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.
Study Type
OBSERVATIONAL
Enrollment
200,000
Johns Hopkins Hospital
Baltimore, Maryland, United States
RECRUITINGNumber of Patients who receive Guideline-Concordant Kidney Care
Time frame: From time of decision support provision until departure from the ED, approximately 6 hours
New or Progressive Acute Kidney Injury
Time frame: Within 72 hours of first creatinine measurement in the ED
Perceived usefulness and usability of AKIDS software as assessed by the System Usability Score
Will be assessed using the usability using the System Usability Score, a 10-item Likert scale that yields a single composite score on a scale of 0-100, with 100 being most usable.
Time frame: Prior to AKIDS implementation, immediately after implementation, and post implementation 6 months
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