AI-Powered Interactive Dashboard: Using Machine Learning and Visual Analytics for Non-Cardiac Surgery Decision Support

Abstract

Clinical decision-making is often complex and time-consuming due to the large amount of required data, contributing to an increase in mortality rates, particularly in non-cardiac surgery cases. To address this problem, we present an interactive artificial intelligence (AI) dashboard that combines visual analytics (VA) and machine learning (ML) to facilitate quick decision-making and enhance patient care. Visual analytics makes use of human perceptual and cognitive capabilities to quickly process complex data for decision-making. Machine learning is used to predict patients' states for quick decision-making. Though a lot of studies have emerged focusing on the application of VA for patient health care, such as diabetes and infectious diseases, little is known about its application to non-cardiac surgery. In this paper, we harness the capabilities of VA and ML to develop an interactive intelligent dashboard to enhance decision-making for non-cardiac surgery patients. This paper presents the design, development, and initial results of assessing the usability and usefulness of the dashboard with HCI experts and medical doctors. The results showed that users found the dashboard usable and useful. The qualitative analysis revealed six key themes, including the system's role in improving healthcare navigation and equity, its impact on patient-centered care delivery, and the ethical implications of predictive analytics in healthcare. We present our findings along with study limitations and future research directions.

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