Veilleux, S. et al. (2016). Factors Leading to Acceptance of and Willingness to Pay for Predictive Testing Among Chronically Ill Patients. Journal of Academy of Business and Economics, 16(4), 35-46.
Personalized medicine can lower healthcare system costs and help ensure that chronic patients get the most appropriate treatment for their individual illness. However, individual factors leading to acceptance of personalized medicine technologies such as predictive testing by chronic patients remain largely unknown. Objectives: This study was aimed at identifying individual sociodemographic factors leading to the acceptance of predictive testing in chronic patients and their willingness to pay for these tests. Design and methods: A web survey was conducted with 210 Canadian patients affected by Inflammatory Bowel Disease (IBD). The data was processed using the SPSS software. Inferential statistical analyses were conducted using the chi square test. Results: Chronic patients were massively in favour of undergoing a genetic test that could predict response to treatment options, and the majority were in favour of paying for the test. While the population in general has concerns regarding genetic testing, the present study indicates that the seriousness of chronic illness along with side effects of treatments leads to a higher acceptance among patients. Only the yearly number of consultations was positively related to acceptance and willingness to pay. Conclusions: While previous studies have reported resistance to genomic technologies amongst the general population, the present study demonstrates that chronic patients are largely open to genetic predictive testing. Furthermore, a vast majority was even prepared to pay for such tests. Clinicians, healthcare organizations and pharmaceutical companies should take this result into consideration when building and promoting predictive testing options. Based on these findings, future studies are warranted for further investigating and characterizing patients’ perception towards genetic predictive testing across other chronic illnesses.
This content has been updated on 1 November 2019 at 14 h 45 min.