At a glance
ClinicalIndex Comparison RecordStandardized by ClinicalIndex from the ClinicalTrials.gov record · verify against the source.
Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
In Brief
A clinical study evaluating CONCERN CDS system notification for Hospital Acquired Condition. Completed, enrolled 60,893 participants across 4 sites.
Detailed Summary
There are patients who die or have a bad outcome in the hospital and this could be prevented. Data in the nurses' notes could be used by computers to tell the rest of the care team that a patient is not doing well and that they should act more quickly. This project will build and evaluate a computer system that makes it easier for the care team to see and understand that data and act quickly to save patients. The aims of this study is to answer the questions, what is the level of provider use of the CONCERN CDS notification system (called CONCERN SMARTapp) and resulting impact on selected patient outcomes? Specifically, the study has 1) validated desired thresholds for the CONCERN CDS system and 2) integrated the CONCERN CDS system for early warning of risky patient states within CDS tools. In this portion of the study (aim 3), the investigator will implement and evaluate the CONCERN CDS system on primary outcomes of in-hospital mortality and length of stay and secondary outcomes of cardiac arrest, unanticipated transfers to the intensive care unit, and 30-day hospital readmission rates.
Study Details
Timeline
Interventions
The CONCERN CDS will trigger based on analytics of nursing documentation that indicates recognition and concern of patient changes. The CONCERN CDS will alert the care team of the patients "risky state" to increase team-based situational awareness (i.e., shared understanding of the patient situation) of patients predicted to be at risk for patient decompensating in need of rapid intervention to prevent mortality and associated harm. Version 1: Burn in phase to evaluate adoption and adaptation to the algorithm being studied. Expected time frame - 3 months Version 2: Version 2 refined based on continuous monitoring of data. Expected time frame - 3 months Version 3: Version 3 refined based on continuous monitoring of data. Expected time frame - 3 months