Abstract
AbstractFor many vaccine-preventable diseases like influenza, vaccination rates are lower than optimal to achieve community protection. Those at high risk for infection and serious complications are especially advised to be vaccinated to protect themselves. Using influenza as a model, we studied one method of increasing vaccine uptake: informing high-risk patients, identified by a machine learning model, about their risk status. Patients (N=39,717) were evenly randomized to (1) a control condition (exposure only to standard direct mail or patient portal vaccine promotion efforts) or to be told via direct mail, patient portal, and/or SMS that they were (2) at high risk for influenza and its complications if not vaccinated; (3) at high risk according to a review of their medical records; or (4) at high risk according to a computer algorithm analysis of their medical records. Patients in the three treatment conditions were 5.7% more likely to get vaccinated during the 112 days post-intervention (p < .001), and did so 1.4 days earlier (p < .001), on average, than those in the control group. There were no significant differences among risk messages, suggesting that patients are neither especially averse to nor uniquely appreciative of learning their records had been reviewed or that computer algorithms were involved. Similar approaches should be considered for COVID-19 vaccination campaigns.
Publisher
Cold Spring Harbor Laboratory
Reference19 articles.
1. Hamel, L. , Kirzinger, A. , Lopes, L. , Kearney, A. , Sparks, G. , & Brodie, M. (2021). KFF COVID-19 Vaccine Monitor: January 2021. Kaiser Family Foundation. Retrieved from https://www.kff.org/report-section/kff-covid-19-vaccine-monitor-january-2021-vaccine-hesitancy/
2. The Effect of Giving Global Coronary Risk Information to Adults
3. Patient delay in seeking help for potential breast cancer;Public Health Reviews,1995
4. Information avoidance;Journal of Economic Literature,2017
5. Understanding the patient privacy perspective on health information exchange: A systematic review;International Journal of Medical Informatics,2019
Cited by
4 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献