Abstract
BACKGROUND: Person-centered care (PCC) requires knowledge about patient preferences. Among people living with cognitive impairments (PlwCI), evidence on quantitative, choice-based preferences, which allow to quantify, weigh, and rank care elements, is limited. Furthermore, data on the congruence of patient preferences with physicians' judgements for PCC are missing. Such information is expected to support the implementation of PCC; state-of-the-art medical care aligned with patients' preferences. OBJECTIVE: To elicit patient preferences and physicians' judgements for PCC and their congruence. METHODS: Data from the mixed-methods PreDemCare study, including a cross-sectional, paper-and-pencil, interviewer-assisted analytic hierarchy process (AHP) survey conducted with n = 50 community-dwelling PlwCI and n = 25 physicians. Individual AHP weights (preferences/judgements) were calculated with the principal eigenvector method and aggregated per group by aggregation of individual priorities mode. Individual consistency ratios (CRs) were calculated and aggregated per group. Group differences in preferences/judgements were investigated descriptively by means and standard deviations (SDs) of AHP weights, resulting ranks, and boxplots. Additionally, differences between groups were investigated with independent paired t-test/Mann Whitney U-test. Sensitivity of AHP results was tested by inclusion/exclusion of inconsistent respondents, with an accepted threshold at CR≤0.3 for patients, and CR≤0.2 for physicians, due to better cognitive fitness of the latter group. RESULTS: Patient preferences and physicians' judgements did not differ significantly, except for the criterion Memory Exercises (AHP weights (mean (SD)): 0.135 (0.066) versus 0.099 (0.068), p = 0.01). We did not see rank-reversals of criteria after exclusion of inconsistent participants. Mean CR for patients at the criteria level was 0.261, and 0.181 for physicians. CONCLUSION: Physicians' judgements in our setting aligned well with patients' preferences. Our findings may be used to guide the implementation of preference-based PCC.