The relationship between perceptions of shared decision‐making and patient‐reported outcomes in a cross‐sectional cohort of hemorrhoidectomy patients

2022 ◽  
Author(s):  
Khalil Merali ◽  
Ahmer Karimuddin ◽  
Trafford Crump ◽  
Carl Brown ◽  
Terry Phang ◽  
...  
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Shared Decision ◽  
Cross Sectional ◽  
Patient Reported ◽  
The Relationship

Describing the Spectrum of Patient Reported Outcomes after Radical Prostatectomy: Providing Information to Improve Patient Counseling and Shared Decision Making

2019 ◽  
Vol 201 (4) ◽  
pp. 751-758 ◽  
Author(s):  
Christopher Martin ◽  
Benjamin Haaland ◽  
Alexander E. Tward ◽  
Christopher Dechet ◽  
Will Lowrance ◽  
...  
Keyword(s):  
Decision Making ◽  
Radical Prostatectomy ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Shared Decision ◽  
Patient Counseling ◽  
Patient Reported ◽  
Improve Patient

scholarly journals The Association Between Patients' eHealth Literacy and Satisfaction With Shared Decision-making and Well-being: Multicenter Cross-sectional Study (Preprint)

2020 ◽  
Author(s):  
Richard Huan Xu ◽  
Ling-Ming Zhou ◽  
Eliza Lai-Yi Wong ◽  
Dong Wang
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Regression Models ◽  
Well Being ◽  
Shared Decision ◽  
Background Characteristics ◽  
Cross Sectional ◽  
Ehealth Literacy ◽  
High Level ◽  
The Relationship

BACKGROUND Although previous studies have shown that a high level of health literacy can improve patients’ ability to engage in health-related shared decision-making (SDM) and improve their quality of life, few studies have investigated the role of eHealth literacy in improving patient satisfaction with SDM (SSDM) and well-being. OBJECTIVE This study aims to assess the relationship between patients’ eHealth literacy and their socioeconomic determinants and to investigate the association between patients’ eHealth literacy and their SSDM and well-being. METHODS The data used in this study were obtained from a multicenter cross-sectional survey in China. The eHealth Literacy Scale (eHEALS) and Investigating Choice Experiments Capability Measure for Adults were used to measure patients’ eHealth literacy and capability well-being, respectively. The SSDM was assessed by using a self-administered questionnaire. The Kruskal-Wallis one-way analysis of variance and Wilcoxon signed-rank test were used to compare the differences in the eHEALS, SSDM, and Investigating Choice Experiments Capability Measure for Adults scores of patients with varying background characteristics. Ordinary least square regression models were used to assess the relationship among eHealth literacy, SSDM, and well-being adjusted by patients’ background characteristics. RESULTS A total of 569 patients completed the questionnaire. Patients who were male, were highly educated, were childless, were fully employed, were without chronic conditions, and indicated no depressive disorder reported a higher mean score on the eHEALS. Younger patients (SSDM<sub>≥61 years</sub>=88.6 vs SSDM<sub>16-30 years</sub>=84.2) tended to show higher SSDM. Patients who were rural residents and were well paid were more likely to report good capability well-being. Patients who had a higher SSDM and better capability well-being reported a significantly higher level of eHealth literacy than those who had lower SSDM and poorer capability well-being. The regression models showed a positive relationship between eHealth literacy and both SSDM (<i>β</i>=.22; <i>P</i>&lt;.001) and well-being (<i>β</i>=.26; <i>P</i>&lt;.001) after adjusting for patients’ demographic, socioeconomic status, lifestyle, and health status variables. CONCLUSIONS This study showed that patients with a high level of eHealth literacy are more likely to experience optimal SDM and improved capability well-being. However, patients’ depressive status may alter the relationship between eHealth literacy and SSDM. CLINICALTRIAL

scholarly journals Shared Decision Making: From Decision Science to Data Science

2020 ◽  
Vol 40 (3) ◽  
pp. 254-265
Author(s):  
Azza Shaoibi ◽  
Brian Neelon ◽  
Leslie A. Lenert
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Data Science ◽  
Treatment Effects ◽  
Predictive Analytics ◽  
Simulated Data ◽  
Treatment Recommendations ◽  
Shared Decision ◽  
Patient Reported

Background. Accurate diagnosis of patients’ preferences is central to shared decision making. Missing from clinical practice is an approach that links pretreatment preferences and patient-reported outcomes. Objective. We propose a Bayesian collaborative filtering (CF) algorithm that combines pretreatment preferences and patient-reported outcomes to provide treatment recommendations. Design. We present the methodological details of a Bayesian CF algorithm designed to accomplish 3 tasks: 1) eliciting patient preferences using conjoint analysis surveys, 2) clustering patients into preference phenotypes, and 3) making treatment recommendations based on the posttreatment satisfaction of like-minded patients. We conduct a series of simulation studies to test the algorithm and to compare it to a 2-stage approach. Results. The Bayesian CF algorithm and 2-stage approaches performed similarly when there was extensive overlap between preference phenotypes. When the treatment was moderately associated with satisfaction, both methods made accurate recommendations. The kappa estimates measuring agreement between the true and predicted recommendations were 0.70 (95% confidence interval = 0.052–0.88) and 0.73 (0.56–0.90) under the Bayesian CF and 2-stage approaches, respectively. The 2-stage approach failed to converge in settings in which clusters were well separated, whereas the Bayesian CF algorithm produced acceptable results, with kappas of 0.73 (0.56–0.90) and 0.83 (0.69–0.97) for scenarios with moderate and large treatment effects, respectively. Limitations. Our approach assumes that the patient population is composed of distinct preference phenotypes, there is association between treatment and outcomes, and treatment effects vary across phenotypes. Findings are also limited to simulated data. Conclusion. The Bayesian CF algorithm is feasible, provides accurate cluster treatment recommendations, and outperforms 2-stage estimation when clusters are well separated. As such, the approach serves as a roadmap for incorporating predictive analytics into shared decision making.

512-P: Desire for and Experience of Shared Decision-Making and Associations with Patient-Reported Outcomes in Those with Diabetes

Diabetes ◽  
2021 ◽  
Vol 70 (Supplement 1) ◽  
pp. 512-P
Author(s):  
EMILY SYVERUD ◽  
SARAH T. MANSER ◽  
STEVEN ARRIAZA ◽  
ELIZABETH A. ROGERS
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Shared Decision ◽  
Patient Reported

scholarly journals Patient-reported outcomes in RA care improve patient communication, decision-making, satisfaction and confidence: qualitative results

Rheumatology ◽  
2019 ◽  
Vol 59 (7) ◽  
pp. 1662-1670 ◽  
Author(s):  
Susan J Bartlett ◽  
Elaine De Leon ◽  
Ana-Maria Orbai ◽  
Uzma J Haque ◽  
Rebecca L Manno ◽  
...  
Keyword(s):  
Decision Making ◽  
Focus Groups ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Medical Decision ◽  
Measurement Information ◽  
Shared Decision ◽  
Clinic Staff ◽  
Patient Reported ◽  
The Impact

Abstract Objective To evaluate the impact of integrating patient-reported outcomes (PROs) into routine clinics, from the perspective of patients with RA, clinicians and other staff. Methods We conducted a prospective cohort study using a mixed methods sequential explanatory design at an academic arthritis clinic. RA patients completed selected Patient-Reported Outcomes Measurement Information System measures on tablets in the waiting room. Results were immediately available to discuss during the visit. Post-visit surveys with patients and physicians evaluated topics discussed and their impact on decision making; patients rated confidence in treatment. Focus groups or interviews with patients, treating rheumatologists and clinic staff were conducted to understand perspectives and experiences. Results Some 196 patients and 20 rheumatologists completed post-visit surveys at 816 and 806 visits, respectively. Focus groups were conducted with 24 patients, 10 rheumatologists and 4 research/clinic staff. PROs influenced medical decision-making and RA treatment changes (38 and 18% of visits, respectively). Patients reported very high satisfaction and treatment confidence. Impact on clinical workflow was minimal after a period of initial adjustment. PROs were valued by patients and physicians, and provided new insight into how patients felt and functioned over time. Reviewing results together improved communication, and facilitated patient-centred care, shared decision making, and the identification of new symptoms and contributing psychosocial/behavioural factors. Conclusion PRO use at RA visits was feasible, increased understanding of how disease affects how patients feel and function, facilitated shared decision-making, and was associated with high patient satisfaction and treatment confidence.

scholarly journals Is Shared Decision-making Associated with Better Patient-reported Outcomes? A Longitudinal Study of Patients Undergoing Total Joint Arthroplasty

2021 ◽  
Vol Publish Ahead of Print ◽  
Author(s):  
Ella A. Chrenka ◽  
Leif I. Solberg ◽  
Stephen E. Asche ◽  
Steven P. Dehmer ◽  
Jeanette Y. Ziegenfuss ◽  
...  
Keyword(s):  
Decision Making ◽  
Longitudinal Study ◽  
Shared Decision Making ◽  
Total Joint Arthroplasty ◽  
Patient Reported Outcomes ◽  
Joint Arthroplasty ◽  
Shared Decision ◽  
Patient Reported
Sign in / Sign up

Export Citation Format

Share Document