Recruitment

Recruitment Status
Active, not recruiting
Estimated Enrollment
Same as current

Summary

Conditions
Pneumonia
Type
Interventional
Phase
Not Applicable
Design
Allocation: Non-RandomizedIntervention Model: Parallel AssignmentMasking: Single (Outcomes Assessor)Primary Purpose: Health Services Research

Participation Requirements

Age
Between 12 years and 125 years
Gender
Both males and females

Description

Clinicians' ability to accurately diagnose pneumonia and then choose the most appropriate treatment options is enhanced by well-designed clinical decision support (CDS). Pneumonia CDS has historically been focused on inpatient settings, but ambulatory care settings with high pneumonia patient volume...

Clinicians' ability to accurately diagnose pneumonia and then choose the most appropriate treatment options is enhanced by well-designed clinical decision support (CDS). Pneumonia CDS has historically been focused on inpatient settings, but ambulatory care settings with high pneumonia patient volumes also might benefit. The investigators propose to adapt an innovative, validated emergency department (ED) CDS tool based on consensus guidelines for pneumonia care (ePNa) and deploy it to urgent care centers (UCC) using the CFIR framework. Electronic tools such as ePNa may become even more useful within UCCs as the COVID-19 pandemic evolves, since recommendations can be readily updated as better methods of diagnosis and effective treatment develop. ePNa within the ED has already been adapted to recommend SARS-coV-2 testing for patients with pneumonia and signs and symptoms characteristic of viral pneumonia. The proposal supports four aims: Adapt ePNa for UCC and after in silico testing, pilot it among "super user" clinicians during UCC shifts and assess its usability. ePNa needs adaptation for more limited patient data available in UCCs, calibration of severity measures for lower observed mortality, and a chest imaging prompt in patients with pneumonia signs and symptoms. ePNa for UCC will incorporate Stanford University's artificial intelligence CheXED model to provide electronic classification of chest images in <10 seconds for elements of pneumonia diagnosis and treatment (radiographic pneumonia, single vs multiple lobes, and pleural effusion). Using the CFIR framework, our prior ED implementation experience, a focus group of UCC clinicians, semi-structured interviews, and direct observations of workflow including ePNa guided transitions of care between clinicians, the investigators will identify barriers and facilitators to adaptation and implementation of ePNa to UCCs. Test the implementation strategy by deploying ePNa at one of two randomly chosen Intermountain Healthcare UCC clusters each with about 800 annual pneumonia patients - the other a usual care control. Co-primary outcomes are a) accuracy of pneumonia diagnosis defined by compatible chief complaint plus ? 1 pneumonia sign/symptom and radiographic confirmation will be ?10% higher in the ePNa cluster, and b) the percent of UCC pneumonia patients transferred to an emergency department for further evaluation will decrease by ? 3% in the ePNa cluster replaced by more direct hospital admissions or discharge home. Safety measures will be unplanned subsequent 7-day ED visits/hospitalizations and 30-day mortality. Based on this rigorous pilot study, the investigators anticipate a subsequent multi-system cluster-randomized trial. Our work incorporates the Five Rights of CDS to ensure that the strengths of this technology are optimized in the clinical environment. The investigators will leverage experience in innovative pneumonia research, pioneering CDS, and implementation science available at Intermountain to successfully complete this proposal.

Tracking Information

NCT #
NCT04606849
Collaborators
Stanford University
Investigators
Principal Investigator: Nathan Dean, MD Intermountain Health Care, Inc.