A retrospective evaluation of treatment decision making for thyroid cancer using clinical decision support in Brazil.

Abstract

e19193 Background: Artificial intelligence-driven clinical decision-support systems such as Watson for Oncology (WfO) may aid cancer care in economically challenged health systems. Evidence of the applicability of such tools in resource-constrained settings is limited. The study objective was to evaluate treatment agreement between physician-prescribed therapy and WfO recommended treatment options in thyroid cancer in Brazil. An in-depth evaluation of discordant cases by a blinded expert panel of medical oncologists and cancer surgeons was performed to identify preferred therapies and predictors of discordance. Methods: Thyroid cancer patients treated at the Instituto do Câncer do Ceará, Brazil from July 2018 to June 2019, but not processed in WfO, were selected for entry into WfO in January 2020. Blinded to treatment-plan source (i.e., WfO or historical), the expert panel reviewed all WfO therapeutic options and historical physician-prescribed treatment plans for discordant cases and selected their preferred treatment options. Clinical and demographic characteristics were analyzed using logistic regression. Results: Thyroid cancer patients (n = 83) evaluated for concordance between WfO therapeutic options and historical treatments were mostly female (91%) and between the ages of 18 - 78 years (mean 47.7). Concordance between historical physician-prescribed treatment decisions and WfO was 73.5% (61/83). Demographics and clinical characteristics associated with discordance are shown in Table. For all discordant cases (n = 22), preferred treatment decisions, as determined by the expert panel, were in agreement with WfO. Conclusions: High concordance between WfO recommended treatment options and historical treatment decisions for thyroid cancer was observed at Instituto do Câncer do Ceará. For discordant cases, a blinded expert panel agreed with WfO recommended treatment options in all cases, demonstrating there may be a role for decision support in aiding individual oncologists to make best-practice and evidence-informed treatment decisions. [Table: see text]