Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

Not Applicable

Completed

• Conditions: Hypertension
• Interventions: Other: AESOP service system

• Registration Number: NCT03484793
• Lead Sponsor: Taipei Medical University

Brief Summary

Medication errors are common, life-threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. In this study, investigators would perform a cluster randomized controlled trial of a clinical reminding system that uses DNN and Probabilistic models to detect and notify physicians of inappropriate prescriptions, giving them the opportunity to correct these gaps and increase prescriptions completeness. This study aim is to assess whether or not this system would improve prescription notation for a broad array of patient conditions.

Detailed Description

This paper focuses on "Big data" in the knowledge base, using "Data minig" study of DM (Disease-Medication) and MM (Medication-Medication) of relevance to develop associated decision resources system-"the intelligent safety system" (Advanced Electronic Safety of Prescriptions,AESOP Model), and test the system in the clinical environment in hospital can assist physicians when open orders reduce medication errors, the system is named "AESOP Model".

Study Timeline

• Status Verified: 2017-01
• Study Start: 2017-05-01
• Primary Completion: 2017-12-31
• Study Completion: 2018-02-28
• Study First Submitted: 2018-03-26
• Study First Submitted QC: 2018-03-26
• Last Update Submitted: 2018-03-31
• Study First Posted: 2018-04-02
• Last Update Posted: 2018-04-03

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 37

Inclusion Criteria

• Physicians who are working at the outpatient clinics in hospitals.
• Physicians who sign the consent form

Exclusion Criteria

• Physicians who are unable to participate in this trial for the whole process
• Physicians who do not sign the consent form

Study & Design

• Study Type: INTERVENTIONAL
• Study Design: PARALLEL

Arm && Interventions

Group	Intervention	Description
AESOP integrated to CPOE for reducing medication errors	AESOP service system	18 were assigned to the experimental group

Primary Outcome Measures

Name	Time	Method
The acceptance rate of reminder between two groups intervention and control	3 months	The primary outcome of this study is the acceptance rate of the reminder, defined as the number of reminders accepted divided by number of unique reminders presented. In certain instances, physicians might see the same reminder serially, so we aggregate presentations and acceptance of the same reminder for the same patients' prescriptions in our calculation of the acceptance rate.

Secondary Outcome Measures

Name	Time	Method
The changes in the number of reminder for each group	3 months	As a secondary outcome, we measure the number of inappropriate prescriptions rate documented in the two groups during the two time periods and calculate the unadjusted relative rate of inappropriateness notation in the intervention group by comparing the number of inappropriateness recorded in the intervention arm during the intervention period to all other groups. The unadjusted relative rate is defined as the ratio (errorsintervention-post/errorscontrol-post)/ (errorsintervention-pre/errorscontrol-pre).

Trial Locations

Locations (1)

TMU-Shuang-Ho Hospital; 🇨🇳 Taipei, Taiwan