Presenting a conceptual model for decision support systems in infertility: A developmental study.

Sajjadi H, Choobineh H, Safdari R
International journal of reproductive biomedicine · 2025 · doi:10.18502/ijrm.v23i10.20316 · PMID:41438399 · PMC12703027
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This developmental study aimed to present a conceptual model for clinical decision support systems (DSS) in infertility in Iran by identifying required data elements and system features. The authors used a scoping-review style approach to search PubMed, Scopus, and Web of Science for English-language studies, supplemented system searches via Google/Yahoo/Bing and AI tools (ChatGPT, Gemini, Perplexity), and then obtained expert input from 32 infertility specialists via a 49-question, 5-domain Likert-scale questionnaire (Cronbach alpha 0.78; content validity ratio 0.60), accepting items with ≥70% agreement. They identified 71 potentially relevant systems, excluded implemented user systems, and ultimately included 10 studies plus 13 systems to extract categories including main DSS features, treatment-recommendation data, and prediction-module data for singles and couples, culminating in the conceptual model. A key limitation explicitly described is that evidence quality and bias were assessed using JBI guidelines even though scoping reviews do not require systematic critical appraisal. The paper does not explicitly discuss endometriosis or adenomyosis; it was included in the corpus via a keyword match in the upstream search index.

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Abstract

BackgroundInfertility is the inability to conceive after a year of trying, resulting in unintentional childlessness. A clinical decision-support system can enhance diagnosis, reduce costs, improve access, and increase treatment accuracy.ObjectiveThis study aimed to present a conceptual model for decision support systems in infertility.Materials and methodsThis developmental study, conducted from April-November 2024 in 3 steps. First, PubMed, Scopus, and Web of Science databases were investigated to identify data for decision support systems in infertility. Next, search engines like Google, Yahoo, and Bing, along with artificial intelligence tools such as ChatGPT, Gemini, and Perplexity, helped identify similar systems. Lastly, opinions from 32 infertility experts were collected via a researcher-made questionnaire, with reliability confirmed by Cronbach's alpha of 0.78 and validity confirmed by content validity ratio of 0.60.ResultsIn the first step, 16,310 articles were identified; 10 were selected after removing duplicates and applying inclusion and exclusion criteria. In the second step, 71 relevant systems were identified in search engines; 58 were excluded, leaving 13 for further analysis. In the third step, a researcher-designed questionnaire was distributed to 32 experts, yielding key agreement rates of 94% for monitoring and follow-up, 94% for sperm analysis data, 90% for abortion data, and 82.5% for infertility information from health magazines. Requirements grouped into 4 categories: main features (10 elements), patient info management (19 elements), fertility prediction data (16 elements), and secondary features (3 elements). The model's overall agreement was 85%.ConclusionDeveloping a decision-support system for infertility could enhance clinical care and outcomes; however, challenges include standardizing validation methods and considering ethnic diversity.
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H. Sajjadi, H. Choobineh, and R. Safdari: Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work. Drafting the work or reviewing it critically for important intellectual content. Final approval of the version to be published, agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

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The authors declare that there is no conflict of interest.

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