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Research for feedback as a method to alter antibiotic prescription control in primary medical institutions based on graph neural network technology

Research for feedback as a method to alter antibiotic prescription control in primary medical institutions based on graph neural network technology

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2000029347
Enrollment
Unknown
Registered
2020-01-25
Start date
2020-03-01
Completion date
Unknown
Last updated
2020-02-03

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

The rate of Antibiotic prescription

Interventions

Intervention group and control group:Intervention of Antibiotic Prescription Rates and Related Situations by Online Feedback to Doctors through HIS

Sponsors

Guizhou Medical University
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: 1. Have the same HIS system; 2. Outpatient doctors have been in the post for at least 1 year; 3. 100 general-level primary hospitals with 3 or more general outpatient doctors who can prescribe 100 prescriptions stably within 10 days.

Exclusion criteria

Exclusion criteria: Exclusion criteria Tuberculosis (TB) should be excluded because the treatment options for TB are fixed and standard.

Design outcomes

Primary

MeasureTime frame
The rate of Antibiotic prescription;—

Countries

China

Contacts

Public ContactChang Yue

Guizhou Medical University

4567401@qq.com+86 13608538065

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026