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Drug Interactions in Outpatients.

Evaluation of a Drug Interactions Software (Interax-AI) in Outpatients.

Status
Suspended
Phases
NA
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT03943524
Enrollment
200
Registered
2019-05-09
Start date
2019-08-01
Completion date
2022-12-01
Last updated
2022-05-25

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Adverse Drug Reaction, Drug Interaction, Outpatient, Polypharmacy

Keywords

Polypharmacy, Outpatient, Drug Interaction, Adverse Drug Reaction, DrApp, Interax-AI

Brief summary

Multiple morbidity is increasing, especially in elderly people, with a corresponding increase in polypharmacy and inappropriate prescriptions. According to different evaluations, between 25 and 75% of patients aged 75 or older are exposed to 5 or more drugs. There is increasing evidence that polypharmacy can cause more harm than good, especially in elderly people, due to factors such as drug-drug and drug-disease interactions. Many strategies were proposed to reduce polypharmacy and inappropriate prescribing, but there is little evidence to show benefit. There is an urgent need to implement effective strategies. The application methodology must be simple so that it does not fail in daily practice. For the current plan, an electronic medical record, named DrApp, will be used, which will include a drug interaction program, (Interax-AI), which will automatically indicate the medication prescriptions that involve a risk for the patient. All outpatient indications followed by physicians using the DrApp electronic history will be registered. The indications will be compared in the 4 months prior to the incorporation of the Interax-AI program with the 4 months after the incorporation of the program. Between both stages a period of 2 weeks will be established in which the data will not be recorded. The minimum & maximum number of patients that will be included in each stage are 100 & 200. The primary end point is to compare the total number of indications per inpatient, before the availability of the Interax-AI program and after the application of this program. The objective is to evaluate if the computer program of detection of drug interactions allows to limit the polypharmacy in outpatients.

Detailed description

Multiple morbidity is increasing, especially in elderly people, with a corresponding increase in polypharmacy and inappropriate prescriptions. According to different evaluations, between 25 and 75% of patients aged 75 or older are exposed to 5 or more drugs. There is increasing evidence that polypharmacy can cause more harm than good, especially in elderly people, due to factors such as drug-drug and drug-disease interactions. Many strategies were proposed to reduce polypharmacy and inappropriate prescribing, but there is little evidence to show benefit. There is an urgent need to implement effective strategies. The application methodology must be simple so that it does not fail in daily practice. For the current plan, an electronic medical record, named DrApp, will be used, which will include a drug interaction program, (Interax-AI), which will automatically indicate the medication prescriptions that involve a risk for the patient. All outpatient indications followed by physicians using the DrApp electronic history will be registered. The indications will be compared in the 4 months prior to the incorporation of the Interax-AI program with the 4 months after the incorporation of the program. Between both stages a period of 2 weeks will be established in which the data will not be recorded. The minimum & maximum number of patients that will be included in each stage are 100 & 200. The primary end point is to compare the total number of indications per inpatient, before the availability of the Interax-AI program and after the application of this program. The objective is to evaluate if the computer program of detection of drug interactions allows to limit the polypharmacy in outpatients.

Interventions

DEVICEInterax-AI

Interax-AI is a drug interactions detection module for the electronic clinical history software DrApp

Sponsors

DrApp S.A.
CollaboratorUNKNOWN
University of Buenos Aires
Lead SponsorOTHER

Study design

Allocation
NON_RANDOMIZED
Intervention model
SEQUENTIAL
Primary purpose
PREVENTION
Masking
QUADRUPLE (Subject, Caregiver, Investigator, Outcomes Assessor)

Intervention model description

Allocation: Non-Randomized An electronic medical record, DrApp, will be used, which will include a drug interaction program, Interax-AI, which will automatically indicate the medication prescriptions that involve a risk for the patient. All indications of each outpatient will be registered. The indications will be compared in the 4 months prior to the incorporation of the Interax-AI program with the 4 months after the incorporation of the program. Between both stages a period of 2 weeks will be established in which the data will not be recorded. The minimum number of patients that will be included in each stage is 100 and the maximum 200. Masking: Triple (Participant, Care Provider, Investigator) Primary Purpose: Prevention

Eligibility

Sex/Gender
ALL
Age
18 Years to No maximum
Healthy volunteers
No

Inclusion criteria

* Outpatients followed in outpatient clinics of doctors using the electronic medical record application DrApp

Exclusion criteria

* Lack of registration of medications used by the patient in the DrApp application

Design outcomes

Primary

MeasureTime frameDescription
Prevalence of polypharmacy cases detected in outpatients of outpatient clinics of doctors using the electronic medical record application DrApp1 yearThrough the electronic medical record called DrApp, the quantity of medicines prescribed to each patient is quantified and used for calculation of polypharmacy prevalence in tha basal period (pre-introduction of Interax-AI) and late period (Post introduction of Interax-AI).

Secondary

MeasureTime frameDescription
Interax-AI associated change in the number of total prescribed drug per patient1 yearChange in total prescribed drug per patient will be calculated as the differene between basal total prescribed drug per patient (Pre-Interax-AI) minus resulting total prescribed drug per patient (Post-Interax-AI)
Number of total drug interactions per patient and subclassification by severity (in post-Interax-AI period).1 yearThe addition of the application called Interax-AI, will allow detecting the presence of drug interactions and their severity in the second phase. These will be reported as the total number of interactions reported per patient, and subclassificated into number of mild (no need to take action), moderate (require patient monitoring), and severe (possible contraindication) interactions detected per patient.
Difference between Number of total drug interactions per patient in the local environment with those reported in the literature at the international level.1 yearThe difference will be calculated as the number of total drug interactions per patient minus the value (number of drug interaction per patient) reported in the bibliography at international level in similar populations.

Countries

Argentina

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026