AI-Driven Model Impact on Patient Engagement in Medically Assisted Reproduction
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Artificial intelligence-Machine learning report with accurate personalized probabilities of having a live birth rate.
- Who it may be relevant to
- Registry conditions: Infertility (IVF Patients), Artificial Intelligence (AI). Basic parameters: 18 years — 45 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Portugal
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Official title
Assessing the Impact of an Artificial Intelligence-Machine Learning Model on Patient Engagement in Medically Assisted Reproduction
Overview
Infertility is a globally significant medical condition, profoundly impacting individuals and couples both emotionally and physically. The multifaceted nature of in vitro fertilization (IVF) treatment demands active patient participation, with engagement playing a pivotal role in treatment success and satisfaction. However, suboptimal engagement can lead to challenges such as not initiating treatment, missed appointments, medication errors, dropping out and heightened stress levels, all of which may adversely affect clinical outcomes. Recent advancements in Artificial Intelligence (AI) and Machine Learning (ML) have revolutionized healthcare, offering innovative solutions for personalized patient care. In IVF, AI-ML models hold the potential to enhance patient engagement by delivering tailored communication, reminders, and educational support, but also improved prognostication by providing personalized and accurate predictions of treatment outcomes. These capabilities enable patients to make more informed decisions and enhance their adherence to treatment protocols.This protocol outlines a prospective evaluation of an AI-ML model, specifically the Univfy PreIVF report, developed to improve patient engagement in IVF care. Recently, a retrospective, multicenter study reported improved IVF utilization rates among patients counselled using the Univfy PreIVF Report. The current study will prospectively assess the model's effectiveness in addressing individual patient needs and creating a supportive treatment environment. Specifically, this study will measure adherence to providers' recommendation of treatment protocols. By analyzing the impact of these interventions, this research aims to provide robust evidence for the integration of AI-ML technologies in reproductive medicine, paving the way for broader implementation and improved patient outcomes.
Interventions
- Other Artificial intelligence-Machine learning report with accurate personalized probabilities of having a live birth rate
Patients included in the prospective arm will receive the Univfy® PreIVF Report with their accurate personalized probabilities of having a live birth rate (Univfy®) together with a medical explanation by their physician
Primary outcome measures
- 9-month conversion rate [Time frame: From enrollment until 9 months after]
Secondary outcome measures (2)
- 3-month MAR conversion [Time frame: From enrollment until 3 months after]
- 6-month MAR conversion [Time frame: From enrollment until 6 month after]
Eligibility criteria
Inclusion criteria
- Infertile patients aged 18-45 years
- Patients willing to undergo Medically Assisted Reproduction (heterosexual couples, same-sex female couples and single females undergoing artificial insemination, IVF/ICSI or oocyte donation treatments)
Exclusion criteria
- Age >45 years
- Patients who are not candidates for IVF/ICSI
- Patients who are menopausal or peri-menopausal
- Patients undergoing Fertility Preservation
- Same-sex couples who will undergo reception of oocytes from partner.
- Patients who decline to be counselled about their probability of having a live birth from IVF/ICSI treatment
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: No
Study design
- Observational model
- Cohort
Study locations
Portugal · 1 center
- IVI-RMA Lisboa — Lisbon
Identifiers
NCT: NCT07087171 · 2412-LIS-233-AN