Objective: To identify groups of potential users based on their preferences for characteristics of personal health records (PHRs) and to estimate potential PHR uptake.
Methods: We performed a discrete choice experiment, which consisted of 12 choice scenarios, each comprising 2 hypothetical PHR alternatives and an opt-out. The alternatives differed based on 5 characteristics. The survey was administered to Internet panel members of the Dutch Federation of Patients and Consumer Organizations. We used latent class models to analyze the data.
Results: A total of 1,443 potential PHR users completed the discrete choice experiment. We identified 3 latent classes: "refusers" (class probability 43%), "eager adopters" (37%), and "reluctant adopters" (20%). The predicted uptake for the reluctant adopters ranged from 4% in the case of a PHR with the worst attribute levels to 68% in the best case. Those with 1 or more chronic diseases were significantly more likely to belong to the eager adopter class. The data storage provider was the most decisive aspect for the eager and reluctant adopters, while cost was most decisive for the refusers. Across all classes, health care providers and independent organizations were the most preferred data storage providers.
Conclusion: We identified 3 groups, of which 1 group (more than one-third of potential PHR users) indicated great interest in a PHR irrespective of PHR characteristics. Policymakers who aim to expand the use of PHRs will be most successful when health care providers and health facilities or independent organizations store PHR data while refraining from including market parties.

, ,
doi.org/10.1093/jamia/ocw158, hdl.handle.net/1765/100112
American Medical Informatics Association. Journal
Erasmus MC: University Medical Center Rotterdam

Determann, D., Lambooij, M., Gyrd-Hansen, D., de Bekker-Grob, E., Steyerberg, E., Heldoorn, M., … de Wit, A. (2017). Personal health records in the Netherlands: Potential user preferences quantified by a discrete choice experiment. American Medical Informatics Association. Journal, 24(3), 529–536. doi:10.1093/jamia/ocw158