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Impact of Timing on Measurement of Decision Quality and Shared Decision Making: Longitudinal Cohort Study of Breast Cancer Patients.

ABSTRACT: Purpose.The objective of this study was to examine whether scores of shared decision-making measures differ when collected shortly after (1 month) or long after (1 year) breast cancer surgical treatment decisions. Methods. Longitudinal, multisite survey of breast cancer (BC) patients, with measurements at 1 month and 1 year after surgery at 4 cancer centers. Patients completed the BC Surgery Decision Quality Instrument (used to generate a knowledge score, ratings of goals, and concordance with treatment preferences) and Shared Decision Making (SDM) Process survey at both time points. We tested several hypotheses related to the scores over time, including whether the scores discriminated between sites that did and did not offer formal decision support services. Exploratory analyses examined factors associated with large increases and decreases in scores over time. Results. Across the 4 sites, 229 patients completed both assessments. The mean total knowledge scores (69.2% [SD 16.6%] at 1 month and 69.4% [SD 17.7%] at 1 year, P = 0.86), SDM Process scores (2.7 [SD 1.1] 1 month v. 2.7 [SD 1.2] 1 year, P = 0.68), and the percentage of patients receiving their preferred treatment (92% at 1 month and 92% at 1 year, P = 1.0) were not significantly different over time. The site using formal decision support had significantly higher knowledge and SDM Process scores at 1 month, and only the SDM Process scores remained significantly higher at 1 year. A significant percentage of patients had large changes in their individual knowledge and SDM Process scores, with increases balancing out decreases. Conclusion. For population-level assessments, it is reasonable to survey BC patients up to a year after the decision, greatly increasing feasibility of measurement. For those evaluating decision support interventions, shorter follow-up is more likely to detect an impact on knowledge scores.

SUBMITTER: Sepucha KR

PROVIDER: S-EPMC7240785 | biostudies-literature | 2019 Aug

REPOSITORIES: biostudies-literature

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Impact of Timing on Measurement of Decision Quality and Shared Decision Making: Longitudinal Cohort Study of Breast Cancer Patients.

Sepucha Karen R KR Langford Aisha T AT Belkora Jeffrey K JK Chang Yuchiao Y Moy Beverly B Partridge Ann H AH Lee Clara N CN

Medical decision making : an international journal of the Society for Medical Decision Making 20190729 6

<b>Purpose.</b>The objective of this study was to examine whether scores of shared decision-making measures differ when collected shortly after (1 month) or long after (1 year) breast cancer surgical treatment decisions. <b>Methods.</b> Longitudinal, multisite survey of breast cancer (BC) patients, with measurements at 1 month and 1 year after surgery at 4 cancer centers. Patients completed the BC Surgery Decision Quality Instrument (used to generate a knowledge score, ratings of goals, and conc ...[more]

PMID: 31354095

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Project description:BACKGROUND:Patients with kidney failure experience a complex decision on dialysis modality performed either at home or in hospital. The options have different levels of impact on their physical and psychological condition and social life. The purpose of this study was to evaluate the implementation of an intervention designed to achieve shared decision-making for dialysis choice. Specific objectives were: 1) to measure decision quality as indicated by patients' knowledge, readiness and achieved preferences; and 2) to determine if patients experienced shared decision-making. METHOD:A mixed methods descriptive study was conducted using both questionnaires and semi-structured interviews. Eligible participants were adults with kidney failure considering dialysis modality. The intervention, based on the Three-Talk model, consisted of a patient decision aid and decision coaching meetings provided by trained dialysis coordinators. The intervention was delivered to 349 patients as part of their clinical pathway of care. After the intervention, 148 participants completed the Shared Decision-Making Questionnaire and the Decision Quality Measurement, and 29 participants were interviewed. Concordance between knowledge, decision and preference was calculated to measure decision quality. Interview transcripts were analysed qualitatively. RESULTS:The participants obtained a mean score for shared decision-making of 86 out of 100. There was no significant difference between those choosing home- or hospital-based treatment (97 versus 83; p?=?0.627). The participants obtained a knowledge score of 82% and a readiness score of 86%. Those choosing home-based treatment had higher knowledge score than those choosing hospital-based treatment (84% versus 75%; p?=?0.006) but no significant difference on the readiness score (87% versus 84%; p?=?0.908). Considering the chosen option and the knowledge score, 83% of the participants achieved a high-quality decision. No significant difference was found for decision quality between those choosing home- or hospital-based treatment (83% versus 83%; p?=?0.935). Interview data informed the interpretation of these results. CONCLUSIONS:Although there was no control group, over 80% of participants exposed to the intervention and responded to the surveys experienced shared decision-making and reached a high-quality decision. Both participants who chose home- and hospital-based treatment experienced the intervention as shared decision-making and made a high-quality decision. Qualitative findings supported the quantitative results. TRIAL REGISTRATION:The full trial protocol is available at ClinicalTrials. Gov ( NCT03868800 ). The study has been registered retrospectively.

