Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

COMPLETED

Clinical Phase

NA

Total Enrollment

37 participants

Study Classification

INTERVENTIONAL

Study Start Date

2017-05-01

Study Completion Date

2018-02-28

Brief Summary

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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.

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Detailed Description

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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".

Conditions

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Hypertension

Study Design

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Allocation Method

RANDOMIZED

Intervention Model

PARALLEL

Primary Study Purpose

HEALTH_SERVICES_RESEARCH

Blinding Strategy

NONE

Study Groups

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AESOP integrated to CPOE for reducing medication errors

18 were assigned to the experimental group

Group Type EXPERIMENTAL

AESOP service system

Intervention Type 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.

Non AESOP

19 were assigned to the traditional CPOE system

Group Type NO_INTERVENTION

No interventions assigned to this group

Interventions

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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.

Intervention Type OTHER