Initial experience with AI Pathway Companion: Evaluation of dashboard-enhanced clinical decision making in prostate cancer screening

Abstract

Purpose Rising complexity of patients and the consideration of heterogeneous information from various IT systems challenge the decision-making process of urological oncologists. Siemens AI Pathway Companion is a decision support tool that provides physicians with comprehensive patient information from various systems. In the present study, we examined the impact of providing organized patient information in comprehensive dashboards on information quality, effectiveness, and satisfaction of physicians in the clinical decision-making process. Methods Ten urologists in our department performed the entire diagnostic workup to treatment decision for 10 patients in the prostate cancer screening setting. Expenditure of time, information quality, and user satisfaction during the decision-making process with AI Pathway Companion were recorded and compared to the current workflow. Results A significant reduction in the physician’s expenditure of time for the decision-making process by -59.9% (p < 0,001) was found using the software. System usage showed a high positive effect on evaluated information quality parameters completeness (Cohen’s d of 2.36), format (6.15), understandability (2.64), as well as user satisfaction (4.94). Conclusion The software demonstrated that comprehensive organization of information improves physician’s effectiveness and satisfaction in the clinical decision-making process. Further development is needed to map more complex patient pathways, such as the follow-up treatment of prostate cancer.

Clinical study of multifactorial diagnosis in prostate biopsy

“…The emergence of AI has the potential to solve some of the challenges associated with the interpretation of mpMRI scans for PCa. [13][14][15]24 Additionally, by using a large data set in our study, we will be able to increase the statistical power and generalizability of our findings.…”

“…The emergence of AI has the potential to solve some of the challenges associated with the interpretation of mpMRI scans for PCa 13–15,24 . AI‐based systems can be used to automatically identify suspicious lesions within the prostate gland, reducing the need for radiologists to manually scan through large numbers of images and decreasing the risk of human error.…”

“…It can also improve the diagnostic ability of radiologists with less experience, bringing their accuracy up to the level of more experienced radiologists. Therefore, it is reasonable to believe that the integration of AI into clinical decision making is feasible, and integrating AI with clinical information may have the potential to improve the accuracy of pre‐biopsy diagnosis of PCa 14 …”

“…Therefore, it is reasonable to believe that the integration of AI into clinical decision making is feasible, and integrating AI with clinical information may have the potential to improve the accuracy of pre-biopsy diagnosis of PCa. 14 In this study, we investigated the feasibility of using an AI algorithm for the diagnosis of csPCa on mpMRI in combination with conventional clinical information.…”

Using a Clinical Data Warehouse to Calculate and Present Key Metrics for the Radiology Department: Implementation and Performance Evaluation

“…The values shown by them can be filtered, for example, by a specific type of examination, but no real queries on these data can be defined by the user. The graphics provided by Henkel et al [ 11 ] are limited to single patients and show, for example, the history of 1 of their laboratory values. Munbodh et al [ 12 ] also provide predefined graphics for the total number of examinations of different kinds during the past month.…”