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Early Patient Identification and Care Pathway for Patients With Acute Exacerbations of Chronic Obstructive Pulmonary Disease: A Randomized Controlled Trial of Informatics Enhanced Hospital Admission (NCT03028805)

This is a fully automated randomized trial with two randomization branch-points. The first is inclusion of disease-specific orders in the admission orders based on a predictive model using real-time data. The second is the use of dynamic orders that are end-user tested rather than static orders designed by a committee. The primary hypothesis is that automatic inclusion of disease specific orders with admission orders will improve adherence to guidelines for patients with COPD. The secondary hypothesis is that clinical and operational outcomes will improve, thereby improving value.
  • Other: Automatic inclusion of COPD orders in admission orders
    Use of real-time data to identify a population of patients with COPD and prompt improved adherence to evidence-based guidelines through the automatic inclusion of a COPD order set in the admission orders.
    • Other: Dynamic, end-user order set design
      Use of a dynamic order set that has been end-user tested prior to launch rather than designed centrally by a committee to test use of order set components.
      Ages eligible for Study
      18 Years and older
      Genders eligible for Study
      All
      Accepts Healthy Volunteers
      No
      Inclusion Criteria:
      • Patients 18 years old or greater admitted to the Hospital Medicine service at UCSF Medical Center who meet criteria as determined by predictive model to be likely admissions for COPD exacerbation.
      Exclusion Criteria:
      • Patients admitted to other clinical services at UCSF Medical Center.
      This is a single-center, single-blinded, 2x2 factorial randomized controlled trial to test both automated order set inclusion and evidence-based order set design with end user testing on order set use and clinical outcomes for adult patients admitted to the hospital with acute exacerbations of Chronic Obstructive Pulmonary Disease (COPD).

      First, the investigators will develop a predictive model to identify patients admitted to the hospital with COPD exacerbations based on retrospective data, but limited to data that is available in real-time at admission.

      Second, 1,000 admissions to UCSF Medical Center of adults predicted to have COPD by the predictive algorithm will be prospectively block randomized by encounter to automatic inclusion of a COPD order set in the admission orders or usual care. Providers caring for patients in both arms of the trial can independently search for and use a COPD order set. Any provider using a COPD order set in either arm will also be randomized to see two versions of the order set. The first is a static list of orders, and the second is dynamic, meaning that orders will display only when appropriate. For example, a patient who just had a chest x-ray does not need a routine repeat test. The dynamic order set will show the provider that the x-ray was completed at a specific time and will not display a prompt for a repeat test. Providers can, of course, still order anything they deem clinically appropriate, and may choose to order a repeat x-ray for a patient with a change in clinical status.

      The components of the order set are based on international guidelines from the Global Initiative for Chronic Lung Disease (GOLD initiative, a collaboration between the National Heart, Lung, and Blood Institute and the World Health Organization) and a multi-stakeholder working group at UCSF including two hospitalists, two pulmonologists, two transitional care nurse specialists, one advanced practice nurse, one pharmacist, one respiratory therapist, one physical therapist, and one nurse.

      1 locations

      United States (1)
      • University of California, San Francisco
        not yet recruiting
        San Francisco, California, United States, 94143
      Status:
      not yet recruiting
      Type:
      Interventional
      Phase:
      -
      Start:
      31 December, 2016
      Updated:
      17 January, 2017
      Participants:
      1000
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