Dataset Information

Measuring Patient-Reported Shared Decision-Making to Promote Performance Transparency and Value-Based Payment: Assessment of collaboRATE's Group-Level Reliability.

ABSTRACT: Shared decision-making (SDM) between clinicians and patients is a key component of patient experience, but measurement efforts have been hampered by a lack of valid and reliable measures that are feasible for routine use. In this study, we aim to investigate collaboRATE's reliability, calculate required sample sizes for reliable measurement, and compare Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient experience survey items to collaboRATE. CollaboRATE's provider group-level reliability reached acceptable reliability at 190 patient reports, while the CAHPS SDM measure demonstrated similar reliability at a sample size of 124. The CAHPS communication measure reached acceptable reliability with 55 patient reports. A strong correlation was observed between collaboRATE and CAHPS communication measures (r = 0.83). As a reliable measure of SDM, collaboRATE may be useful for both building payment models that support shared clinical decision-making and encouraging data transparency with regard to provider group performance.

SUBMITTER: Forcino RC

PROVIDER: S-EPMC7705832 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Publications

Measuring Patient-Reported Shared Decision-Making to Promote Performance Transparency and Value-Based Payment: Assessment of collaboRATE's Group-Level Reliability.

Forcino Rachel C RC Thygeson Marcus M O'Malley A James AJ Meinders Marjan J MJ Westert Gert P GP Elwyn Glyn G

Journal of patient experience 20191103 5

Shared decision-making (SDM) between clinicians and patients is a key component of patient experience, but measurement efforts have been hampered by a lack of valid and reliable measures that are feasible for routine use. In this study, we aim to investigate collaboRATE's reliability, calculate required sample sizes for reliable measurement, and compare Consumer Assessment of Healthcare Providers and Systems (CAHPS) patient experience survey items to collaboRATE. CollaboRATE ...[more]

PMID: 33294610

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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

Measuring Patient-Reported Shared Decision-Making to Promote Performance Transparency and Value-Based Payment: Assessment of collaboRATE's Group-Level Reliability.

Publications

Measuring Patient-Reported Shared Decision-Making to Promote Performance Transparency and Value-Based Payment: Assessment of collaboRATE's Group-Level Reliability.

Similar Datasets

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