Precision-Medicine Diagnostic Support in Hospitalized Veterans With Acute Kidney Injury

Overview

Acute kidney injury (AKI) affects up to 20% of hospitalized Veterans and is strongly associated with morbidity and death. AKI is a diverse condition and timely and accurate diagnosis of the type of AKI is critical to begin appropriate therapies, especially those causes that require specific treatments beyond general supportive care. Yet, there are still significant gaps in the initial evaluation of AKI among hospitalized patients. Clinical decision support systems (CDSS) have shown promise to address these barriers, but most consist of simple alerting schemes and general care recommendations provided at a single point in time. The goal of this proposal is to develop and test the feasibility and usability of a rule-based and Artificial Intelligence-assisted precision CDSS tool (PRECISE-AKI) that can provide cognitive support to improve timely initial diagnostic evaluation of AKI.

Acute kidney injury (AKI), defined as a sudden loss of kidney function, affects up to 20% of hospitalized Veterans and is strongly associated with chronic kidney disease, poor quality of life, and death. Clinical practice guidelines recommend timely identification of the cause of AKI, but gaps remain in conducting the initial and subsequent diagnostic evaluation. Some causes of AKI also require specific treatments beyond supportive care, can be challenging to diagnose, and can require even more detailed evaluation including kidney biopsies. This clinical trial will evaluate the usability and feasibility of an Artificial Intelligence (AI)-assisted automated and comprehensive precision CDSS tool (PRECISE-AKI) to provide iterative cognitive support of general and nephrology-based providers in the diagnostic evaluation of hospitalized patients experiencing AKI within the Tennessee Valley Health Systems (TVHS). The investigators hypothesize that PRECISE-AKI will be acceptable, appropriate, and feasible to a variety of users and improve the diagnostic evaluation of hospitalized patients AKI.