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(CONCERN) Clinical Decision Support (CDS) System

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ClinicalTrials.gov Identifier: NCT03911687
Recruitment Status : Recruiting
First Posted : April 11, 2019
Last Update Posted : July 26, 2022
Sponsor:
Collaborators:
National Institute of Nursing Research (NINR)
Brigham and Women's Hospital
Information provided by (Responsible Party):
Sarah Collins Rossetti, Columbia University

Study Description
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Brief 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.


Condition or disease Intervention/treatment Phase
Hospital Acquired Condition Behavioral: CONCERN CDS system notification Not Applicable

Detailed Description:

Annually, more than 200,000 patients die in U.S. hospitals from cardiac arrest and over 130,000 patients inpatients deaths are attributed to sepsis. These deaths are preventable if patients who are at risk are detected earlier. Prior work found that nursing documentation within electronic health records (EHRs) contains information that could contribute to early detection and treatment, but these data are not being analyzed and exposed by EHRs to clinicians to initiate interventions quickly enough to save patients. A new source of predictive data is defined by analyzing the frequency and types of nursing documentation that indicated nurses' increased surveillance and level of concern for a patient. These data documented in the 48 hours preceding a cardiac arrest and hospital mortality were predictive of the event. While clinicians strive to provide the best care, there is a systematic problem within hospital settings of non-optimal communication between nurses and doctors leading to delays in care for patient at risk. Well-designed and tested EHRs are able to trend data and support communication and decision making, but too often fall short of these goals and actually increase clinician cognitive load through fragmented information displays, "note bloat", and information overload. Substitutable Medical Applications & Reusable Technologies (SMARTapps) using Fast Health Interoperability Resource (FHIR) standard allow for open sharing and use of innovations across EHR systems. The aim of this project is to design and evaluate a SMARTapp on FHIR used across two large academic medical centers that exposes to physicians and nurses our new predictive data source from nursing documentation to increase care team situational awareness of at risk patients to decrease preventable adverse outcomes.

Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) system is the application being designed and evaluated. CONCERN Intervention Trial Design will be a multiple time-series intervention. Baseline data will be collected at all study sites. Silent release mode (no SMARTapp notification) will be used in non-equivalent control units and as a post-intervention unit control to evaluate if notifying clinicians can decrease rates of length of stay on non-ICU units and rates of 30-day hospital readmissions. Different versions of the CDS system (SMARTapp) will be incorporated for dynamic, adaptive functionality and determine if the pattern of nursing documentation has changed. A "burn-in" phase is built in to evaluate adoption and adaptation to the algorithm and phases for deployment of the silent release mode within the multiple time-series intervention trial for a total of 18 months of data collection, including pre-intervention data collection and silent release modes.

Study Design
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Study Type : Interventional  (Clinical Trial)
Estimated Enrollment : 36000 participants
Allocation: Randomized
Intervention Model: Sequential Assignment
Masking: None (Open Label)
Primary Purpose: Health Services Research
Official Title: Communicating Narrative Concerns Entered by RNs (CONCERN) Clinical Decision Support (CDS) System
Actual Study Start Date : July 21, 2020
Estimated Primary Completion Date : October 31, 2022
Estimated Study Completion Date : October 31, 2022
Arms and Interventions
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Arm Intervention/treatment
No Intervention: Control Group
Control data will be collected in the CONCERN CDS system that will be "live" in the EHR, but in silent release mode (e.g., not providing notification to clinicians).
Experimental: Intervention Group
Experimental data will be collected in the CONCERN CDS system that will be "live" in the EHR in "active" release mode (e.g., providing CONCERN CDS system notification to clinicians).
 Behavioral: CONCERN CDS system notification

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

Other Names:
  • CONCERN SMARTapp notification
  • CONCERN Clinical Decision Support (CDS) system notification


Outcome Measures
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Primary Outcome Measures :
  1. In-hospital mortality rate [ Time Frame: Up to 24 months ]
    Deaths occurring in the hospital

  2. Average length of hospital stay [ Time Frame: Up to 24 months ]
    The number of days that a patient was in the hospital


Secondary Outcome Measures :
  1. Number of Cardiac Arrest [ Time Frame: Up to 24 months ]
    Cardiopulmonary events during hospitalization

  2. Number of hospital acquired sepsis [ Time Frame: Up to 24 months ]
    Sepsis occurring during hospitalization

  3. Number of unanticipated transfer to ICU [ Time Frame: Up to 24 months ]
    Transfer to ICU from acute care study units during hospitalization

  4. Hospital readmission rates [ Time Frame: Up to 24 months ]
    Readmission to the hospital


Eligibility Criteria
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Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years and older   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Criteria

Study inclusion criteria for patients:

  • Inpatients with a stay of at least 24 hours on one of our study units

Study exclusion criteria for patients:

  • Inpatients with less than 24 hours on one of our study units
  • Patients less than 18 years of age
  • Hospice patients
  • Did not have a hospital encounter, patients not on one of our study units.

