Evaluation of a Scalable Decision Support and Shared Decision Making Tool for Lung Cancer Screening
Last updated on July 2021Recruitment
- Recruitment Status
- Recruiting
- Estimated Enrollment
- Same as current
Summary
- Conditions
- Early Detection of Cancer
- Lung Neoplasms
- Type
- Interventional
- Phase
- Not Applicable
- Design
- Allocation: N/AIntervention Model: Single Group AssignmentIntervention Model Description: interrupted time seriesMasking: None (Open Label)Masking Description: no masking due to the nature of the interventionPrimary Purpose: Screening
Participation Requirements
- Age
- Between 55 years and 80 years
- Gender
- Both males and females
Description
The purpose of this project is to increase appropriate low-dose computed tomography (LDCT) lung cancer screening through the development and wide dissemination of patient-centered clinical decision support (CDS) tools that (1) are integrated with the electronic health record (EHR) and clinical workf...
The purpose of this project is to increase appropriate low-dose computed tomography (LDCT) lung cancer screening through the development and wide dissemination of patient-centered clinical decision support (CDS) tools that (1) are integrated with the electronic health record (EHR) and clinical workflows, (2) prompt for shared decision making (SDM) when patients meet screening criteria, and (3) enable effective SDM using individually-tailored information on the potential benefits and harms of screening. The study will promote standard of care that is endorsed by the Centers for Medicare & Medicaid Services (CMS) and the US Preventive Services Task Force (USPSTF). This project is supported both operationally and by an Agency for Healthcare Research and Quality (AHRQ) R18 grant. This project will leverage Decision Precision, a validated Web-based tool for LDCT SDM developed at the Veterans Health Administration, as well as an initial version of Decision Precision+, an EHR-integrated version of the tool which can be accessed directly in the EHR and auto-populate relevant patient data in the tool instead of requiring manual data entry. This study will be an 18-month interrupted time series study conducted at the University of Utah Health primary care clinics.
Tracking Information
- NCT #
- NCT04498052
- Collaborators
- Not Provided
- Investigators
- Principal Investigator: Kensaku Kawamoto, MD, PhD, MHS University of Utah