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Use and Usefulness of Risk Prediction Tools in Urologic Surgery: Current State and Path Forward.

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Risk prediction tools (RPTs) show limited use and helpfulness among urologists, who often rely on intuition. Addressing these barriers is key for artificial intelligence (AI) to improve surgical decision-making and patient outcomes.

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Area of Science:

  • Surgical Decision-Making
  • Medical Informatics
  • Urology

Background:

  • Artificial intelligence (AI) holds promise for enhancing surgical decision-making.
  • Previous risk prediction tools (RPTs) have had limited impact, necessitating an evaluation of their role and user attitudes.
  • Understanding urologists' perspectives is crucial for developing effective AI-powered surgical tools.

Purpose of the Study:

  • To evaluate the current use, helpfulness, and trust in risk prediction tools (RPTs) among urologists.
  • To explore urologists' attitudes towards RPTs and their integration into surgical decision-making.
  • To identify barriers limiting the broader adoption of RPTs and inform future AI development.

Main Methods:

  • A national mixed-methods study employing a sequential explanatory design.
  • Survey of 2081 urologic surgeons (weighted sample 12,366) on RPT use, helpfulness, and trust via the 2019 AUA Census.
  • Qualitative interviews with 25 participants, followed by coding-based thematic analysis integrated with survey data.

Main Results:

  • Only 30.4% of urologists routinely used RPTs, with 34.3% finding them helpful.
  • Urologists with more years in practice showed less RPT use, helpfulness, and trust (P < .001).
  • Qualitative findings revealed reliance on intuition and gist-based approximations over numerical RPT data; methodological and operational challenges hinder RPT adoption.

Conclusions:

  • Risk prediction tools (RPTs) have limited impact due to the intuitive nature of surgical decision-making and implementation challenges.
  • Addressing both cognitive and practical barriers is essential for AI to fulfill its potential in improving surgical care.
  • Future AI tools must overcome these limitations to enhance surgical decision-making and patient outcomes.