Viewing Study NCT03484793


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Study NCT ID: NCT03484793
Status: COMPLETED
Last Update Posted: 2018-04-03
First Post: 2018-03-26
Is NOT Gene Therapy: False
Has Adverse Events: False

Brief Title: Reduce Medication Errors by Translating AESOP Model Into CPOE Systems
Sponsor: Taipei Medical University
Organization:

Study Overview

Official Title: Using Big Data and Deep Neural Network to Prevent Medication Errors
Status: COMPLETED
Status Verified Date: 2017-01
Last Known Status: None
Delayed Posting: No
If Stopped, Why?: Not Stopped
Has Expanded Access: False
If Expanded Access, NCT#: N/A
Has Expanded Access, NCT# Status: N/A
Acronym: AESOP
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 Oversight

Has Oversight DMC: False
Is a FDA Regulated Drug?: False
Is a FDA Regulated Device?: False
Is an Unapproved Device?: None
Is a PPSD?: None
Is a US Export?: None
Is an FDA AA801 Violation?: