Predictive Analytics in Hemodialysis: Enabling Precision Care for Patient with ESKD

点击翻译按钮获取中文摘要

Machine learning (ML) based clinical prediction models (CPMs) have proliferated over the past few years, becoming a central component of healthcare. These tools show great promise in informing both providers and patients of impending health outcomes, ultimately allowing for greater personalization of patient care. In our parent R01 we are developing a ML based mortality prediction model for patients undergoing hemodialysis. The goal of this tool is to predict both short and long term mortality risk in order to promote shared decision making between patients and providers. Importantly, there are a number of ethical concerns inherent in ML based CPMs. These include consideration of ethical performance (i.e., algorithmic bias), ethical usage (i.e., ensuring outputs are properly interpreted) and ethical implementation (i.e., the tool is properly integrated into the clinical workflow. For this current NOSI we propose to explore questions of ethical usage of ML based CPMs. To do this, we will conduct focus group of patients & their caregivers, providers and data scientists. We will address questions of trust in ML based CPMs, understanding of risk and optimal risk communication, interaction with CPMs and AI/ML tools and usage of CPMs to empower patients and promote shared decision-making. We will compare responses across constituencies. We will use our findings to develop practice oriented guidances that target both develops and users of ML based CPMs.