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A composite measure for patient-reported outcomes in orthopedic care: design principles and validity checks
Lukas Schöner1, David Kuklinski2, Alexander Geissler2
1Department of Health Care Management, Technical University Berlin, Straße des 17. Juni 135, 10623, Berlin, Germany. Lukas.schoener@tu-berlin.de.
Background:
The complex, multidimensional nature of healthcare quality makes provider and treatment decisions based on quality difficult. Patient-reported outcome (PRO) measures can enhance patient centricity and involvement. The proliferation of PRO measures, however, requires a simplification to improve comprehensibility. Composite measures can simplify complex data without sacrificing the underlying information.
Objective And Methods:
We propose a five-step development approach to combine different PRO into one composite measure (PRO-CM): (i) theoretical framework and metric selection, (ii) initial data analysis, (iii) rescaling, (iv) weighting and aggregation, and (v) sensitivity and uncertainty analysis. We evaluate different rescaling, weighting, and aggregation methods by utilizing data of 3145 hip and 2605 knee replacement patients, to identify the most advantageous development approach for a PRO-CM that reflects quality variations from a patient perspective.
Results:
The comparison of different methods within steps (iii) and (iv) reveals the following methods as most advantageous: (iii) rescaling via z-score standardization and (iv) applying differential weights and additive aggregation. The resulting PRO-CM is most sensitive to variations in physical health. Changing weighting schemes impacts the PRO-CM most directly, while it proves more robust towards different rescaling and aggregation approaches.
Conclusion:
Combining multiple PRO provides a holistic picture of patients' health improvement. The PRO-CM can enhance patient understanding and simplify reporting and monitoring of PRO. However, the development methodology of a PRO-CM needs to be justified and transparent to ensure that it is comprehensible and replicable. This is essential to address the well-known problems associated with composites, such as misinterpretation and lack of trust.
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