Dataset Information

Can patients be trained to expect shared decision making in clinical consultations? Feasibility study of a public library program to raise patient awareness.

ABSTRACT: INTRODUCTION:Shared decision making (SDM) is a process whereby decisions are made together by patients and/or families and clinicians. Nevertheless, few patients are aware of its proven benefits. This study investigated the feasibility, acceptability and impact of an intervention to raise public awareness of SDM in public libraries. MATERIALS AND METHODS:A 1.5 hour interactive workshop to be presented in public libraries was co-designed with Quebec City public library network officials, a science communication specialist and physicians. A clinical topic of maximum reach was chosen: antibiotic overuse in treatment of acute respiratory tract infections. The workshop content was designed and a format, whereby a physician presents the information and the science communication specialist invites questions and participation, was devised. The event was advertised to the general public. An evaluation form was used to collect data on participants' sociodemographics, feasibility and acceptability components and assess a potential impact of the intervention. Facilitators held a post-workshop focus group to qualitatively assess feasibility, acceptability and impact. RESULTS:All 10 planned workshops were held. Out of 106 eligible public participants, 89 were included in the analysis. Most participants were women (77.6%), retired (46.1%) and over 45 (59.5%). Over 90% of participants considered the workshop content to be relevant, accessible, and clear. They reported substantial average knowledge gain about antibiotics (2.4, 95% Confidence Interval (CI): 2.0-2.8; P < .001) and about SDM (4.0, 95% CI: 3.4-4.5; P < .001). Self-reported knowledge gain about SDM was significantly higher than about antibiotics (4.0 versus 2.4; P < .001). Knowledge gain did not vary by sociodemographic characteristics. The focus group confirmed feasibility and suggested improvements. CONCLUSIONS:A public library intervention is feasible and effective way to increase public awareness of SDM and could be a new approach to implementing SDM by preparing potential patients to ask for it in the consulting room.

SUBMITTER: Adisso EL

PROVIDER: S-EPMC6291239 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Can patients be trained to expect shared decision making in clinical consultations? Feasibility study of a public library program to raise patient awareness.

Adisso Evehouenou Lionel EL Borde Valérie V Saint-Hilaire Marie-Ève MÈ Robitaille Hubert H Archambault Patrick P Blais Johanne J Cameron Cynthia C Cauchon Michel M Fleet Richard R Létourneau Jean-Simon JS Labrecque Michel M Quinty Julien J Samson Isabelle I Boucher Alexandrine A Zomahoun Hervé Tchala Vignon HTV Légaré France F

PloS one 20181212 12

<h4>Introduction</h4>Shared decision making (SDM) is a process whereby decisions are made together by patients and/or families and clinicians. Nevertheless, few patients are aware of its proven benefits. This study investigated the feasibility, acceptability and impact of an intervention to raise public awareness of SDM in public libraries.<h4>Materials and methods</h4>A 1.5 hour interactive workshop to be presented in public libraries was co-designed with Quebec City public library network offi ...[more]

PMID: 30540833

Similar Datasets

Project description:BackgroundShared decision-making (SDM) is widely recommended and required by the Centers for Medicare and Medicaid for patients considering lung cancer screening (LCS).ObjectiveWe examined clinicians' communication practices and perceived barriers of SDM for LCS at three medical centers with established screening programs.DesignMulticenter qualitative study of clinicians participating in LCS.ApproachWe performed semi-structured interviews, which were transcribed and analyzed using directed content analysis, guided by a theoretical model of patient-clinician communication.ParticipantsWe interviewed 24 clinicians including LCS coordinators (2), pulmonologists (3), and primary care providers (17), 4 of whom worked for the LCS program, a thoracic surgeon, and a radiologist.ResultsAll clinicians agreed with the goal of SDM, to ensure the screening decision was congruent with the patient's values. The depth and type of information presented by each clinician role varied considerably. LCS coordinators presented detailed information including numeric estimates of benefit and harm. Most PCPs explained the process more generally, focusing on logistics and the high rate of nodule detection. No clinician explicitly elicited values or communication preferences. Many PCPs tailored the conversation based on their implicit understanding of patients' values and preferences, gained from past experiences. PCPs reported that time, lack of detailed personal knowledge of LCS, and patient preferences were barriers to SDM. Many clinicians perceived that a significant proportion of patients were not interested in specific percentages and preferred to receive a clinician recommendation.ConclusionsOur results suggest that clinicians support the goal of SDM for LCS decisions but PCPs may not perform some of its elements. The lack of completion of some elements, such as PCPs' lack of in-depth information exchange, may reflect perceived patient preferences for communication. As LCS is implemented, further research is needed to support a personalized, patient-centered approach to produce better outcomes.

Project description: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.

Dataset Information

Can patients be trained to expect shared decision making in clinical consultations? Feasibility study of a public library program to raise patient awareness.

Publications

Can patients be trained to expect shared decision making in clinical consultations? Feasibility study of a public library program to raise patient awareness.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets