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

NCT ID: NCT06613074

Last Updated: 2025-08-08

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

RECRUITING

Clinical Phase

NA

Total Enrollment

20000 participants

Study Classification

INTERVENTIONAL

Study Start Date

2025-04-01

Study Completion Date

2026-05-01

Brief Summary

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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.

Detailed Description

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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.

Conditions

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Acute Ischemic Stroke

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

SINGLE

Outcome Assessors

Study Groups

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Ali Pay intelligent navigation applet group

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

Group Type EXPERIMENTAL

No interventions assigned to this group

Routine pre-hospital triage

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.

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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Ali Pay intelligent navigation 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

Intervention Type DEVICE

Eligibility Criteria

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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
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Second Affiliated Hospital, School of Medicine, Zhejiang University

OTHER

Sponsor Role lead

Responsible Party

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Min Lou

Prof. Dr.

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Shaoxing People's Hospital

Shaoxing, Zhejing, China

Site Status RECRUITING

Countries

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China

Central Contacts

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Min Lou, PhD, MD

Role: CONTACT

86057187783777

Other Identifiers

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i-Path

Identifier Type: -

Identifier Source: org_study_id

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