Purpose: Choosing the best treatment is challenging when there is more than one reasonable option and each option has good and bad attributes that people may value differently. Our objective was to develop a practical approach to integrate patient preferences with clinical evidence in order to help patients more easily identify treatments most consistent with their preferences
Method: We developed a prototype that uses a vector space model to combine quantitative evidence about the impact of different treatment options with patient preferences. The evidence matrix defined by Pm-n describes the impact of each treatment T1-n on each attribute A1-m affected by these treatments. For each pairwise combination of T within each A, weights are assigned to each T in proportion to the difference (D) between the 2 treatments’ impact on each domain (Dt1t2). The preference attributes of greatest importance to elicit from patients are selected empirically, based on Dt1t2, and are framed consistently across attributes. Visual analog scales (ranging from 0 to 1) elicit patient preferences for each selected A, which are then normalized to create a unique preference vector. Treatments are rank ordered by multiplying the evidence matrix by the patient preference matrix. The evidence matrix can be easily updated to reflect new data, regional data, group-specific data, or different time horizons. Patient preferences can be obtained iteratively for additional attributes, as needed, to help distinguish among treatments.
Result: We created an algorithm that integrates evidence about the impact of treatments for low risk prostate cancer with individual patient preferences. Three treatments (active surveillance, radical prostatectomy, and radiation treatment) and four attributes (surviving prostate cancer, incontinence, impotence, and rectal problems) are considered as a test case. Using data from a 2011 AHRQ Evidence Report, the most important attributes to query patients about their preferences are impotence (1st), rectal problems (2nd), and incontinence (3rd). If patients only valued survival, the preferred treatment is radiation therapy; if patients equally valued all four attributes, the preferred treatment is surveillance. The model is sensitive to small changes in preferences.
Conclusion: This new approach to combining individual preferences with evidence minimizes both patient burden and bias on the part of the decision support tool designer, and is generalizable to other preference-sensitive decisions.