Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

NCT03484793 | Completed

• 1 other identifier: NSC105-2634-F-038-001
• Study Type: interventional
• Target: 37
• Locations: 1 country
• Sites: 1

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.

Conditions

Hypertension

Keywords

AESOP Model; Big data; medication error; data mining; patient safety; CPOE; CDSS; alarm fatigue; association rule mining.

Outcome Measures

Primary Outcomes (1)

• 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 Outcomes (1)

• The changes in the number of reminder for each group

  3 months

Study Arms (2)

AESOP integrated to CPOE for reducing medication errors

EXPERIMENTAL

18 were assigned to the experimental group

Other: AESOP service system

Non AESOP

NO INTERVENTION

19 were assigned to the traditional CPOE system

Interventions

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.

AESOP integrated to CPOE for reducing medication errors

Eligibility Criteria

• Sex: all
• Healthy Volunteers: No
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)

You may qualify if:

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

You may not qualify if:

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

Sponsors & Collaborators

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

Study Sites (1)

TMU-Shuang-Ho Hospital

Taipei, Taiwan

Location

MeSH Terms

Conditions

Hypertension; Alert Fatigue, Health Personnel

Condition Hierarchy (Ancestors)

Vascular Diseases; Cardiovascular Diseases; Mental Fatigue; Fatigue; Signs and Symptoms; Pathological Conditions, Signs and Symptoms; Behavioral Symptoms; Behavior

Study Officials

• Yu-Chuan MD Li, PhD

  Taipei Medical University

  STUDY CHAIR

• Chuya Huang, MA

  Taipei Medical University

  STUDY DIRECTOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: March 26, 2018
• First Posted: April 2, 2018
• Study Start: May 1, 2017
• Primary Completion: December 31, 2017
• Study Completion: February 28, 2018
• Last Updated: April 3, 2018

Record last verified: 2017-01

Locations

TW (1)