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Improvement of shared decision making in integrated stroke care: a before and after evaluation using a questionnaire survey.

ABSTRACT: BACKGROUND:Shared decision making (SDM) is at the core of policy measures for making healthcare person-centred. However, the context-sensitive nature of the challenges in integrated stroke care calls for research to facilitate its implementation. This before and after evaluation study identifies factors for implementation and concludes with key recommendations for adoption. METHODS:Data were collected at the start and end of an implementation programme in five stroke services (December 2017 to July 2018). The SDM implementation programme consisted of training for healthcare professionals (HCPs), tailored support, development of decision aids and a social map of local stroke care. Participating HCPs were included in the evaluation study: A questionnaire was sent to 25 HCPs at baseline, followed by 11 in-depth interviews. Data analysis was based on theoretical models for implementation and 51 statements were formulated as a result. Finally, all HCPs were asked to validate and to quantify these statements and to formulate recommendations for further adoption. RESULTS:The majority of respondents said that training of all HCPs is essential. Feedback on consultation and peer observation are considered to help improve performance. In addition, HCPs stated that SDM should also be embedded in multidisciplinary meetings, whereas implementation in the organisation could be facilitated by appointed ambassadors. Time was not seen as an inhibiting factor. According to HCPs, negotiating patients' treatment decisions improves adherence to therapy. Despite possible cognitive or communications issues, all are convinced patients with stroke can be involved in a SDM-process. Relatives play an important role too in the further adoption of SDM. HCPs provided eight recommendations for adoption of SDM in integrated stroke care. CONCLUSIONS:HCPs in our study indicated it is feasible to implement SDM in integrated stroke care and several well-known implementation activities could improve SDM in stroke care. Special attention should be given to the following activities: (1) the appointment of knowledge brokers, (2) agreements between HCPs on roles and responsibilities for specific decision points in the integrated stroke care chain and (3) the timely investigation of patient's preferences in the care process - preferably before starting treatment through discussions in a multidisciplinary meeting.

SUBMITTER: Voogdt-Pruis HR

PROVIDER: S-EPMC6896582 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Improvement of shared decision making in integrated stroke care: a before and after evaluation using a questionnaire survey.

Voogdt-Pruis H R HR Ras T T van der Dussen L L Benjaminsen S S Goossens P H PH Raats I I Boss G G van Hoef E F M EFM Lindhout M M Tjon-A-Tsien M R S MRS Vrijhoef H J M HJM

BMC health services research 20191205 1

<h4>Background</h4>Shared decision making (SDM) is at the core of policy measures for making healthcare person-centred. However, the context-sensitive nature of the challenges in integrated stroke care calls for research to facilitate its implementation. This before and after evaluation study identifies factors for implementation and concludes with key recommendations for adoption.<h4>Methods</h4>Data were collected at the start and end of an implementation programme in five stroke services (Dec ...[more]

PMID: 31805927

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Project description:PURPOSE:The SDM-Q-9 and SDM-Q-Doc measure patient and physician perception of the extent of shared decision making (SDM) during a physician-patient consultation. So far, no self-report instrument for SDM was available in Dutch, and validation of the scales in other languages has been limited. The aim of this study was to translate both scales into Dutch and assess their psychometric characteristics. METHODS:Participants were patients and their treating physicians (general practitioners and medical specialists). Patients (N = 182) rated their consultation using the SDM-Q-9, 43 physicians rated their consultations using the SDM-Q-Doc (N = 201). Acceptability, reliability (internal consistency), and the factorial structure of the instruments were determined. For convergent validity the CPSpost was used. RESULTS:Reliabilities of both scales were high (alpha SDM-Q-9 0.88; SDM-Q-Doc 0.87). The SDM-Q-9 and SDM-Q-Doc total scores correlated as expected with the CPSpost (SDM-Q-9: r = 0.29; SDM-Q-Doc: r = 0.48) and were significantly different between the CPSpost categories, with lowest mean scores when the physician made the decision alone. Principal Component Analyses showed a two-component model for each scale. A confirmatory factor analysis yielded a mediocre, but acceptable, one-factor model, if Item 1 was excluded; for both scales the best indices of fit were obtained for a one-factor solution, if both Items 1 and 9 were excluded. CONCLUSION:The Dutch SDM-Q-9 and SDM-Q-Doc demonstrate good acceptance and reliability; they correlated as expected with the CPSpost and are suitable for use in Dutch primary and specialised care. Although the best model fit was found when excluding Items 1 and 9, we believe these items address important aspects of SDM. Therefore, also based on the coherence with theory and comparability with other studies, we suggest keeping all nine items of the scale. Further research on the SDM-concept in patients and physicians, in different clinical settings and different countries, is necessary to gain a better understanding of the SDM-construct and its measurement.

