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. 2018 Oct 23;3(4):e0017.
doi: 10.2106/JBJS.OA.18.00017. eCollection 2018 Dec 20.

Characterizing Patient Preferences Surrounding Total Knee Arthroplasty

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Free PMC article

Characterizing Patient Preferences Surrounding Total Knee Arthroplasty

John M Reuter et al. JB JS Open Access. .
Free PMC article

Abstract

Background: Episode-based bundled payments for total knee arthroplasty emphasize cost-effective patient-centered care. Understanding patients' perceptions of components of the total knee arthroplasty care episode is critical to achieving this care. This study investigated patient preferences for components of the total knee arthroplasty care episode.

Methods: Best-worst scaling was used to analyze patient preferences for components of the total knee arthroplasty care episode. Participants were selected from patients presenting to 2 orthopaedic clinics with chronic knee pain. They were presented with descriptions of 17 attributes before completing a best-worst scaling exercise. Attribute importance was determined using hierarchical Bayesian estimation. Latent class analysis was used to evaluate varying preference profiles.

Results: One hundred and seventy-four patients completed the survey, and 117 patients (67%) were female. The mean age was 62.71 years. Participants placed the highest value on surgeon factors, including level of experience, satisfaction rating, and complication rates. Latent class analysis provided a 4-segment model of the population.

Conclusions: This study demonstrated differences in patient preferences for the components of a total knee arthroplasty care episode and characterized distinct preference profiles among patient subsets. Stakeholders can use this information to focus efforts and policy on high-value components and to potentially create customized bundles guided by preference profiles.

Clinical relevance: This study is clinically relevant because the patient preferences identified here may help providers to design customized bundles for total knee arthroplasty care.

Figures

Fig. 1
Fig. 1
Best-worst scaling survey introduction.
Fig. 2
Fig. 2
Example of a best-worst scaling choice task.
Fig. 3
Fig. 3
Whole-sample importances. The importance value is written with the standard deviation in parentheses.
Fig. 4
Fig. 4
Attribute importance values for the whole-sample and segment populations.

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