Skip to main content
OpenTrials
Recruiting

NCT Number: NCT04606849

Adaptation and Pilot Implementation of ePNa Clinical Decision Support for Utah Urgent Care Clinics

We plan to adapt an innovative, validated emergency department (ED) CDS tool based on consensus guidelines for pneumonia care (ePNa) to function in urgent care clinics (Instacares at Intermountain) and combine it seamlessly with Stanford's CheXED artificial intelligence model using an interoperable platform currently under development by Care Transformation Information Services at Intermountain. We will then deploy it to one of two groups of Instacares (randomly selected) using the CFIR framework for Implementation Science best practice.

Recruiting

Interested in participating?

Request Info

Key information

Age range

12 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

American Fork Instacare, American Fork, Utah, United States

Loading trial locations.

About this study

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.

Who can participate

Healthy volunteers accepted: Yes

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • All patients ≥ 12 years of age with pneumonia: defined by the J-18.X pneumonia code or acute respiratory failure or sepsis with secondary pneumonia codes

Survey All physicians and advanced practice clinicians who are employed and actively seeing patients in the 4 Utah Valley Instacares

Exclusion criteria

  • Patients without radiographic confirmation of pneumonia
  • Subsequent episodes of pneumonia within 12 months (so as not to over-represent patients with recurrent pneumonia caused by recurrent aspiration or structural lung disease).

Survey No providers will be excluded from the survey invitation

Treatment and study plan

Physician Survey

Other

Our questionnaire includes questions on respondent demographics and Likert-style questions about respondent experiences with ePNa. We will validate our modified questionnaire by calculating component loadings and Cronbach Alphas (i.e., internal consistency) of Likert questions loading onto the same components.

ePNa-CheXED

Device

ePNa-CheXED will incorporate Stanford University's artificial intelligence CheXED model to provide electronic classification of chest images in <1 second for elements of pneumonia diagnosis and treatment (radiographic pneumonia, single vs multiple lobes, and pleural effusion).

Primary outcomes

  1. ePNa utilization and impact on the UCC clinical environment

    Time frame: through study completion, year 3 of the study

    Frequency of clinicians' disagreement with different ePNa recommendations will be monitored along with a tally of the structured reasons for disagreement entered by clinicians into ePNa.

Secondary outcomes

  1. Number of unplanned subsequent ED Visits

    Time frame: within 7 days of initial encounter

  2. Number of unplanned hospitalizations

    Time frame: within 7 days of initial encounter

  3. Accuracy of pneumonia diagnosis given

    Time frame: through study completion, year 3 of the study

    defined by compatible chief complaint (cough, dyspnea, chest pain, fever) plus . 1 pneumonia sign/symptom (temperature . 38.0C or < 36.0C, white blood cell count >10,000/ul or <4000/ul), bandemia >10%, SpO2<90% on room air, respiratory rate >20/minute)19 and radiographic confirmation

  4. The change in the transfer rate of UCC pneumonia patients to an ED

    Time frame: through study completion, year 3 of the study

    we want a decrease of . 3% in the ePNa cluster with those transfers replaced by direct hospital admissions or discharge home.

  5. Use of fewer health care resources

    Time frame: through study completion, year 3 of the study

Study contacts

Contact information is provided by the study sponsor or research team.

Carlos Barbagelata, MS

CONTACT

[email protected]

801-507-4607

Valerie Aston

CONTACT

[email protected]

801-507-4606

Sponsors and collaborators

Lead sponsor

Intermountain Health Care, Inc.

Other

Collaborators

  • Stanford University

Registry information

Official study title

Adaptation and Pilot Implementation of a Validated, Electronic Real-Time Clinical Decision Support Tool for Care of Pneumonia Patients in 10 Utah Urgent Care Centers

Important dates

Study start
2020
Primary completion
2024
Study completion
2024
First posted
Oct 28, 2020
Registry last updated
Aug 26, 2024

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

Published trials that share one or more normalized conditions with this study.