Skip to main content
OpenTrials
Completed

NCT Number: NCT03484793

Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

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.

Completed

Looking for future studies?

Notify Me

Key information

Conditions

Hypertension Alert Fatigue, Health Personnel Behavior Behavioral Symptoms Cardiovascular Diseases Fatigue Mental Fatigue Pathological Conditions, Signs and Symptoms Signs and Symptoms Vascular Diseases

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

TMU-Shuang-Ho Hospital

Taipei, Taiwan

About this study

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".

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

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

Treatment and study plan

AESOP service system

Other

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.

Primary outcomes

  1. The acceptance rate of reminder between two groups intervention and control

    Time frame: 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 outcomes

  1. The changes in the number of reminder for each group

    Time frame: 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).

Sponsors and collaborators

Lead sponsor

Taipei Medical University

Other

Collaborators

  • 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 WanFang Hospital
  • Taiwan College of Healthcare Executives
  • Yong He Cardinal Tien Hospital

Registry information

Official study title

Using Big Data and Deep Neural Network to Prevent Medication Errors

Acronym: AESOP

Important dates

Study start
2017
Primary completion
2017
Study completion
2018
First posted
Apr 2, 2018
Registry last updated
Apr 3, 2018

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Related clinical trials

Published trials that share one or more normalized conditions with this study.