Scalable Decision Support and Shared Decisionmaking for Lung Cancer Screening (Utah)

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Decision Precision+: Increasing Lung Cancer Screening for At-Risk Patients

SIGNIFICANCE AND POTENTIAL IMPACT

Widely disseminating a CDS tool that supports individualized shared decision making for lung cancer screening is expected to increase appropriate screening and save lives.

An effective, but underused screening for identifying early stage lung cancer

Lung cancer is the leading cause of cancer-related death in the United States. Screening for lung cancer using low-dose computed tomography (LDCT) is effective in early detection of lung cancer among individuals with a history of heavy smoking and is recommended by the U.S. Preventive Services Task Force (USPSTF). Yet, less than 5 percent of eligible patients are screened using LDCT every year. Increasing use of LDCT for at-risk patients could prevent as many as 10,000 lung cancer deaths annually. However, the decision to screen requires a risk-benefit discussion between patients and their physicians, as false positives can result in unnecessary biopsies and possible complications. Dr. Kensaku Kawamoto and a group of researchers from the University of Utah, the University of Michigan, and Intermountain Healthcare are examining ways to integrate this life-saving screening into clinical workflow.

CDS can improve lung cancer screenings

Study co-investigators Tanner Caverly and Angie Fagerlin previously led the development of a standalone shared decision making tool for lung cancer screening called Decision Precision. This CDS tool incorporates the USPSTF guidelines for LDCT screening and provides patient-specific information on the expected benefits and harms of screening. When used in eight Veterans Health Administration medical centers, decision-making improved about LDCT screenings among at-risk patients. While standalone, web-based CDS tools may enable clinicians to more easily personalize screening, they are also limited by a lack of workflow integration and often require duplicate data entry, thus increasing provider burden and limiting the tool’s usefulness.

“The data is there [in the EHR]. We need to figure out how to intelligently mine the data to get the right information to providers at the points of care for shared decision making with patients.”
– Dr. Kawamoto

Integrating and scaling an effective CDS tool into clinical workflow

Dr. Kawamoto and his colleagues are adapting Decision Precision into a shareable tool that can be integrated into any EHR system. This new tool, Decision Precision+, pulls data from the EHR to enable providers to have an individualized risk-benefit discussion with at-risk patients on whether lung cancer screening is right for them. The team’s goal is for patients and their providers to engage in informed, shared decision making regarding this potentially lifesaving test. Dr. Kawamoto and his colleagues feel strongly about sharing and scaling effective CDS. The team plans to integrate Decision Precision+ into multiple EHR systems and make the tool available to other health systems.

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Widely disseminating a CDS tool that supports individualized shared decision making for lung cancer screening is expected to increase appropriate screening and save lives.

Project Details - Ongoing

Summary:

Lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer-related deaths among both men and women. Low-dose computed tomography (LDCT) can be used as a screening test for the early detection of lung cancer among individuals with a history of heavy smoking, potentially reducing deaths. However, use of the test requires a risk-benefit discussion between patients and their physicians prior to ordering. Today, there are an estimated 8.6 million US adults who meet the US Preventive Services Task Force LDCT screening guidelines. Despite its availability, less than 5 percent of eligible patients are screened with this modality every year.

This study will take a previously developed shared decision making tool for lung cancer screening, Decision Precision LDCT, and adapt it into a shareable tool that can be integrated into any electronic health record (EHR) system. This new tool, Decision Precision+, will pull patient risk data from a given EHR to determine eligible patients, prompt providers to have a risk-benefit discussion with those individuals based on their individualized risk-profile, prompt for missing smoking history, and prompt to consider lung cancer screening when appropriate. The investigators’ goal is for patients and their physicians to make informed, patient-centered decisions regarding this potentially lifesaving test.

The specific aims of this study are as follows:

  • Adapt the standalone Decision Precision LDCT shared decision making tool into a standards-based clinical decision support (CDS) tool (Decision Precision+), develop CDS tools for optimally integrating Decision Precision+ into clinical workflows, and advance underlying standards and their adoption. 
  • Integrate Decision Precision+ with multiple EHR systems and widely disseminate the tool. 
  • Evaluate the impact of the CDS tool, including for adoption, clinical impact, and financial impact. 

Following its design and development, Decision Precision+ will be implemented and evaluated at the primary care clinics of University of Utah Health. It will also be made available to other healthcare organizations as a free tool that can be downloaded and used within other EHR systems. Ultimately, the investigators hope that the research will enable widespread implementation of Decision Precision+ to optimize lung cancer LDCT screening, while providing a model for widely disseminating other evidence-based CDS. Such optimized LDCT screening could prevent as many as 10,000 lung cancer deaths annually while minimizing adverse events associated with screening.