Can providing real-time warnings and feedback to doctors within a hospital information system reduce inappropriate antibiotic prescribing?

Research on 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

Interventional

Source

ISRCTN

Registry ID

ISRCTN13817256

Enrollment

320

Registered

2020-01-09

Start date

2020-10-01

Completion date

Unknown

Last updated

2025-03-24

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

Conditions

Antibiotic prescription for infectious diseases common in primary care institutions Infections and Infestations

Interventions

This is a non-blind cluster randomized crossover open controlled trial (multicentre) conducted in 100 hospitals randomly selected from the Health Information Center Database of Guizhou Provincial Hea

Eligibility

Sex/Gender

All

Inclusion criteria

Inclusion criteria: 1. Primary care physicians who have worked at the township public hospital for at least 6 months 2. Prescribe an average of 100 prescriptions or more in 10 days 3. Have given their consent before being enrolled in the study

Exclusion criteria

Exclusion criteria: 1. Do not meet inclusion criteria 2. Do not have the right to prescribe 3. Refuse to accept intervention

Design outcomes

Primary

Measure	Time frame
The 10-day antibiotic prescription rate of the physicians, defined as the number of antibiotic prescriptions divided by the total number of prescriptions in each 10-day time period. A 'prescription' refers to each antibiotic drug. The antibiotic prescription rate is assessed using hospital pharmacy stock records during the 3-month intervention period.	—

Countries

China

Outcome results

None listed

Source: ISRCTN (via WHO ICTRP) · Data processed: Feb 13, 2026