ArticleNature human behaviour2026
Algorithm-assisted personalized risk communication to encourage flu vaccination in the USA: three randomized field trials.
Article in Nature human behaviour, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to 3 registered trials, which are not on this map. Not yet cited in PubMed.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Encouraging Flu Vaccination Among High-Risk Patients Identified by a Machine-Learning Model of Flu Complication Risk
Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
Nudging Flu Vaccination in Patients at Moderately High Risk for Flu and Flu-related Complications
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0 citing papers in PubMed.
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Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
Abstract
Artificial intelligence (AI) is increasingly used in healthcare to identify high-risk patients. Laboratory studies are mixed regarding patient attitudes towards AI use. Here we field-tested whether informing patients of their risk for influenza and its complications can serve as an effective behaviour-change intervention to increase vaccination, and whether disclosing AI use in the risk determination or providing personalized reasons for the algorithm's prediction (a form of explainable AI) influence message effectiveness. We ran three preregistered randomized controlled trials with over 90,000 unique healthcare system patients in the USA (ClinicalTrials.gov identifiers: NCT04323137 , NCT05009251 and NCT05509283 ). Patients identified by a previously validated machine learning algorithm as being at high risk for influenza and related complications were randomized to be sent no message or one of several different messages encouraging influenza vaccination (the primary outcome). High-risk nudges increased vaccination: among patients informed of their high risk, vaccination was 1.1-1.4 percentage points (3.3-5.4%) higher than those simply reminded to get a vaccine, and 1.7-3.5 percentage points (3.3-14.7%) higher than non-messaged patients. Vaccination was similar across message arms that did versus did not disclose 'algorithm' involvement or its risk explanations, indicating that patients are neither averse to nor appreciative of algorithm use or explainability in this realistic health application. This work was partially funded by the National Institute on Aging of the National Institutes of Health under award number P30AG034532.
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.