How to identify and treat data inconsistencies when eliciting health-state utility values for patient-centered decision making

Highlights：

• Health-state utilities are crucial in shared decision making (SDM), cost / utility analysis as they are at the core of patient-centered care.

• Such data are hard to be elicited objectively and may possess certain numerical inconsistences when they are elicited from patients.

• The proposed discrete optimization approaches offer the means to make initial utility values consistent by minimally adjusting them.

• Data monotonicity is a major property to be satisfied. Illustrative examples, based on real data, are used to demonstrate the proposed methods.

• The proposed approaches are novel and bridge a critical gap in patient-centered healthcare and offer many possibilities for new research.

摘要

•Health-state utilities are crucial in shared decision making (SDM), cost / utility analysis as they are at the core of patient-centered care.•Such data are hard to be elicited objectively and may possess certain numerical inconsistences when they are elicited from patients.•The proposed discrete optimization approaches offer the means to make initial utility values consistent by minimally adjusting them.•Data monotonicity is a major property to be satisfied. Illustrative examples, based on real data, are used to demonstrate the proposed methods.•The proposed approaches are novel and bridge a critical gap in patient-centered healthcare and offer many possibilities for new research.

论文关键词：Health-state utilities,Quality-Adjusted life years (QALYs),Shared decision making,Cost/utility analysis,Patient-Centered healthcare,Quadratic optimization

论文评审过程：Received 1 December 2018, Revised 28 February 2020, Accepted 12 May 2020, Available online 26 May 2020, Version of Record 5 June 2020.