Nudging Flu Vaccination in Patients at Moderately High Risk for Flu and Flu-related Complications
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 40,671
- 试验地点
- 2
- 主要终点
- Flu vaccination
研究概览
简要总结
This study will test the relative efficacy of high-risk messages in increasing flu shot rates in patients at moderately high risk for flu and complications (those in the top 11-20% of risk). It will also examine whether informing patients that their high-risk status was determined by analyzing their medical records or by an artificial intelligence (AI) / machine-learning (ML) algorithm analyzing their medical records will affect the likelihood of receiving a flu vaccine.
详细描述
Almost everyone age 6 months or older can benefit from the vaccine, which can reduce illnesses, missed work, hospitalizations, and death by reducing the likelihood of contracting influenza. Flu shots are particularly important for patients at high risk of experiencing severe outcomes.
In the 2020-21 and 2021-22 flu seasons, the study team sent messages to Geisinger patients in the top 10% of risk for flu and complications according to an artificial intelligence algorithm. Messages that disclosed patients' risk status significantly increased flu vaccination rates. Additionally, messages that included risk information were most effective in patients at relatively lower risk (those in the top 4-10%) compared with those at the highest risk (top 3%).
The present work will test the effectiveness of high-risk messages in patients who are in the top 11-20% of risk, at high risk but lower than previous studies. These communications will inform patients they are at high risk with either (a) no additional explanation, (b) an explanation that this determination comes from an analysis of their medical records, or (c) the additional explanation that an AI or ML algorithm made this determination.
研究设计
- 研究类型
- Interventional
- 分配方式
- Randomized
- 干预模型
- Parallel
- 主要目的
- Prevention
- 盲法
- Single (Care Provider)
盲法说明
Providers who prescribe vaccination and diagnose conditions will not be randomized to study arms or informed of patient assignment. Although patients will not be explicitly informed which arm they have been randomized to, they will be aware of the messages they receive.
入排标准
- 年龄范围
- 18 Years 至 —（Adult, Older Adult）
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •Included on a list of active Geisinger patients (all patients on this list attended at least one primary care appointment at Geisinger between 10/1/2008 and 4/13/2022, and either had a Geisinger primary care provider assigned as of April 2022, or were in the Electronic Health Record [EHR] since at least September 2021 and had at least one encounter in 2020-2022)
- •Aged 18 or older
- •In the top 11-20% of risk for flu and flu complications, according to Medial's flu complications machine learning algorithm (which operates on coded EHR data)
- •Has a Geisinger PCP assigned as of August 2022
- •Has had an encounter in the last 2 years as of August 2022
- •Exclusion criteria:
- •Cannot be contacted via any of the communication modalities (e.g., letter, patient portal, SMS) being used in the study, either due to insufficient/missing contact information in the EHR or because they opted out of all modalities
排除标准
- 未提供
结局指标
主要结局
Flu vaccination
时间窗: Within 6 weeks of the patient's study start date
Received a flu vaccination within within 6 weeks of the patient's study start date
次要结局
未报告次要终点
研究者
Christopher F Chabris, PhD
Professor
Geisinger Clinic