Project description:BackgroundGoal attainment of guideline-recommended low-density lipoprotein cholesterol (LDL-C) is suboptimal. Little is known about how patient factors influence physicians' treatment decision-making in hypercholesterolemia. We examined physicians' treatment recommendations in high-risk patients whose LDL-C remained uncontrolled despite statin monotherapy.MethodsPhysicians completed a questionnaire prior to randomization into period I of a two-period randomized controlled trial evaluating LDL-C goal attainment in patients whose LDL-C remained ≥100 mg/dL after 5 weeks' treatment with atorvastatin 10 mg/day (NCT01154036). Physicians' treatment recommendations were surveyed for two hypothetical and one real scenario: (1) LDL-C presumed near goal (between 100-105 mg/dL), (2) LDL-C presumed far from goal (~120 mg/dL), and (3) observed baseline LDL-C of enrolled patients. Prognostic factors considered during decision-making were identified by regression analysis. Observed lipid outcomes at the end of period I (following 6 weeks' treatment with ezetimibe 10 mg plus atorvastatin 10 mg, atorvastatin 20 mg, or rosuvastatin 10 mg) were compared with estimated LDL-C outcomes for physicians' treatment recommendations after 6 weeks (based on individual patients' pre-randomization LDL-C and expected incremental change).ResultsQuestionnaires were completed for 1,534 patients. No change in therapy, or double atorvastatin dose, were frequently recommended, even when LDL-C was far from goal (6.5% and 52.2% of patients, respectively). Double atorvastatin dose was commonly recommended in all scenarios (43-52% of patients). More intensive LDL-C-lowering regimens were recommended infrequently e.g. double atorvastatin dose and add ezetimibe only <12% in all scenarios. Overall, cardiovascular risk factors and desire to achieve a more aggressive LDL-C goal were prominent factors in decision-making for treatment. Comparison of observed and estimated LDL-C levels showed that physicians tended to overestimate the effectiveness of their recommendations.ConclusionsThis study provides insight into physicians' perspectives on clinical management of hypercholesterolemia and highlights a gap in knowledge translation from guidelines to clinical practice. The need for lower LDL-C and cardiovascular risk were key drivers in clinical decision-making, but physicians' treatment choices were more conservative than guideline recommendations, potentially resulting in poorer LDL-C reduction. When compared with actual outcomes, projected LDL-C control was better if physicians used more comprehensive strategies rather than simply doubling the statin dose.Trial registrationClinicaltrials.gov: NCT01154036.

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.

Project description:BackgroundDespite health policy that promotes shared decision-making, it is not yet the norm in clinical practice. We aimed to assess how much shared decision-making Canadians experienced in health-related decisions in 2017.MethodsWe conducted a cross-sectional online survey in January 2018 with a Web-based panel of Canadians representing all 10 provinces. We assessed their involvement in health-related decisions made with a health care professional over the previous year by asking about 1) discussion of choice of treatment or care plan, 2) presentation of advantages and disadvantages, 3) exploration of ideas and preferences, 4) discussion of preferred option and 5) match between preferred and actual level of participation. We computed an average shared decision-making score (range 1 [never] to 5 [always]). We presented characteristics of participants and responses using descriptive statistics and explored variations across sociodemographic factors, jurisdictions, geographical areas and care settings (home care or not) using multivariate weighted regressions.ResultsOf the 1591 participants surveyed, 1010 (63.5%) reported receiving health care in the previous 12 months. The mean of the average shared decision-making score was 2.25/5 (standard deviation [SD] 1.16). After weighting, 42.8% of respondents reported that their health care professional often or always mentioned that they had a choice of treatment or care plan, 45.4% reported that advantages and disadvantages were often or always presented, 38.8% reported that they were often or always asked for their ideas or preferences, 40.2% reported that they were often or always asked about their preferred option, and 54.1% stated that their level of participation in decision-making often or always matched their preferred level of participation. Increasing age, rural setting, living in the province of Quebec and not being white significantly decreased the level of shared decision-making experienced. Older respondents (age ≥ 65 yr) receiving home care reported the least shared decision-making (mean score 1.7 [SD 0.5]).InterpretationCanadians in all 10 provinces experienced a low degree of shared decision-making in 2017, with variations across sociodemographic factors, jurisdictions, care settings and geographical areas. Further efforts to foster implementation of shared decision-making are needed and should take these variations into account.

Dataset Information

Improvement of shared decision making in integrated stroke care: a before and after evaluation using a questionnaire survey.

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Improvement of shared decision making in integrated stroke care: a before and after evaluation using a questionnaire survey.

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