scholarly journals Educational interventions for shared decision making and the role of patient agency:  A Systematic Review

F1000Research ◽  
2021 ◽  
Vol 10 ◽  
pp. 753
Author(s):  
Catherine Witkop ◽  
Dario Torre ◽  
Emily Harvey ◽  
Lauren Maggio
Keyword(s):  
Decision Making ◽  
Shared Decision Making ◽  
Patient Reported Outcomes ◽  
Role Play ◽  
Educational Interventions ◽  
Simulated Patients ◽  
Shared Decision ◽  
Relational Agency ◽  
Patient Reported

Background:  Shared decision making is critical to patient-centered care and yet there is limited consensus on effective teaching approaches for training physicians in this domain. As a collaborative process in which the patient and physician co-create a decision, patient and relational agency may be important contributors and studies with patient-reported outcomes may identify successful approaches and determine gaps in pedagogy.  The authors conducted a systematic review of educational interventions for shared decision making, focusing on patient-reported outcomes and consideration of agency.  Methods:  Ovid MEDLINE, Embase, and Web of Science were searched for studies describing educational interventions with patient-level outcomes published between January 2000 and January 2020. Articles were excluded if they were not in English, included only patient interventions, or reported only physician outcomes.  Quality assessment was performed using the Medical Education Research Study Quality Instrument (MERSQI).  Data about the educational methods used were extracted and included studies were assessed for quality. Thematic analysis was performed to identify the potential role of agency. Results: 26 articles were identified describing 17 unique studies.  Educational interventions were diverse in duration and content, with multiple components.  Three-quarters of studies used role play or simulated patients and 82% included tools to facilitate shared decisions.  Although no articles explicitly discussed facilitating agency as a component of the intervention or as an outcome, one qualitative study demonstrated themes of patient and relational agency. Conclusions:  Educational interventions included small group discussion, decision aids, role play, and simulated patients, and improved a range of patient outcomes, but our study included only studies including practicing physicians, limiting applicability to trainees and other health care providers. Interventions have not included explicit instructional design around agency, but qualitative analyses demonstrated interventions may facilitate agency and shared decision making. Future instructional strategies should consider the complexity inherent in co-constructing decisions.

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.

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