Chiba University Venture OPQRST Raises ¥1,013,000 in Crowdfunding for AI Interview Training "OPQRST Nurse" for Nursing Education - Official Version to be Released July 2, 2026 -

OPQRST Inc. (Headquarters: Shibuya-ku, Tokyo; President and CEO: Ikutaka Yanagida) announced that its crowdfunding project "Developing AI Interview Training 'OPQRST Nurse' to Enhance Interview Skills for Nursing Students and Nurses" on CAMPFIRE successfully raised ¥1,013,000, exceeding its target of ¥1,000,000, with 52 supporters (101% of the goal). Crowdfunding Page: https://camp-fire.jp/projects/919369/view

In conjunction with this achievement, the official version of the AI interview training service "OPQRST Nurse" for nursing education will be released on July 2, 2026 (Thu). The official version will provide an environment where nursing students and new nurses can practice pre-interviewing and handover reporting 24/7, as many times as they wish, focusing on two scenarios: outpatient pre-interviewing and ward handover.

OPQRST Nurse Top Screen

OPQRST Nurse

Background of Development: A Safe Place to Practice Before Entering the Field

For nursing students and new nurses, tasks such as pre-interviewing patients, reporting to senior nurses, and reporting to doctors require not only knowledge but also the ability to "organize information within a limited time and communicate it effectively." However, in actual clinical settings, it is often difficult to secure sufficient practice opportunities, and first-time encounters can become the actual practice.

In our development, we interviewed 20 active nurses, and 65% responded that they desired "communication practice opportunities" during their early careers. Based on these voices, OPQRST Nurse was developed as a service that allows learners to repeatedly practice pre-interviewing and handover reporting while reducing psychological burden.

Overview of OPQRST Nurse

OPQRST Nurse is an AI training service developed specifically for nursing education, based on the knowledge gained from the AI interview service "OPQRST" for physicians. All cases are virtual cases created for learning purposes, and through dialogue with AI, learners can cultivate their ability to elicit necessary information from patients and organize and report the obtained information.

Outpatient Pre-interview Practice: Supports 30 chief complaints commonly encountered in outpatient settings, including difficulty breathing, fever, dizziness, insomnia, loss of appetite, fatigue, chest pain, and abdominal pain.

Ward Handover Practice: Supports 20 symptoms that lead to reporting and handover in wards, including fever, difficulty breathing, chest pain, impaired consciousness, falls, convulsions, and anaphylaxis.

Real-time AI Feedback: Based on the dialogue content, learners can review deficiencies in information gathering and reporting.

Reduced Instructor Burden: Dialogue history can be saved, allowing for use in self-study and review by learners, as well as instruction by educators.

Outpatient Pre-interview Case Selection Screen (Official Version) Ward Handover Case Selection Screen (Official Version)

Outpatient Pre-interview: Master the Interview Flow Through Dialogue with an AI Simulated Patient

In outpatient pre-interviews, learners will practice eliciting information such as chief complaint, history of present illness, accompanying symptoms, past medical history, medication status, and social background from an AI simulated patient. Since the patient's personality and background are also set, learners can practice with an awareness of the flow of dialogue closer to real-world situations, rather than just textbook question-and-answer.

Practice Screen for Pre-interviewing with an AI Simulated Patient in an Outpatient Setting

Practice Screen for Handover to an AI Nurse in a Ward Setting

Ward Handover: Organize and Convey Necessary Information According to SBAR

In ward handover practice, learners can practice organizing and conveying patient information to an AI nurse. Following the SBAR (Situation, Background, Assessment, Recommendation) framework, they can learn what to prioritize and what information is lacking.

Similar to actual clinical situations, learners are required to select and summarize necessary information in a way that is understandable to the listener, rather than reading out all the presented information. Through interaction with the AI, they can identify their own reporting habits and deficiencies and prepare for practice.

Pricing Plans and Getting Started

Crowdfunding supporters are being offered early access as a return. Additionally, anyone can experience a free demo by registering for free (Google account required). The official version will allow users to access outpatient pre-interview and ward handover training anytime, anywhere, and as many times as they wish through paid registration.

For individuals, we offer a Basic Plan primarily for outpatient pre-interview practice, and a Standard Plan that includes ward handover practice in addition to outpatient pre-interviews. Contract periods of 1 month, 3 months, and 1 year will be available, with discounts planned for 3-month and 1-year contracts. We aim for a pricing structure that is accessible to nursing students and new nurses.

For corporate plans, we accept individual consultations for implementation for educational purposes in high schools, vocational schools, universities, and hospitals. We envision its use in classes, pre-internship training, new nurse training, and in-hospital education.

Pricing Screen for Individual and Corporate Plans

Future Developments

OPQRST Nurse will continue to develop a learning environment that allows nursing students and new nurses to gradually improve their communication skills, starting with outpatient pre-interviews and ward handovers. In the future, we are considering expanding to various communication scenarios that nurses face in clinical practice, such as reporting to doctors, explaining to patient families, responding to emergencies, and discharge support.

Comment from Ikutaka Yanagida, President and CEO (Physician, Ph.D. in Medical Science)

"This challenge, which began with the desire to have had such a service when we were students, is now taking shape with the support of many people. We interpret the ¥1,013,000 in support we received this time not just as funds, but as an expectation that we will make the future of nursing education better.

Through OPQRST Nurse, we will support nursing students and new nurses in gaining sufficient practice before facing patients, enabling them to communicate with confidence. As a Chiba University venture, we will steadily implement new learning environments for medical education utilizing AI into society."