Grant Number:	5R01CA290559-02 Interpret this number
Primary Investigator:	Daskivich, Timothy
Organization:	Cedars-Sinai Medical Center
Project Title:	Natural Language Processing Based Consultation Feedback to Improve Patient Decision Making and Physician Risk Communication
Fiscal Year:	2026

PROJECT SUMMARY Prostate cancer is a highly prevalent disease requiring complex decision making due to its multiple treatment options, each with tradeoffs of survival and sexual and urinary function. As a result, shared decision making (SDM)—a decision-making process where physicians collaborate with patients to select the option that best matches their values and preferences—is the standard of care for treatment decision making. For SDM to be effective, physicians must clearly communicate risks and rewards of treatment options, and patients must retain that information so that they can weigh these tradeoffs when making a treatment decision. Yet, in pilot work, we found that physicians often fail to adequately communicate tradeoffs to men with prostate cancer. Furthermore, patients often feel that they do not have enough knowledge to be fully informed participants in SDM. We recently developed a highly accurate natural language processing (NLP)-based feedback system capable of delivering point-of-care, real-time feedback to patients and providers about what was said about key risks and rewards of treatment options during prostate cancer treatment consultations that has potential to enhance both patient decision making and physician risk communication. At the patient level, receiving a summary of conversation about tradeoffs has the potential to reinforce awareness of key tradeoffs, enhance perception of risk of those tradeoffs, reduce decisional conflict, improve quality of SDM, and ultimately improve appropriateness of treatment choice. At the physician level, receiving feedback on what was said will provide awareness of the quality of their own communication and promote accountability for the quality of information. In this grant application, we propose a cluster randomized controlled trial to test whether providing NLP-based feedback with artificial intelligence (AI) summaries of NLP-extracted information on key tradeoffs to patients and physicians after prostate cancer treatment consultations improves patient decision making and physician risk communication compared to standard-of-care counseling. In Aims 1/2, patients will be randomized within clusters of physicians to: (1) a control arm, in which they will receive standard of care counseling along with American Urological Association-endorsed educational materials on treatment decision making (for patients) and SDM (for physicians) or (2) an experimental arm, in which patients and physicians will receive NLP+AI-based feedback on what was said about key tradeoffs during the consultation. A follow-up phone visit will permit further discussion. In Aim 3, we will analyze variation in quality of risk communication in subgroups of race, tumor risk, and provider specialty in the control arm to identify groups at risk for poor communication. We hypothesize that providing patients and physicians with key information extracted using the NLP system will improve decisional conflict, shared decision making, and appropriateness of treatment at the patient level and quality of risk communication at the physician level. We also hypothesize that risk communication will differ by race, tumor risk, and physician specialty, which will permit targeting of communication interventions to at risk groups in the future.




Development and validation of a natural language processing system to assess quality of physician communication in prostate cancer consultations.
Authors: Zheng R. , Friedrich N.A. , Luu M. , Gale R. , Khodyakov D. , Freedland S.J. , Spiegel B. , Daskivich T.J. .
Source: Prostate Cancer And Prostatic Diseases, 2025-08-21 00:00:00.0; , .
EPub date: 2025-08-21 00:00:00.0.
PMID: 40841832
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Scoring Physician Risk Communication in Prostate Cancer Using Large Language Models.
Authors: Lopez-Garcia G. , Xu D. , Luu M. , Zheng R. , Daskivich T.J. , Gonzalez-Hernandez G. .
Source: Medrxiv : The Preprint Server For Health Sciences, 2025-08-11 00:00:00.0; , .
EPub date: 2025-08-11 00:00:00.0.
PMID: 40832413
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