跳至主要内容
临床试验/NCT05492786
NCT05492786已完成不适用

Alerts With Risk Information to Increase Influenza Vaccinations

Geisinger Clinic2 个研究点 分布在 1 个国家目标入组 80,452 人开始时间: 2022年9月1日最近更新:
适应症

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

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

申办方类型
Other
责任方
Principal Investigator
主要研究者

Christopher F Chabris, PhD

Professor

Geisinger Clinic

研究点 (2)

Loading locations...

相似试验