Validation and implementation of a mobile app decision support system for quality assurance of tumor boards. Analyzing the concordance rates for prostate cancer from a multidisciplinary tumor board of a University Cancer Center

Abstract

Abstract Background Certified Cancer Centers must present all patients in multidisciplinary tumor boards (MTD), including standard cases with well-established treatment strategies. Too many standard cases can absorb much of the available time, which can be unfavorable for the discussion of complex cases. In any case, this leads to a high quantity, but not necessarily a high quality of tumor boards Objective Our aim was to develop a partially algorithm-driven decision support system (DSS) for smart phones to provide evidence-based recommendations for first-line therapy of common urological cancers. To assure quality, we compared each single digital decision with recommendations of an experienced MTD and obtained the concordance. Design, setting and participants 1873 prostate cancer patients presented in the MTD of the urological department of the University Hospital of Cologne from 2014 to 2018 have been evaluated. Outcome measurements and statistical analysis Patient characteristics included age, disease stage, Gleason Score, PSA and previous therapies. The questions addressed to MTD were again answered using DSS. All blinded pairs of answers were assessed for discrepancies by independent reviewers. Results and limitations Overall concordance rate was 99.1% (1856/1873). Stage specific concordance rates were 97.4% (stage I), 99.2% (stage II), 100% (stage III), and 99.2% (stage IV.Quality of concordance were independent of age and risk profile. Conclusions The reliability of any DSS is the key feature before implementation in clinical routine. Although our system appears to provide this safety, we are now performing cross-validation with several clinics to further increase decision quality and avoid potential clinic bias. Patient summary The quality of therapeutic decisions provided in tumor boards is perhaps the most relevant criterion for optimal cancer outcome. This tool aims to provide optimal recommendations, to assess the quality on a case-by-case basis and furthermore to objectively display the quality of oncological care. Author summary Everyday clinicians face the difficult task to choose the optimal treatment for their cancer patients due to the emergence of newly available therapeutics and continuously altering treatment guidelines. The resulting flood of information is impossible for clinicians to keep up with. Therefore, clinicians decide as a team, in so called tumor boards, upon the best possible cancer treatment for each patient. Even though the treatment decisions recommended by tumor boards play a critical role for the long-term survival of cancer patients, their accuracy in decision-making has hardly ever been assessed. Unfortunately, current digital tools that have been developed to support clinicians on the process of decision-making, have failed to provide treatment recommendations with sufficient accuracy. Therefore, we evaluated the quality of a novel decision-making application by comparing the decision concordance generated by the App with therapeutic recommendations given by a tumor board of a University Cancer Center. For newly diagnosed cancer patients we found that the novel tool matched the decisions made by the tumor board in almost 100% of the cases. These promising results not only show the potential providing digital support for patient care, but also provide objective quality management while saving board time in favor of discussing more complex cases.

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