Using Explainable AI Risk Predictions to Nudge Influenza Vaccine Uptake
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 入组人数
- 45,061
- 试验地点
- 2
- 主要终点
- Flu Vaccination at 2 Weeks After Final Outreach Date
研究概览
简要总结
The study team previously demonstrated that patients are more likely to receive flu vaccine after learning that they are at high risk for flu complications. Building on this past work, the present study will explore whether providing reasons that patients are considered high risk for flu complications (a) further increases the likelihood they will receive flu vaccine and (b) decreases the likelihood that they receive diagnoses of flu and/or flu-like symptoms in the ensuing flu season. 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 the flu vaccine or diagnoses of flu and/or flu-like symptoms.
详细描述
Geisinger has partnered with Medial EarlySign and developed an ML algorithm to identify patients at risk for serious (moderate to severe) flu-associated complications on the basis of their existing electronic health record (EHR) data. Geisinger will apply this algorithm to current patients during the 2021-22 flu season.
This study will evaluate the effect of contacting patients identified as high risk with special messages to encourage vaccination. 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, along with a short list of the top factors from their medical record that explain their risk, and (c) the additional explanation that an AI or ML algorithm made this determination, along with a short list of the top factors from their medical record that explain their risk.
Included in the study will be current Geisinger patients 18+ years of age with no contraindications for flu vaccine and who have been assessed by the Medial algorithm and assigned a risk score. The primary study outcomes will be the rates of flu vaccination and flu diagnosis during the 2020-21 season by targeted patients.
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
- •Aged 18 or older
- •Current Geisinger patient at the time of study
- •Falls in the top 10% of patients at highest risk, as identified by the flu-complication risk scores of machine learning algorithm (which operates on coded EHR data)
排除标准
- •Has contraindications for flu vaccination
- •Has opted out of receiving communications from Geisinger via all of the modalities being tested
结局指标
主要结局
Flu Vaccination at 2 Weeks After Final Outreach Date
时间窗: Within 2 weeks of the final outreach date, 57 days (8.14 weeks) after the study start
Received flu vaccination
次要结局
- Healthcare Utilization(11 months (between September 9, 2021 and July 31, 2022))
- Flu Diagnosis(8 months (between September 9, 2021 and April 30, 2022))
- Flu Complications(11 months (between September 9, 2021 and July 31, 2022))
- Flu Vaccination at 9 Weeks After Final Outreach Date(Within 9 weeks of the final outreach date, 106 days (15.14 weeks) after the study start)