Dataset Information

Variations in factors associated with healthcare providers' intention to engage in interprofessional shared decision making in home care: results of two cross-sectional surveys.

ABSTRACT: BACKGROUND:DOLCE (Improving Decision making On Location of Care with the frail Elderly and their caregivers) was a post-intervention clustered randomised trial (cRT) to assess the effect of training home care teams on interprofessional shared decision-making (IP-SDM). Alongside the cRT, we sought to monitor healthcare providers' level of behavioural intention to engage in an IP-SDM approach and to identify factors associated with this intention. METHODS:We conducted two cross-sectional surveys in the province of Quebec, Canada, one each at cRT entry and exit. Healthcare providers (e.g. nurses, occupational therapists and social workers) in the 16 participating intervention and control sites self-completed an identical paper-based questionnaire at entry and exit. Informed by the Integrated model for explaining healthcare professionals' clinical behaviour by Godin et al. (2008), we assessed their behavioural intention to engage in IP-SDM to support older adults and caregivers of older adults with cognitive impairment to make health-related housing decisions. We also assessed psychosocial variables underlying their behavioural intention and collected sociodemographic data. We used descriptive statistics and linear mixed models to account for clustering. RESULTS:Between 2014 and 2016, 271 healthcare providers participated at study entry and 171 at exit. At entry, median intention level was 6 in a range of 1 (low) to 7 (high) (Interquartile range (IQR): 5-6.5) and factors associated with intention were social influence (??=?0.27, P?

SUBMITTER: Adekpedjou R

PROVIDER: S-EPMC7069220 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Variations in factors associated with healthcare providers' intention to engage in interprofessional shared decision making in home care: results of two cross-sectional surveys.

Adekpedjou Rhéda R Haesebaert Julie J Stacey Dawn D Brière Nathalie N Freitas Adriana A Rivest Louis-Paul LP Légaré France F

BMC health services research 20200312 1

<h4>Background</h4>DOLCE (Improving Decision making On Location of Care with the frail Elderly and their caregivers) was a post-intervention clustered randomised trial (cRT) to assess the effect of training home care teams on interprofessional shared decision-making (IP-SDM). Alongside the cRT, we sought to monitor healthcare providers' level of behavioural intention to engage in an IP-SDM approach and to identify factors associated with this intention.<h4>Methods</h4>We conducted two cross-sect ...[more]

PMID: 32164669

Similar Datasets

Project description:Despite an increased awareness of shared decision-making (SDM) and its prominent position on the health policy agenda, its implementation in routine care remains a challenge in Germany. In order to overcome this challenge, it is important to understand healthcare providers' views regarding SDM and to take their perspectives and opinions into account in the development of an implementation program. The present study aimed at exploring a) the attitudes of different healthcare providers regarding SDM in oncology and b) their experiences with treatment decisions in daily practice.A qualitative study was conducted using focus groups and individual interviews with different healthcare providers at the University Cancer Center Hamburg, Germany. Focus groups and interviews were audio-recorded, transcribed and analyzed using conventional content analysis and descriptive statistics.N = 4 focus groups with a total of N = 25 participants and N = 17 individual interviews were conducted. Attitudes regarding SDM varied greatly between the different participants, especially concerning the definition of SDM, the attitude towards the degree of patient involvement in decision-making and assumptions about when SDM should take place. Experiences on how treatment decisions are currently made varied. Negative experiences included time and structural constraints, and a lack of (multidisciplinary) communication. Positive experiences comprised informed patients, involvement of relatives and a good physician-patient relationship.The results show that German healthcare providers in oncology have a range of attitudes that currently function as barriers towards the implementation of SDM. Also, their experiences on how decision-making is currently done reveal difficulties in actively involving patients in decision-making processes. It will be crucial to take these attitudes and experiences seriously and to subsequently disentangle existing misconceptions in future implementation programs.

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

Variations in factors associated with healthcare providers' intention to engage in interprofessional shared decision making in home care: results of two cross-sectional surveys.

Publications

Variations in factors associated with healthcare providers' intention to engage in interprofessional shared decision making in home care: results of two cross-sectional surveys.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure