Do patients benefit from participating in medical decision making? Longitudinal follow-up of women with breast cancer

2005 ◽  
Vol 15 (1) ◽  
pp. 9-19 ◽  
Author(s):  
Thomas F. Hack ◽  
Lesley F. Degner ◽  
Peter Watson ◽  
Luella Sinha
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Medical Decision Making ◽  
Medical Decision

The impact of “significant others” on breast cancer patients’ treatment decision making.

2012 ◽  
Vol 30 (34_suppl) ◽  
pp. 20-20
Author(s):  
Suepattra G. May ◽  
Katharine Rendle ◽  
Meghan Halley ◽  
Nicole Ventre ◽  
Allison W. Kurian ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Cancer Patients ◽  
Medical Decision Making ◽  
Treatment Decision ◽  
Significant Others ◽  
Medical Decision ◽  
Breast Cancer Patients ◽  
Treatment Decision Making ◽  
The Impact

20 Background: Shared medical decision making (SDM) has been lauded by advocates for its potential to democratize the patient-physician relationship. However, the practice of SDM is still conceived of as largely a dyadic moment that exists between the patient and the physician. Few studies have looked at the role of significant others (spouses, partners, family members and friends) in decision making or considered how discussions and actions outside the consultation room affect a patient’s medical decisions. This prospective study investigated the impact of significant others on the decision making deliberations of newly diagnosed breast cancer patients. Methods: Forty-one newly diagnosed breast cancer patients were interviewed at four critical time points throughout treatment to explore how they deliberated decisions with both care providers and significant others. Surveys assessing HRQOL, role preferences and treatment satisfaction along with EHR abstraction augmented interview data. Grounded theory analysis was used to identify recurrent themes in the qualitative data, and survey data were analyzed using IBM SPSS Statistics 20. Results: Emergent themes from our analysis identified several factors that patients consider when faced with cancer treatment decisions, including 1) presentation of treatment options 2) patient or significant other conflict/concordance with care team recommendations 3) perceived risk of recurrence and 4) short and long term impact of treatment on daily life. Participants stressed the need for clinicians to view patients beyond diagnosis and recognize their larger care network as influential factors in their decision making. Conclusions: Our interviews highlight how the current healthcare delivery structure rarely acknowledges the circles of care that can exert influence on decision making. Lack of attention to non-clinical others can lead to sub-optimal medical decision making because these influences are not adequately understood by clinicians. Findings from this study suggest the need to enhance clinicians’ and researchers’ understanding of the influence of others in patients’ treatment decision making, enabling them to intervene in these practices.

scholarly journals CANCER PROGNOSTIC EVALUATION VIA SUPPORT VECTOR MACHINES

2014 ◽  
pp. 29-34
Author(s):  
Domenico Conforti ◽  
Domenico Costanzo ◽  
Rosita Guido
Keyword(s):  
Decision Making ◽  
Medical Decision Making ◽  
Binary Classification ◽  
Classification Problem ◽  
Good Prognosis ◽  
Medical Decision ◽  
Support Vector ◽  
Breast Cancer Patients ◽  
Decision Making Problem

In this paper we considered a very challenging medical decision making problem: the short-term prognosis evaluation of breast cancer patients. In particular, the oncologist has to predict the more likely outcome of the disease in terms of survival or recurrence after a given follow-up period: “good” prognosis if the patient is still alive and has not recurrence after the follow-up period, “poor” prognosis if the patient has recurrence or dies within the follow-up period. This prediction can be realized on the basis of the execution of specific clinical tests and patient examinations. The relevant medical decision making problem has been formulated as a supervised binary classification problem. By the framework of generalized Support Vector Machine models, we tested and validate the behavior of four kernel based classifiers: Linear, Polynomial, Gaussian and Laplacian. The overall results demonstrate the effectiveness and robustness of the proposed approaches for solving the relevant medical decision making problem.

Patient-reported satisfaction with reconstructed breasts in the long-term survivorship period: Comparison of autologous and nonautologous breast reconstruction.

2012 ◽  
Vol 30 (15_suppl) ◽  
pp. 9043-9043 ◽  
Author(s):  
Amie Scott ◽  
Adelyn Ho ◽  
Anne Klassen ◽  
Stefan Cano ◽  
Nancy VanLaeken ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Decision Making ◽  
Patient Satisfaction ◽  
Breast Reconstruction ◽  
Medical Decision Making ◽  
Univariate Analysis ◽  
Medical Decision ◽  
Patient Centered ◽  
Patient Reported ◽  
Type Of Surgery

9043 Background: Breast cancer patients undergoing mastectomy may choose to have reconstruction performed using either their own tissue or an implant. As many patients are candidates for both, valid and reliable patient-centered outcomes data are crucial to shared medical decision-making. The objective of this study is to determine whether patient-reported satisfaction with their reconstructed breasts is dependent on type of reconstructive surgery and length of time from reconstruction. Methods: Participants were recruited from Memorial Sloan-Kettering Cancer, NY and the University of British Columbia, Canada. Patients completed the BREAST-Q, a new patient-reported outcome measure for breast surgery patients. The dependent variable was the BREAST-Q Satisfaction with Breast score, a 16-item scale scored from 0-100. Procedure type, time since surgery, and patient characteristics were independent variables. Univariate analysis and clinical judgment were used to identify variables included in the model, and multivariate linear regression models were constructed to control for confounders. Results: The study sample consisted of 510 women (response rate 62%). The sample was on average aged 54.3 ± 9.3 (range 21-81), surveyed 3.71 years ± 1.55 (range 1-9) after surgery, 66% were reconstructed using an implant. Type of surgery and laterality were found to be variables that predicted higher patient satisfaction with their breasts after controlling for radiation therapy, follow-up time, timing of surgery, age, body mass index, and major complications (surgery type p<0.001; laterality p<0.001, R-square=0.17). Conclusions: As there is a growing population of breast cancer survivors, understanding how a woman’s satisfaction with her reconstructed breasts changes over time is essential. This study suggests that patient satisfaction with breast reconstruction depends on the type of reconstruction a woman undergoes. This patient-centered outcome data can be used to enhance shared medical decision-making by providing patients with information about realistic expectations for satisfaction with breasts related to type of surgery chosen.

Sign in / Sign up

Export Citation Format

Share Document