Natural Language Processing-Based Feedback to Improve Physician Risk Communication and Informed Shared Decision Making in Men With Clinically Localized Prostate Cancer

Summary

The purpose of the research is to assess the impact of a natural language processing + artificial intelligence (NLP+AI)-based risk communication feedback system to improve quality of risk communication of key tradeoffs during prostate cancer consultations among physicians and to improve patient decision making. In this cluster randomized trial, an evaluable 259 patients with newly diagnosed clinically localized prostate cancer will be cluster randomized within an evaluable 24 physicians to: 1. a control arm, in which patients will receive standard of care treatment consultations along with AUA-endorsed educational materials on treatment risks and benefits (for patients) and on SDM (for physicians) or 2. an experimental arm, in which patients and participating physicians will receive NLP+AI-based feedback on what was said about key tradeoffs within approximately 72 hours of the consultation to assist with decision making. Physicians will additionally be provided with grading of their risk communication for each visit based on an a priori defined framework for quality of risk communication and recommendations for improvement. In both study arms, there will be an audio-recorded follow-up phone or video call between the physician and patient to allow for further discussion of risk and clarifying any areas of ambiguity, which will be qualitatively analyzed to see if areas of poor communication were rectified. After the follow-up phone call, patients and participating physicians will be asked to complete a very brief survey about their experience. The study plans to test whether receiving NLP+AI-based feedback improves decisional conflict, shared decision making, and appropriateness of treatment choice over the standard of care in patients undergoing treatment consultations for prostate cancer. Study staff will also test whether providing feedback and grading of risk communication to physicians affects quality of physician risk communication, since providing feedback will promote more accountability for the quality of information provided to patients. The study will also analyze data from the control arm of the randomized controlled trial to understand variation in risk communication of key tradeoffs in relevant subgroups of tumor risk (low-, intermediate-, and high-risk), provider specialty (Urology, Radiation Oncology, Medical Oncology), and patient sociodemographics.

Eligibility

Age range

18+ years

Sex

Male

Healthy volunteers

No

Interventions

• Other
  NLP+AI Report

  A NLP model will extract key content from consultations and AI (Chat GPT) will summarize that information. Reports including the top sentences by NLP probability for key content areas will be generated and will be provided to patients and providers within approximately 72 hours after each case. In both arms, a follow up phone call will allow for clarification any concepts that was inadequately communicated during the consultation. This call will be audio recorded and qualitatively assessed to determine whether deficiencies in risk communication observed in the consultation were rectified.

Location