cancer ◽  
Breast Cancer Treatment ◽  
Breast Cancer Patients ◽  
Treatment Pathways ◽  
Kolmogorov Smirnov ◽  
System Clustering ◽  
Sequence Rules ◽  
Smirnov Test

Abstract Background: Due to the multidimensional, multilayered, and chronological order of the cancer data in this study, it was challenging for us to extract treatment paths. Therefore, it was necessary to design a new data mining scheme to effectively extract the treatment path of breast cancer. To determine whether the cSPADE algorithm and system clustering proposed in this study can effectively identify the treatment pathways for early breast cancer. Methods: We applied data mining technology to the electronic medical records of 6891 early breast cancer patients to mine treatment pathways. We provided a method of extracting data from EMR and performed three-stage mining: determining the treatment stage through the cSPADE algorithm → system clustering for treatment plan extraction → cSPADE mining sequence pattern for treatment. The Kolmogorov-Smirnov test and correlation analysis were used to cross-validate the sequence rules of early breast cancer treatment pathways.Results: We unearthed 55 sequence rules for early breast cancer treatment, 3 preoperative neoadjuvant chemotherapy regimens, 3 postoperative chemotherapy regimens, and 2 chemotherapy regimens for patients without surgery. Through 5-fold cross-validation, Pearson and Spearman correlation tests were performed. At the significance level of P <0.05, all correlation coefficients of support, confidence and lift were greater than 0.89. Using the Kolmogorov-Smirnov test, we found no significant differences between the sequence distributions.Conclusions: The cSPADE algorithm combined with system clustering can achieve hierarchical and vertical mining of breast cancer treatment models. By uncovering the treatment pathways of early breast cancer patients by this method, the real-world breast cancer treatment behavior model can be evaluated, and it can provide a reference for the redesign and optimization of the treatment pathways.

scholarly journals Understanding the engagement of key decision support persons in patient decision making around breast cancer treatment

Cancer ◽  
2019 ◽  
Vol 125 (10) ◽  
pp. 1709-1716 ◽  
Author(s):  
Christine M. Veenstra ◽  
Lauren P. Wallner ◽  
Paul H. Abrahamse ◽  
Nancy K. Janz ◽  
Steven J. Katz ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Decision Support ◽  
Cancer Treatment ◽  
Breast Cancer Treatment ◽  
Patient Decision Making ◽  
Patient Decision
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