Definition of Study Units:

A clinical unit is considered a CONCERN Study unit if it meets the following criteria:

  • A general medical or surgical acute care or critical care unit

The following clinical units are NOT considered CONCERN Study units:

  • Pediatric or Neonatal units
  • Hospice units
  • Emergency Department
  • Oncology units
  • Obstetrician (OB)/labor and delivery units
  • Behavioral/psych units
  • Observational units
  • Operating room
  • Pre-op
  • Post-op/Post Anesthesia Care Unit (PACU)
  • Same day surgical units
  • Plastics units
  • Virtual departments in EHR database.
Contacts and Locations
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Information from the National Library of Medicine

To learn more about this study, you or your doctor may contact the study research staff using the contact information provided by the sponsor.

Please refer to this study by its ClinicalTrials.gov identifier (NCT number): NCT03911687


Contacts
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Contact: Sarah Collins Rossetti, PhD 2123056605 sac2125@cumc.columbia.edu
Contact: Kenrick Cato, PhD 2123421566 kdc2110@cumc.columbia.edu

Locations
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United States, Massachusetts
Brigham and Women's Hospital Recruiting
Boston, Massachusetts, United States, 02115
Contact: Patricia Dykes, PhD         
Principal Investigator: Patricia Dykes, PhD         
Newton-Wellesley Hospital Recruiting
Newton, Massachusetts, United States, 02462
Contact: Patricia Dykes, PhD         
Principal Investigator: Patricia Dykes, PhD         
United States, New York
New York Presbyterian Columbia University Medical Center Not yet recruiting
New York, New York, United States, 10032
Contact: Kenrick Cato, PhD         
Principal Investigator: Sarah Collins Rossetti, PhD         
Principal Investigator: Kenrick Cato, PhD         
New York Presbyterian Allen Hospital Not yet recruiting
New York, New York, United States, 10034
Contact: Kenrick Cato, PhD         
Principal Investigator: Sarah Collins Rossetti, PhD         
Principal Investigator: Kenrick Cato, PhD         
Sponsors and Collaborators
Columbia University
National Institute of Nursing Research (NINR)
Brigham and Women's Hospital
Investigators
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Principal Investigator: Patricia Dykes, PhD Brigham and Women's Hospital
Principal Investigator: Sarah Collins Rossetti, PhD Columbia University
Principal Investigator: Kenrick Cato, PhD Columbia University
More Information
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Publications:

Publications automatically indexed to this study by ClinicalTrials.gov Identifier (NCT Number):

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Responsible Party: Sarah Collins Rossetti, Assistant Professor of Biomedical Informatics and Nursing, Columbia University
ClinicalTrials.gov Identifier: NCT03911687    
Other Study ID Numbers: AAAR1389
R01NR016941 ( U.S. NIH Grant/Contract )
First Posted: April 11, 2019    Key Record Dates
Last Update Posted: July 26, 2022
Last Verified: July 2022
Individual Participant Data (IPD) Sharing Statement:
Plan to Share IPD: No
Plan Description: This study will develop an open source SmartApp using interoperability standards. The investigators will make all source code available to any researchers upon request. This approach will ensure open access to all project aims, methods, resources, and deliverables by anyone via a study web-page. To aid in replication this web-page will include methods as well as other project processes such as stakeholder engagement, technical development, governance and lessons learned.

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Studies a U.S. FDA-regulated Drug Product: No
Studies a U.S. FDA-regulated Device Product: No
Keywords provided by Sarah Collins Rossetti, Columbia University:
Early warning system
Clinical decision support
Predictive analysis
Failure to rescue
 Inter-professional situational awareness
Nursing surveillance
Healthcare process models of clinical concern (HPM-CC)
Additional relevant MeSH terms:
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Iatrogenic Disease
Disease Attributes
Pathologic Processes