Transforming Kidney Care in the Emergency Department using Artificial Intelligence Driven Clinical Decision Support

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PROJECT SUMMARY/ABSTRACT The objective of this proposal is to determine best-practice methods for incorporating artificial intelligence (AI)- derived insights into emergency care. This investigation will use the iterative development and evaluation of an AI-driven clinical decision support (CDS) system to prevent or mitigate acute kidney injury (AKI) as a reference use-case. We are responding to the AHRQ Special Emphasis Notice: Health Services Research Priorities for Achieving a High Value Healthcare System (NOT-HS-18-015), calling for research on prevention of disease through incorporation of AI into healthcare and on interventions to prevent kidney disease progression. Emergency departments (EDs) deliver high-volume patient care in hazardous decision-making environments fraught with excessive cognitive loading and time-pressure. AI has potential to support ED clinician decisions by exploiting large-scale electronic health record (EHR) data to aid prognosis, extract signal from noise, and reduce untoward variability in practice. Despite AI’s fervent promotion, translation to practice is rare and means to incorporate AI that is trustworthy, transparent, and explainable in the ED are unknown. AKI is an important target for AI-driven predictive modeling. It is prevalent and strongly associated with adverse outcomes including dialysis and death, yet is under-recognized and therefore under-treated. In addition, many ED treatments inadvertently promote the progression of AKI and kidney disease. AKI prevention is achievable with evidence-based CDS at the point of care. We will use our AI-driven model, with proven capacity for early and reliable AKI risk estimation, to achieve the following Specific Aims: Aim 1: Develop an AI-driven algorithm for promotion of AKI-focused clinical decision-making in the ED. We will leverage previously developed AKI surveillance and prediction tools to generate a unified EHR-based algorithm that empowers ED clinician prevention of kidney disease progression. Aim 2: Translate the AI algorithm to an AKI-CDS system to enable in-depth study of Clinician-AI interactions in the ED. We will establish end-user requirements while creating data collection instruments to examine AI in the ED. Both efforts will support the development of the AKI-CDS system to pilot and investigate ED clinician perceptions of AI trustworthiness and explainability in preparation for multi-site implementation. Aim 3: Perform a multi-site effectiveness-implementation evaluation of the AKI-CDS system in the ED. We will implement the AI-driven CDS system across three ED study sites using a pragmatic investigational framework to perform effectiveness and implementation evaluations in parallel. The research proposed will generate new knowledge and tools to advance the study of AI in the ED, and will result in a scalable CDS product with the capacity to improve the quality of kidney care delivered to more than 1 million patients affected by AKI in the US each year.