Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

SBAR II: Application of SBAR

SBAR II: Application of SBAR

SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by

Same author

Predicting the Development of BCG-unresponsive Disease Among Patients with High-grade Non-muscle-invasive Bladder Cancer: An International Multicenter Analysis.

European urology oncology·2026

Same author

Surrogacy of Intermediate Clinical Endpoints for Overall Survival in Patients With Localized Muscle-Invasive Bladder Cancer.

Journal of the National Comprehensive Cancer Network : JNCCN·2026

Same author

Paediatric penile length: a systematic review and meta-analysis.

BJU international·2026

Same author

Expect the Unexpected: Frequency, Predictors, and Survival Impact of Pathological Upstaging from Non-Muscle-Invasive to Muscle-Invasive Bladder Cancer Following Radical Cystectomy.

Cancers·2026

Same author

Beyond the BCG Paradox: Biological Rationale and Clinical Evidence for Combining Intravesical BCG with Immune Checkpoint Inhibitors in High-Risk Non-muscle-Invasive Bladder Cancer.

Oncology and therapy·2026

Same author

Nephroureterectomy for upper tract urothelial carcinoma recurrence in bladder cancer patients treated with radical cystectomy: a multicentric propensity score matched analysis on predictors, practice patterns and survival outcomes.

World journal of urology·2026

Same journal

Editorial Commentary.

Urology practice·2026

Same journal

Reply by authors.

Urology practice·2026

Same journal

The Impact of Patient-Provider Relationships on Prostate Cancer Screening: A Cross-Sectional Analysis of the All of Us Research Program.

Urology practice·2026

Same journal

Adjuvant Radiation Therapy Versus Initial Observation for Patients After Prostatectomy with Adverse Pathology: Have Findings from 3 Randomized Clinical Trials Influenced Clinical Practice?

Urology practice·2026

Same journal

National Patterns and Predictors of Perioperative Intravesical Chemotherapy Use Following Nephroureterectomy for Upper Tract Urothelial Carcinoma.

Urology practice·2026

Same journal

Patient Trust and Medical Mistrust Toward Overactive Bladder Therapies: General Consumer Awareness and Perceptions.

Urology practice·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 16, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

Assessing Artificial Intelligence-Generated Responses to Urology Patient In-Basket Messages.

Michael Scott¹, Wade Muncey², Nicolas Seranio¹

¹Department of Urology, Stanford University School of Medicine, Palo Alto, California.

Urology Practice

|August 20, 2024

Summary

This summary is machine-generated.

ChatGPT can generate acceptable responses to nearly half of urology patient messages, performing better on simpler inquiries. This artificial intelligence tool shows promise in reducing physician workload and improving wellness.

Keywords:

artificial intelligence electronic medical record quality improvement urology

More Related Videos

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Related Experiment Videos

Last Updated: Jun 16, 2025

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

Published on: April 12, 2021

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

E-Patient Counseling Trial E-PACO: Computer Based Education versus Nurse Counseling for Patients to Prepare for Colonoscopy

Published on: August 1, 2019

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Urology
Artificial Intelligence
Medical Informatics

Background:

Electronic patient messaging is increasingly used, contributing to physician burnout.
Large language models like ChatGPT can generate human-like text responses.

Purpose of the Study:

To evaluate the quality of ChatGPT responses to real-world urology patient messages.
To assess the suitability of AI-generated responses for patient communication.

Main Methods:

100 urology patient messages were collected and categorized.
ChatGPT generated responses for each message.
5 urologists evaluated responses for accuracy, completeness, harmfulness, helpfulness, and intelligibleness on a 5-point Likert scale.

Main Results:

47% of ChatGPT responses were deemed acceptable for patient use.
Responses to easy questions (56% acceptable) were superior to those for difficult questions (34% acceptable).
Response quality correlated with question difficulty but not content type.

Conclusions:

ChatGPT is a viable tool for assisting with patient messages, particularly for less complex queries.
Utilizing AI can potentially alleviate care team time burdens and enhance provider wellness.
Advancements in generative AI are expected to further improve performance.