Decisional Conflict Among Patients Newly Diagnosed With Clinical T1 Renal Masses: A Prospective Study.

PURPOSE: Because multiple management options exist for clinical T1 renal masses, patients may experience a state of uncertainty about the course of action to pursue (ie, decisional conflict). To better support patients, we examined patient, clinical, and decision-making factors associated with decisional conflict among patients newly diagnosed with clinical T1 renal masses suspicious for kidney cancer. MATERIALS AND METHODS: From a prospective clinical trial, participants completed the Decisional Conflict Scale (DCS), scored 0 to 100 with < 25 associated with implementing decisions, at 2 time points during the initial decision-making period. The trial further characterized patient demographics, health status, tumor burden, and patient-centered communication, while a subcohort completed additional questionnaires on decision-making. Associations of patient, clinical, and decision-making factors with DCS scores were evaluated using generalized estimating equations to account for repeated measures per patient. RESULTS: Of 274 enrollees, 250 completed a DCS survey; 74% had masses ≤ 4 cm in size, while 11% had high-complexity tumors. Model-based estimated mean DCS score across both time points was 17.6 (95% CI 16.0-19.3), though 50% reported a DCS score ≥ 25 at least once. On multivariable analysis, DCS scores increased with age (+2.64, 95% CI 1.04-4.23), high- vs low-complexity tumors (+6.50, 95% CI 0.35-12.65), and cystic vs solid masses (+9.78, 95% CI 5.27-14.28). Among decision-making factors, DCS scores decreased with higher self-efficacy (-3.31, 95% CI -5.77 to -0.86]) and information-seeking behavior (-4.44, 95% CI -7.32 to -1.56). DCS scores decreased with higher patient-centered communication scores (-8.89, 95% CI -11.85 to -5.94). CONCLUSIONS: In addition to patient and clinical factors, decision-making factors and patient-centered communication relate with decisional conflict, highlighting potential avenues to better support patient decision-making for clinical T1 renal masses.

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. First, survey responses from the 2019 American Urological Association Annual Census evaluated attitudes toward an automatic CDS tool that would display risk/benefit data. This was followed by the purposeful sampling of 25 urologists and qualitative interviews assessing perspectives on CDS impact and design needs. Bivariable, multivariable, and coding-based thematic analysis were applied and integrated. RESULTS: Among a weighted sample of 12,366 practicing urologists, the majority agreed CDS would help decision-making (70.9%, 95% CI 68.7%-73.2%), aid patient counseling (78.5%, 95% CI 76.5%-80.5%), save time (58.1%, 95% CI 55.7%-60.5%), and improve patient outcomes (42.9%, 95% CI 40.5%-45.4%). More years in practice was negatively associated with agreement (P <.001). Urologists described how CDS could bolster evidence-based care, personalized medicine, resource utilization, and patient experience. They also identified multiple implementation barriers and provided suggestions on form, functionality, and visual design to improve usefulness and ease of use. CONCLUSION: Urologists have favorable attitudes toward the potential for clinical decision support in the EHR. Smart design will be critical to ensure effective implementation and impact.