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Recruiting No phase listed Observational

Effectiveness-Implementation Evaluation of Acute Kidney Injury Decision Support

ClinicalTrials.gov ID: NCT06840210

Public ClinicalTrials.gov record NCT06840210. Field values are reproduced from the official study page; the official ClinicalTrials.gov record remains the source of truth for eligibility, enrollment, and contact information.

ClinicalTrials.gov public records Last synced Oct 2, 2026, 2:37 AM EDT

Data is sourced from official ClinicalTrials.gov public API records. Always review the official ClinicalTrials.gov record for the latest information.

Official title

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

Brief summary

Reproduced verbatim from the official ClinicalTrials.gov record. Not medical advice.

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 identification

NCT ID
NCT06840210
Recruitment status
Recruiting
Study type
Observational
Phase
Not listed
Lead sponsor
Johns Hopkins University
Other
Enrollment
200,000 participants

Conditions and interventions

Interventions

Not listed

Eligibility (public fields only)

Age range
18 Years and older
Sex
All
Healthy volunteers
Healthy volunteers not accepted

This page does not interpret eligibility. Detailed inclusion and exclusion criteria are on the official ClinicalTrials.gov record.

Study timeline

Start date
Mar 10, 2026 (Actual)
Primary completion
Dec 1, 2028 (Estimated)
Completion
Dec 1, 2030 (Estimated)
Last update posted
Apr 20, 2026

2026 – 2030

United States locations

U.S. sites
1
U.S. states
1
U.S. cities
1
Facility City State ZIP Site status
Johns Hopkins Hospital Baltimore Maryland 21287 Recruiting

Site contact phone numbers, emails, and investigator names are intentionally not displayed here. Open the official ClinicalTrials.gov record for site contact information.

About this trial record page

What this page shows
Public field values for ClinicalTrials.gov record NCT06840210, including study identification, conditions, interventions, eligibility (age, sex, healthy volunteer), timeline, and U.S. site list.
What this page does not do
No medical advice, eligibility judgments, treatment recommendations, study quality scoring, or AI-generated medical summaries. No site contact phone numbers, emails, or investigator names.
Where the data comes from
Sourced from the official ClinicalTrials.gov public API. The official record is the source of truth.
Last refresh
Last update posted Apr 20, 2026 · Synced Oct 2, 2026

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Open the official record

The complete public registry record, listed eligibility criteria, and available contact information for NCT06840210 live on ClinicalTrials.gov.

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