Verbi for Medical English: Patient Communication Practice for Clinicians

Internationally trained doctors and nurses who pass clinical licensing exams often still struggle with the spoken, patient-facing English that real practice demands: explaining a diagnosis in plain terms, giving discharge instructions a patient will actually follow, breaking bad news, and navigating accent and idiom gaps during a consult. General language tools and even business English products do not drill the specific scripts of clinical communication, and classroom coaching is expensive and hard to schedule around hospital shifts. Verbi for Medical English is an AI conversation tutor that targets exactly this gap. The product simulates realistic patient dialogues and discharge-instruction scenarios through voice, scores the clinician on clarity, empathy phrasing, and medical accuracy, and gives targeted feedback they can practice between shifts. The MVP is a web and mobile voice app with a library of scripted patient scenarios (chronic disease counseling, medication instructions, pre-op consent, pediatric caregiver guidance) plus an AI patient persona the clinician talks to out loud, followed by a transcript with correction and rephrasing suggestions. Revenue comes from B2B seat licenses sold to hospitals, staffing agencies, and nursing recruitment pipelines that onboard overseas-trained staff, with a prosumer self-serve tier for individual clinicians preparing for relocation. Go-to-market starts bottom-up with individual clinicians found through licensing-prep and relocation communities, then converts to institutional deals via the agencies and hospital onboarding teams that already pay to retain international hires. Growth loops: clinicians who improve share with peers in the same cohort, hospitals standardize it into onboarding, and recorded scenario completion becomes a credential clinicians can show employers.