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Learning Lab for Diagnostic Fidelity

The safety and scientific validity of this study is the responsibility of the study sponsor and investigators. Listing a study does not mean it has been evaluated by the U.S. Federal Government. Know the risks and potential benefits of clinical studies and talk to your health care provider before participating. Read our disclaimer for details.
 
ClinicalTrials.gov Identifier: NCT03865303
Recruitment Status : Recruiting
First Posted : March 6, 2019
Last Update Posted : October 19, 2020
Sponsor:
Collaborator:
Agency for Healthcare Research and Quality (AHRQ)
Information provided by (Responsible Party):
Brian W. Pickering, M.B., B.Ch., Mayo Clinic

Brief Summary:
Diagnostic error and delay remain a leading cause of preventable harm and death in the United States. Using a learning laboratory structure, researchers will implement mixed-methods research approaches to identify the systemic weaknesses that contribute to diagnostic error and delay in the hospital setting. The knowledge gained from research innovative will allow researchers to design, develop, implement, and refined a suite of human-centered tools that can be deployed to reduce the risk of diagnostic error and delay in both community and academic hospital settings.

Condition or disease
Critical Illness

Detailed Description:
Despite the recognition that diagnostic errors an delays are a major contributor to preventable deaths in the USA, little progress has been made to reduce mortality outcomes from this known killer. An effective strategy leading to meaningful reduction in diagnostic error and delay rates has not made its way into practice. This proposal is unique and novel and combines mixed-methods research approaches with systems engineering research approaches to understand the interplay of the multiple factors contributing to diagnostic error and delay. The knowledge gained from this holistic approach will then be used within the learning laboratory to inform the design, development, evaluation, and refinement of the solutions to diagnostic error and delay. "Control Tower" will be the staging ground for the in situ learning laboratory and will be built on top of a well-established clinical informatics infrastructure and hospital environment open to innovation and practice change.

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Study Type : Observational
Estimated Enrollment : 100000 participants
Observational Model: Cohort
Time Perspective: Prospective
Official Title: Acute Care Learning Laboratory-Reducing Threats to Diagnostic Fidelity in Critical Illness
Actual Study Start Date : April 22, 2019
Estimated Primary Completion Date : July 31, 2022
Estimated Study Completion Date : July 31, 2022



Primary Outcome Measures :
  1. Validation of automated phenotypes [ Time Frame: 1 year ]
    Use data from the patient electronic medical record to identify the number of diagnostic error or delay to validate clinical environment automated phenotypes

  2. Adoption (Number of time the Control Tower used during the clinical encounters) [ Time Frame: 1 year ]
    Standardized process tracking sheets to track each time the control tower system is triggered and used.

  3. Implementation Acceptability [ Time Frame: 1 year ]
    Focused questions about beliefs, attitudes, usability by healthcare providers



Information from the National Library of Medicine

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Ages Eligible for Study:   18 Years to 120 Years   (Adult, Older Adult)
Sexes Eligible for Study:   All
Accepts Healthy Volunteers:   No
Sampling Method:   Non-Probability Sample
Study Population
  1. All patients at risk of diagnostic error will be eligible for the study.
  2. We will include both sexes and all genders and all minority populations as they present with the risk of diagnostic error in the hospitals.

Given the nature of the study, which focuses on patients with or at risk of diagnostic error, outreach efforts to underrepresented populations or individuals would not be possible.

Criteria

Inclusion Criteria

  • For EMR review all adults admitted to the hospital ages 18 and older with research authorization
  • For survey-clinicians including physicians, advanced care practitioners
  • For focus groups and interviews-clinicians including physicians, advanced care practitioners

Exclusion Criteria

  • Age <18 years old
  • No research authorization
  • Refusal to give consent

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): NCT03865303


Contacts
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Contact: Yue Dong, MD 5072556149 dong.yue@mayo.edu
Contact: Ann Johnson 5072556149 ajohnson@mayo.edu

Locations
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United States, Minnesota
Mayo Clinic Health System - Mankato MCHS Recruiting
Mankato, Minnesota, United States, 56001
Contact: Syed Khan, M.D.         
Mayo Clinic in Rochester Recruiting
Rochester, Minnesota, United States, 55905
Contact: Yue Dong, M.D.         
Principal Investigator: Brian Pickering, M.B., B.Ch.         
Sub-Investigator: Ognjen Gajic, M.D.         
Sub-Investigator: Yue Dong, M.D.         
Sub-Investigator: Vitaly Herasevich, M.D.,Ph.D         
Sub-Investigator: Aaron Leppin, M.D.         
Sub-Investigator: Ashok Kumbamu, Ph.D         
Sponsors and Collaborators
Mayo Clinic
Agency for Healthcare Research and Quality (AHRQ)
Investigators
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Principal Investigator: Brian W Pickering, M.B., B.Ch. Mayo Clinic
Additional Information:
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Responsible Party: Brian W. Pickering, M.B., B.Ch., Pricinple Investigator, Mayo Clinic
ClinicalTrials.gov Identifier: NCT03865303    
Other Study ID Numbers: 18-007115
R18HS026609 ( U.S. AHRQ Grant/Contract )
First Posted: March 6, 2019    Key Record Dates
Last Update Posted: October 19, 2020
Last Verified: October 2020

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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 Brian W. Pickering, M.B., B.Ch., Mayo Clinic:
Critical Illness
Patient Safety
Delivery of Health Care
Electronic Health Records
Additional relevant MeSH terms:
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Critical Illness
Disease Attributes
Pathologic Processes