Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

Hypertension Clinical Trial

— AESOP  

Official title:

Using Big Data and Deep Neural Network to Prevent Medication Errors

Clinical Trial Details — Status: Completed

Administrative data

• NCT number: NCT03484793
• Other study ID #: NSC105-2634-F-038-001
• Status: Completed
• Phase: N/A
• First received: March 26, 2018
• Last updated: March 31, 2018
• Start date: May 1, 2017
• Est. completion date: February 28, 2018

Study information

• Verified date: January 2017
• Source: Taipei Medical University
• Contact: n/a
• Is FDA regulated: No
• Study type: Interventional

Clinical Trial 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.

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".

Recruitment information / eligibility

• Status: Completed
• Enrollment: 37
• Est. completion date: February 28, 2018
• Est. primary completion date: December 31, 2017
• Accepts healthy volunteers: No
• Gender: All
• Age group: N/A and older

Eligibility

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

Related Conditions & MeSH terms

• Hypertension

Intervention

Other:
• AESOP service system
Investigators develop an electronic reminder in CPOE system which notifies physicians when there appears to be an inappropriate prescription. At the time, a physician saves a typed prescription, our system analyzes the patient's medications, diseases and uses the knowledge base to determine whether a medication is uncommonly prescribed to all diseases in a given prescription. If the system detects the common associations of medications and diseases in a given prescription, it considers an appropriate prescription, and, if not, an actionable reminder is shown onscreen. To the right of each suggested uncommon medication is a reason why the reminder is appearing. Physicians can accept the reminder or ignore the reminder.

Locations

Country	Name	City	State
Taiwan	TMU-Shuang-Ho Hospital	Taipei

Sponsors (10)

Lead Sponsor	Collaborator
Taipei Medical University	Cardinal Tien Hospital, Case Western Reserve University, Chang Hua Christian Hospital, Ministry of Science and Technology, Taiwan, Taipei Medical University Hospital, Taipei Medical University Shuang Ho Hospital, Taipei Medical University Taipei Municipal Wan Fang Hospital, Taiwan College of Healthcare Executives, Yong He Cardinal Tien Hospital

Country where clinical trial is conducted

Taiwan, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	The acceptance rate of reminder between two groups intervention and control	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.	3 months
Secondary	The changes in the number of reminder for each group	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).	3 months