Ali Pay Intelligent Navigation Applet-aided Pre-hospital Triage for Non-emergency Medical Service Patients With Acute Ischemic Stroke (i-Path)

Zhejiang University

Status: Enrolling

Conditions: Acute Ischemic Stroke

Treatments: Device: Ali Pay intelligent navigation applet

Study type: Interventional

Funder types: Other

Identifiers

NCT06613074

i-Path

Details and patient eligibility

About

According to the Bigdata Observatory platform for Stroke of China (BOSC), the proportion of patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis or endovascular treatment in China is 5.64% and 1.45% respectively. One of the important reasons for the low treatment rate is the prolonged pre-hospital and in-hospital delay. Besides, for patients receiving reperfusion therapy, the prolonged pre-treatment delay is associated with unfavorable functional outcomes.

Although tons of efforts have been made to improve the efficiency of emergency medical system in the transportation of patients with AIS, little attention has been paid to patients who arrived at hospitals on their owns, which occupying approximately 2/3 of emergency patients. This leaves a huge gap in the pre-hospital management of patietns with AIS.

Therefore, the investigators plan to develop an intelligent navigation system for patients with AIS. For the convenience of public use, this system was carried on the applet of Ali Pay, which has over 1.1 billion users in China. This system comprises of three functional modules, namely stroke knowledge education, stroke recognition and hospital recommendation. The investigators aim to explore whether this intelligent navigatino system could shorten pre-hospital delay and improve functional outcomes of patients with AIS undergoing reperfusion therapy.

Full description

According to the Bigdata Observatory platform for Stroke of China (BOSC), the proportion of patients with acute ischemic stroke (AIS) receiving intravenous thrombolysis or endovascular treatment in China is 5.64% and 1.45% respectively. One of the important reasons for the low treatment rate is the prolonged pre-hospital and in-hospital delay. Besides, for patients receiving reperfusion therapy, the prolonged pre-treatment delay is associated with unfavorable functional outcomes.

Although tons of efforts have been made to improve the efficiency of emergency medical system in the transportation of patients with AIS, little attention has been paid to patients who arrived at hospitals on their owns, which occupying approximately 2/3 of emergency patients. This leaves a huge gap in the pre-hospital management of patietns with AIS.

Therefore, the investigators plan to develop an intelligent navigation system for patients with AIS. For the convenience of public use, this system was carried on the applet of Ali Pay, which has over 1.1 billion users in China. This system comprises of three functional modules, namely stroke knowledge education, stroke recognition and hospital recommendation.The investigators aim to explore whether this intelligent navigatino system could shorten pre-hospital delay and improve functional outcomes of patients with AIS undergoing reperfusion therapy.

Enrollment: 20,000 estimated patients

Sex: All

Ages: 18+ years old

Volunteers: No Healthy Volunteers

Inclusion criteria

• Patients diagnosed as acute ischemic stroke undergoing reperfusion therapy within 24 hours of onset

Exclusion criteria

• Patients transported to hospitals via emergency medical service
• Patients with in-hospital stroke

Trial design

Primary purpose: Health Services Research

Allocation: Randomized

Interventional model: Parallel Assignment

Masking: Single Blind

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20,000 participants in 2 patient groups

Ali Pay intelligent navigation applet group

Experimental group

Description:

Patients in regions with Ali Pay intelligent navigation applet being released would be classified as experimental arm. In this arm, patients have access to this applet. The intelligent navigation applet comprises of three function modules: 1. Stroke knowledge public education: information regarding prevention and emergency treatment of stroke would be push to users' mobile phones regularly; 2. Stroke recognition: questionaires, voice interaction, and facial recognition are employed to identify patients with AIS and large vessel occlusion; 3. Hospital recommendation: this module combines real-time traffic and average in-hopital delay of each stroke center nearby, recommending the stroke center in which patients are mostly likely to receive reperfusion therapy

Routine pre-hospital triage

No Intervention group

Description:

Patients in regions without Ali Pay intelligent navigation applet being released would be classified as control arm. In this arm, patients do not have access to this applet.