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Learning Lab for Diagnostic Fidelity

Acute Care Learning Laboratory-Reducing Threats to Diagnostic Fidelity in Critical Illness

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT03865303
Enrollment
25551
Registered
2019-03-06
Start date
2019-04-22
Completion date
2022-11-30
Last updated
2023-02-08

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Critical Illness

Keywords

Critical Illness, Patient Safety, Delivery of Health Care, Electronic Health Records

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.

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.

Interventions

None listed

Sponsors

Agency for Healthcare Research and Quality (AHRQ)
CollaboratorFED
Mayo Clinic
Lead SponsorOTHER

Study design

Observational model
COHORT
Time perspective
PROSPECTIVE

Eligibility

Sex/Gender
ALL
Age
18 Years to 120 Years
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Validation of automated phenotypes1 yearUse data from the patient electronic medical record to identify the number of diagnostic error or delay to validate clinical environment automated phenotypes
Adoption (Number of time the Control Tower used during the clinical encounters)1 yearStandardized process tracking sheets to track each time the control tower system is triggered and used.
Implementation Acceptability1 yearFocused questions about beliefs, attitudes, usability by healthcare providers

Countries

United States

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026