Project description:ObjectivesWe aimed to construct a theoretical model and an observer-based measurement of shared decision making (SDM) in web-based pharmaceutical care consultation.MethodsWe first constructed a theoretical model through analysis of SDM models and interviews with 24 key informants. Then, the initial measurement was formulated, and the content was evaluated by 16 key informants alone. A cross-sectional study was executed, where 300 provider-patient encounters were collected at the online platform 'Good Doctor Website', for reliability and validity assessment. Content validity was evaluated by appropriateness score, that is the median of 7-9 and without disagreement. Interrater reliability was evaluated by Spearman correlation coefficient using 30 out of 300 samples. Single indicator validity was tested by Pearson correlation analysis, and convergent validity was evaluated by the Multiple Indicators Multiple Causes (MIMIC) model using 300 samples.ResultsTheoretical model constructed included four elements: 'comprehensive understanding of the patient's condition', 'exchanging ideas on medication options', 'achieving a medication plan', and 'providing decision support'. Measurement constructed included four dimensions and 19 items, and four dimensions were in accordance with four elements of the theoretical model. We found median scores of these dimensions and items were 7-9 and without disagreement, indicating good content validity. Spearman correlation coefficients of 19 items were greater than 0.750, suggesting good interrater reliability. Pearson coefficients between 19 items and their external variables were significant (p < 0.001), and MIMIC model results showed that the R 2 values of dimensions 1-4 were over 0.5, indicating good single indicator validity and convergent validity.ConclusionsCharacteristics of web-based pharmaceutical care consultation, highlighting the importance of medication history, medication regimen, and detailed medication plan, were observed in the theoretical model. Measurement constructed had good reliability and validity and may be used by health policymakers to improve the quality of pharmaceutical care consultation online.

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:BackgroundShared decision-making (SDM) is a collaborative process whereby patients and clinicians jointly deliberate on the best treatment option that takes into account patients' preferences and values. In breast cancer care, different treatment options have become available to patients in the last decade. Various interventions, including patient decision aids (PtDAs), have been designed to promote SDM in this disease area. This study aimed at investigating the factors that influence the successful adoption and implementation of SDM interventions in real-world healthcare delivery settings.MethodsA scoping review of scientific and grey literature was conducted for the period 2006-2021 to analyse the support for SDM interventions and their adoption in breast cancer clinical practice. The interpretation of findings was based on the Practical, Robust Implementation and Sustainability Model (PRISM) for integrating research findings into practice.ResultsOverall, 19 studies were included for data synthesis, with more than 70% published since 2017. The availability of SDM tools does not automatically translate into their actual use in clinical settings. Factors related to users' co-creation, the clinical team's attitude and knowledge, organisational support and regulatory provisions facilitate the adoption of SDM interventions. However, overlooking aspects such as the re-organisation of care pathways, patient characteristics, and assigning of resources (human, financial, and facilities) can hinder implementation efforts.ConclusionsCompared to the mounting evidence on the efficacy of SDM interventions, knowledge to support their sustained implementation in daily care is still limited, albeit results show an increasing interest in strategies that facilitate their uptake in breast cancer care over time. These findings highlight different strategies that can be used to embed SDM interventions in clinical practice. Future work should investigate which approaches are more effective in light of organisational conditions and external factors, including an evaluation of costs and healthcare system settings.

Dataset Information

Impact of Timing on Measurement of Decision Quality and Shared Decision Making: Longitudinal Cohort Study of Breast Cancer Patients.

Publications

Impact of Timing on Measurement of Decision Quality and Shared Decision Making: Longitudinal Cohort Study of Breast Cancer Patients.

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