Clinical Decision Support for Surgery: A Mixed Methods Study on Design and Implementation Perspectives From Urologists.

Objective: To assess urologist attitudes toward clinical decision support (CDS) embedded into the electronic health record (EHR) and define design needs to facilitate implementation and impact. With recent advances in big data and artificial intelligence (AI), enthusiasm for personalized, data-driven tools to improve surgical decision-making has grown, but the impact of current tools remains limited.

Methods: A sequential explanatory mixed methods study from 2019 to 2020 was performed.

Patterns of multispecialty care for low- and intermediate-risk prostate cancer in the use of active surveillance.

Background: Multidisciplinary models of care have been advocated for prostate cancer (PC) to promote shared decision-making and facilitate quality care. Yet, how this model applies to low-risk disease where the preferred management is expectant remains unclear. Accordingly, we examined recent practice patterns in specialty visits for low/intermediate-risk PC and resultant use of active surveillance (AS).

Use of expectant management based on prostate cancer risk and health status: How far are we from a risk-adapted approach?

Objectives: While active surveillance, a form of expectant management (EM), is preferred for patients with low-risk prostate cancer (PCa), some favor a more risk-adapted approach that recognizes patient preferences and condition-specific factors. However, previous research has shown non-patient-related factors often drive PCa treatment. In this context, we characterized trends in AS with respect to disease risk and health status.

Patient Reported vs Claims Based Measures of Health for Modeling Life Expectancy in Men with Prostate Cancer.

Purpose: Life expectancy has become a core consideration in prostate cancer care. While multiple prediction tools exist to support decision making, their discriminative ability remains modest, which hampers usage and utility. We examined whether combining patient reported and claims based health measures into prediction models improves performance.