Pictographs for Preventing Wrong-Patient Errors in NICUs

Part of paid clinical trials in Baltimore, Maryland.

Sponsor
Columbia University
Study ID
NCT03960099
Status
Recruiting

Conditions

  • Electronic Medical Records
  • Medical Errors

Eligibility Criteria

Sex
ALL
Age
N/A - N/A
Healthy Volunteers
Not accepted

Interventions

  • Pictograph in Banner and Verification Alert — BEHAVIORAL
    Patient Pictograph will be displayed in the banner at the top of the screen in the electronic health record AND patient Pictograph displayed in a patient ID verification alert when placing electronic orders in the electronic health record.

Study Details

Newborns in the neonatal intensive care unit (NICU) are at high risk for wrong-patient errors. Effective 2019, The Joint Commission requires that health systems adopt distinct methods of newborn identification as part of its National Patient Safety Goals. Displaying patient photographs in the electronic health record (EHR) is a promising strategy to improve identification of children and adults, but is unlikely to be effective for identifying newborns. This study assesses the use of Pictographs as a "photo equivalent" for improving identification of newborns in the NICU. This multi-site, two-arm, parallel group, cluster randomized controlled trial will test the effectiveness of Pictographs for preventing wrong-patient order errors in the NICU. Pictographs consist of three elements: 1) pictorial symbols of easy-to-remember objects (e.g., rainbow, lion); 2) the infant's given name (when available); and 3) a color-coded border indicating the infant's sex. The study will be conducted at three academic medical centers that utilize Epic EHR. All parents or guardians will be asked to select a unique Pictograph for each infant admitted to the NICU to be displayed on the isolette and in the EHR for the duration of the infant's hospital stay. All clinicians with the authority to place electronic orders in the study NICUs will be randomly assigned to either the intervention arm (Pictographs displayed in the EHR) or the control arm (no Pictographs displayed in the EHR). The main hypothesis is that clinicians assigned to view Pictographs in the EHR will have a significantly lower rate of wrong-patient order errors in the NICU versus clinicians assigned to no Pictographs. The primary outcome is wrong-patient order sessions, defined as a series of orders placed for a single patient by a single clinician that contains at least one wrong-patient order. The Wrong-Patient Retract-and-Reorder (RAR) measure, a validated, reliable, and automated method for identifying wrong-patient orders, will be used as the primary outcome measure. The Wrong-Patient RAR measure identifies one or more orders placed for a patient that are retracted within 10 minutes, and then reordered by the same clinician for a different patient within the next 10 minutes. In the validation study conducted at a large academic medical center, real-time telephone interviews with clinicians confirmed that 76.2% of RAR events were correctly identified by the measure as wrong-patient orders.

Key Dates

Start date
Mar 16, 2022
Status verified
Mar 2026
Primary completion
Jun 30, 2027
Completion
Jun 30, 2028

Study Design

Enrollment
6,250 participants (estimated)
Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
HEALTH_SERVICES_RESEARCH

Arms

  • Active Comparator: Pictograph in Banner and Verification Alert
    Patient Pictograph displayed in the banner (at the top of the screen) AND Pictograph displayed in a verification alert when placing electronic orders.
  • No Intervention: No Pictograph
    No patient Pictographs displayed in the electronic health record.

Primary Outcome Measure

Wrong-patient order sessions that contain at least one Retract-and-Reorder (RAR) event as identified by the Wrong-Patient Retract-and-Reorder (RAR) measure. [ Time Frame: 2.5 years ]

Central Contacts

Locations (4)

FacilityCityStateZIPSite coordinators
Johns Hopkins MedicineBaltimoreMaryland21287
Allen Chen, MD, PhD
Brigham and Women's HospitalBostonMassachusetts02115-
New York-Presbyterian HospitalNew YorkNew York10032
Jason S Adelman, MD,MS
Montefiore Medical CenterThe BronxNew York10461-

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