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临床试验/NCT07829432
NCT07829432尚未招募不适用

Preventing No-shows With Pre-appointment "Reserved for You" Nudges

Geisinger Clinic0 个研究点目标入组 17,376 人开始时间: 2026年9月1日最近更新:
适应症
干预措施

试验速览

阶段
不适用
状态
尚未招募
入组人数
17,376
主要终点
No showed to appointment (y/n)

研究概览

简要总结

The study team will run an A/B test to assess whether pre-appointment messages can reduce appointment no-shows. One message version will indicate the appointment is "reserved" for the patient; another version will be nearly identical but will not include the "reserved" language. A passive control group will not be sent messages.

详细描述

No-shows are a problem for health systems because they prevent appointment slots from being used by other patients waiting to be seen. Canceling at least 48 hours in advance of the appointment time helps ensure improved access for those patients who are waiting, as well as potentially improving the chances that the canceling patients will eventually be seen (by encouraging rescheduling). An algorithm in Epic attempts to predict which patients are at high risk for no-showing to their appointments. In the present initiative, Geisinger will send pre-appointment text messages and/or MyChart messages to patients predicted to be at high risk for no-showing to their appointment (>50% likely to no-show according to the algorithm).

Prior research suggests that telling patients a vaccine is "reserved for you" increases vaccine update (e.g., Milkman et al., 2021). However, it is unclear whether "reserved for you" language also motivates other health behaviors, like attending appointments.

The study team will run an A/B test to assess whether pre-appointment messages can reduce appointment no-shows. One message version will indicate the appointment is "reserved" for the patient; another version will be nearly identical but will not include the "reserved" language. A passive control group will not be sent messages.

Messages will be sent 10 days pre-appointment via MyChart or text message based on patient communication preferences.

For the primary analysis, we will include only the first appointment for every unique patient.

研究设计

研究类型
Interventional
分配方式
Randomized
干预模型
Parallel
主要目的
Health Services Research
盲法
None

盲法说明

Patients will know what messages they receive if assigned to a message group, but they will not know that other patients get different messages or no messages.

入排标准

性别
All
接受健康志愿者

入选标准

  • at least a 50% probability of no-showing for the appointment based on the no-show algorithm
  • eligible to be sent MyChart messages or text messages
  • has an upcoming appointment in 10 days

排除标准

  • Ancillary visits
  • Nursing visits

研究组 & 干预措施

Passive control

No Intervention

Patient will not be sent a message

Message without "reserved" language

Experimental

Patient will be sent a message encouraging them to reschedule or cancel their appointment if they cannot attend. This message will NOT state the appointment is reserved for them.

干预措施: Message (Behavioral)

Message with "reserved" language

Experimental

Patient will be sent a message encouraging them to reschedule or cancel their appointment if they cannot attend. This message WILL state the appointment is reserved for them.

干预措施: Message (Behavioral)

结局指标

主要结局

No showed to appointment (y/n)

时间窗: 1 day (10 days following the message date)

Patient no-showed to the target appointment

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

Gail Rosenbaum

Program Director, Geisinger Behavioral Insights Team

Geisinger Clinic

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