Reduce Medication Errors by Translating AESOP Model Into CPOE Systems

Study Overview
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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.

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.

Official Title
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Using Big Data and Deep Neural Network to Prevent Medication Errors

Conditions
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Hypertension

Intervention / Treatment
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* Other: AESOP service system


Participation Criteria
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Eligibility Criteria
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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
Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: None (Open Label)


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: AESOP integrated to CPOE for reducing medication errors<br>18 were assigned to the experimental group | Other: AESOP service system<br>* 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.<br>|
| No Intervention: Non AESOP<br>19 were assigned to the traditional CPOE system | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| 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 | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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Big data, AESOP Model, medication error, data mining, patient safety, CPOE, CDSS, alarm fatigue, association rule mining.