Communication intelligence for healthcare
Clinicians and trainees talk through realistic cases out loud with AI patients. Each clinical simulation is scored against your standard, so every person sees what they did well and what to practice next. It's built for anyone who talks to a patient, from residency programs to the front desk.
More than 25 academic medical centers and children's hospitals are piloting ClinicalSim.
A learner talks a hesitant parent through a two-month vaccine visit. The report then scores the conversation and quotes the lines that earned each score.
Participants practice with AI patients and get feedback based on your communication standard.
Share your service model, consent or disclosure policy, debrief framework, or rubric. We'll review which parts we can assess in a simulated conversation before participants begin.
Choose who'll take part and which conversation they'll practice. Each participant speaks with an AI patient from their own device, with no observer in the room.
Each participant sees what they did well and what to practice next, with quotes from their conversation supporting each score. Leaders can review results across the group and choose cases for further practice. Formative scores stay out of personnel files.
What one health system learned
In one health system pilot, participants consistently opened conversations well. The report identified three elements to reinforce at the close: thanking the patient for raising a concern, reflecting the concern back, and summarizing the plan. Leaders could see those patterns in the transcripts and use the same cases for focused practice.
Bring the policy, service model, script, or rubric your institution already teaches. ClinicalSim scores the conversation element by element, as you wrote it.
Policy, service model, script, or rubric
We review which elements a spoken conversation can show before the assessment begins.
Start with a case built on a named, published framework that health care or medical education already uses.
Either way, every score quotes the participant's own words, so participants and faculty can inspect the feedback.
Conversations participants can practice
ClinicalSim turns practice in consent, disclosure, debriefing, and patient service into feedback for each participant and measurable patterns for leaders. Each simulation uses your own service standard or policy, with the participant's words behind every score. It works alongside the healthcare simulation your education team already runs, so people can practice by voice from any device between scheduled sessions.
Learners get repeatable practice between scheduled SP encounters. Faculty get rubric-scored feedback with the transcript behind every score, so they can inspect any case rather than trust a number. Your program keeps its medical simulation and SP sessions, and learners add simulation training on communication whenever they need it, not only when an SP is booked.
I just tried it out and it was like talking to a real patient.
Faculty, Johns Hopkins University School of Medicine
It was helpful to have time to think and reflect without feeling the pressure of a person across from you expecting a response.
Clinician, Pilot Study Participant
I had to tell a patient's father that his daughter would need long-term oxygen support through a tracheostomy. Before speaking with him, I practiced the conversation on ClinicalSim. It gave me confidence and feedback on my tone and delivery. Having difficult conversations can be extremely stressful, but having the practice beforehand allowed me to give appropriate time for silence and empathy.
Nurse practitioner, major health system
A resident's reflection, weeks after a practice simulation
Rather than feeling compelled to immediately define "goals of care" or guide the conversation toward a particular medical decision, I was able to slow down and truly listen.
As I stood beside the interpreter, waiting for them to translate our response to the many fears this mother had just shared, I was struck by how familiar the conversation felt. Just a few weeks earlier, I had participated in a remarkably similar conversation—but that one did not involve a real mother or a real child suffering from a life-threatening illness. Rather, it was an AI-generated clinical simulation. The simulation, along with the feedback that followed, had prepared me to approach this encounter differently. I found myself more attuned not only to listening to this mother's fears, worries, and hopes, but also to validating and holding space for them. Rather than feeling compelled to immediately define "goals of care" or guide the conversation toward a particular medical decision, I was able to slow down and truly listen. I focused on understanding what a meaningful life looked like for her daughter and what mattered most to her as a mother—even when those priorities might not have aligned with what I would have initially considered the first-line "medical" recommendation.
In reflecting, I realized the value of ClinicalSim extended far beyond practicing communication skills in a simulated environment. It had changed the way I approached a real, difficult conversation with a family and overall strengthened my ability to better listen to, support, and advocate for families navigating profoundly difficult circumstances.
