Simulation can preserve affirming care practice as exposure shrinks

Researchers have documented a practical problem in affirming care education: learners may recognize that the work matters without getting enough clinical exposure to practice it. Sixty percent of surveyed residency program directors reported no rotation with direct clinical exposure to transgender patients (Baylor University Medical Center Proceedings, 2023).

A team presenting at IPSS Rome designed an AI patient scenario to give pediatric residents structured communication practice when clinical exposure is limited. The work focuses on training access, not on replacing patients, standardized patients, or faculty.

The training gap is measurable

A multicenter cross-sectional study of OB-GYN and family medicine residents found that 62.4% had received no health education tailored to transgender patients. In the same study, 68.3% reported inadequate knowledge of the clinical implications of gender affirming hormone therapy, and 74.1% reported inadequate knowledge of gender affirming surgeries (Healthcare, 2025).

An internal medicine study found that 97% of residents considered transgender medical issues relevant to their practice, while 45% reported prior education on the topic (Transgender Health, 2017). These samples do not describe every residency program, but they identify a consistent mismatch between expected clinical responsibility and available training.

Communication requires spoken practice

A lecture can introduce terminology and clinical principles. It cannot show whether a learner can respond when a patient corrects an assumption, a parent asks a hostile question, or a conversation becomes emotionally charged.

Standardized patients remain important because human presence and judgment matter. AI patients can add private, repeatable practice between those encounters. A learner can try the conversation, review feedback, and return to an educator with specific questions.

The IPSS Rome scenario

Gillian Brennan, MB BCh BAO, Vinod Havalad, MD, and colleagues developed a pediatric AI patient scenario for structured communication practice. The case gives residents a defined situation, a named communication framework, and feedback tied to the transcript.

The presentation describes a simulation design for a training gap. It does not establish effects on patient care, and the practice report does not replace faculty judgment.

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