HypertensionAESOPNCT03484793
Reduce Medication Errors by Translating AESOP Model Into CPOE Systems
A study testing AESOP service system in people with Hypertension, run by Taipei Medical University at 1 site in Taiwan.
Trial 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. Read more about this study
- Who is it for?
- Any age
- Conditions the study is for
- Hypertension
- What is being tested?
- AESOP service system
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 37 participants
- Where
- 1 siteTaiwan
- Study dates
- 1 May 2017to 28 Feb 2018
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages Any age
- All sexes
- No healthy volunteers
- Child, Adult, Older adult
Who may be able to join
- Physicians who are working at the outpatient clinics in hospitals.
- Physicians who sign the consent form
Who cannot join
- Physicians who are unable to participate in this trial for the whole process
- Physicians who do not sign the consent form
Trial locations and contacts
1 site in 1 country.
About this study
Official title: Using Big Data and Deep Neural Network to Prevent Medication Errors
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.
Read the 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".
- Conditions
- Hypertension
- Being tested
- AESOP service system
- Keywords
- AESOP Model, Big data, medication error, data mining, patient safety, CPOE, CDSS, alarm fatigue, association rule mining.
- Medical subject headings
- Hypertension, Alert Fatigue, Health Personnel
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
AESOP service systemOther
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.
| Study group | Role | What they receive |
|---|---|---|
| AESOP integrated to CPOE for reducing medication errors | Experimental | 18 were assigned to the experimental group Receives: Other: AESOP service system |
| Non AESOP | No intervention | 19 were assigned to the traditional CPOE system |
What is being measured
The primary outcome is the main question the study is designed to answer.
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.
Measured: 3 months
Secondary outcomes (1)
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).
Measured: 3 months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted2 Apr 2018
- Study start1 May 2017
- Primary completion31 Dec 2017
- Study completion28 Feb 2018
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 37 participants (actual)
Sponsor and investigators
- Lead sponsor
- Taipei Medical University
- Sponsor type
- University, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan, Taipei Medical University Shuang Ho Hospital, Taiwan College of Healthcare Executives, Taipei Medical University WanFang Hospital, Case Western Reserve University, Cardinal Tien Hospital, Yong He Cardinal Tien Hospital, Chang Hua Christian Hospital, Taipei Medical University Hospital
- Responsible party
- Sponsor
- Investigators
- Yu-Chuan MD Li, PhD, Study chair, Taipei Medical UniversityChuya Huang, MA, Study director, Taipei Medical University
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03484793
- Sponsor's protocol ID
- NSC105-2634-F-038-001
- Acronym
- AESOP
- Status last verified
- Jan 2017
- First submitted
- 26 Mar 2018
- Last updated
- 3 Apr 2018
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Study at a glance
From the registry record, last updated by the sponsor on 3 Apr 2018. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Using Big Data and Deep Neural Network to Prevent Medication Errors
- ClinicalTrials.gov ID
- NCT03484793
- Acronym / sponsor ID
- AESOP / NSC105-2634-F-038-001
- Conditions
- Hypertension
- Keywords
- AESOP Model, Big data, medication error, data mining, patient safety, CPOE, CDSS, alarm fatigue
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 37 participants (actual)
- Lead sponsor
- Taipei Medical UniversityUniversity, hospital or other
- Collaborators
- Ministry of Science and Technology, Taiwan, Taipei Medical University Shuang Ho Hospital, Taiwan College of Healthcare Executives, Taipei Medical University WanFang Hospital, Case Western Reserve University, Cardinal Tien Hospital, Yong He Cardinal Tien Hospital, Chang Hua Christian Hospital, Taipei Medical University Hospital
- Study start
- 1 May 2017 (actual)
- Primary completion
- 31 Dec 2017 (actual)
- Study completion
- 28 Feb 2018 (actual)
- First posted
- 2 Apr 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| AESOP integrated to CPOE for reducing medication errors | Experimental | Other: AESOP service system18 were assigned to the experimental group |
| Non AESOP | No intervention | –19 were assigned to the traditional CPOE system |
Who may qualify
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Child, Adult, Older adult
Key inclusion criteria (2 listed)
- Physicians who are working at the outpatient clinics in hospitals.
- Physicians who sign the consent form
Key exclusion criteria (2 listed)
- Physicians who are unable to participate in this trial for the whole process
- Physicians who do not sign the consent form
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| AESOP service systemInvestigators 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. | Other | AESOP integrated to CPOE for reducing medication errors |
Primary outcome
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.
1 secondary outcome
The changes in the number of reminder for each group
Time frame: 3 months
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 3 Apr 2018.
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Yu-Chuan MD Li, PhD study chair
Taipei Medical University
Chuya Huang, MA study director
Taipei Medical University
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing AESOP service system in people with Hypertension, run by Taipei Medical University at 1 site in Taiwan.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 2 inclusion and 2 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 3 Apr 2018. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Taiwan. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
The acceptance rate of reminder between two groups intervention and control (time frame: 3 months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 3 Apr 2018.