Transforming Kidney Care in the Emergency Department: A Prospective Effectiveness-Implementation Evaluation

Summary

Study Purpose: This study is testing an artificial intelligence (AI)-powered clinical decision support (CDS) system designed to help emergency department (ED) doctors detect and manage acute kidney injury (AKI) earlier. The goal is to see whether the tool improves patient care, clinician decision-making, and hospital outcomes when used in real-world ED settings. Study Design: The AI tool will be gradually introduced at three hospital EDs: Johns Hopkins Hospital (JHH) Bayview Medical Center (BMC) Howard County General Hospital (HCGH) Before the tool is activated, it will run in the background to collect baseline data without influencing care. Once implemented, doctors will receive training, and researchers will track how often the tool is used and whether it improves AKI care. What the Study Measures: Process Outcomes: Does the tool help doctors identify AKI sooner, avoid harmful medications, and improve decision-making about hospitalization? Clinical Outcomes: Does the tool reduce the number and severity of AKI cases and improve kidney-related health outcomes? Implementation Outcomes: Do ED doctors find the tool useful? Does it fit into the ED doctor's workflow without slowing the ED doctor's down? Expected Impact: If successful, the AI tool could be expanded to other hospitals and used to improve early detection and treatment of AKI, reducing kidney complications and improving patient care nationwide.

Eligibility

Age range

18+ years

Sex

All

Healthy volunteers

No

Location