Alerts With Risk Information to Increase Influenza Vaccinations
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 80,452
- 试验地点
- 2
- 主要终点
- Flu Vaccination
研究概览
简要总结
The purpose of this study is to assess, prospectively, the effect on flu vaccination rates of salient alerts in the electronic health record that indicate a patient's high risk for flu and its complications. The investigators hypothesize that the salient alerts will lead to increased flu vaccination compared with a standard flu alert.
详细描述
The CDC (Centers for Disease Control) recommends a flu vaccination to everyone aged 6+ months, with rare exception; almost anyone can benefit from the vaccine, which can reduce illnesses, missed work, hospitalizations, and death. One barrier to vaccination is a lack of "cues to action," and, in particular, the lack of direct recommendation from medical personnel; this barrier is arguably the most effectively overcome by a simple nudge of clinicians, compared with barriers such as negative attitudes toward vaccination, low perceived utility of vaccination, and less experience with having received the vaccine.
Geisinger partnered with Medial EarlySign (Medial) to develop a machine learning (ML) algorithm to help identify people at risk for serious flu-associated complications based on existing electronic health record data. Eligible at-risk patients will be randomized to an active control group (clinician will be shown a standard flu alert) or one of two experimental groups (clinician will be shown an alert indicating patient's high risk, with or without describing the patient's factors contributing to that risk).
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 (Participant)
盲法说明
Patient-participants will not be explicitly told about the different arms, although clinician-participants will see the different arms and may notice these differences.
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
- •Have been determined to be in the top 20% of risk through Medial's ML algorithm
- •Attend an appointment where the flu alert fires (Geisinger sets when flu alerts start and end--between ~9/1/2022 and ~4/30/2023, as well as the trigger conditions for the alert, which includes valid departments and visits and excludes contraindications like Guillain-Barre syndrome)
- •Clinician Inclusion Criteria:
- •Any Geisinger clinician who sees patient-participants in our study for an appointment where their flu shot alert fires
排除标准
- 未提供
结局指标
主要结局
Flu Vaccination
时间窗: At the 1 day visit
Patient received a flu vaccine (yes/no)
次要结局
未报告次要终点
研究者
Christopher F Chabris, PhD
Professor
Geisinger Clinic
