Scalable Decision Support and Shared Decision Making for Lung Cancer Screening

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If scaled nationally, a shared decision making tool to support the appropriate use of low-dose computed tomography screening could prevent as many as 10,000 lung cancer deaths annually, while minimizing adverse events associated with screening

Project Details -
Completed

Lung cancer is the leading cause of cancer-related deaths among both men and women. The U.S. Preventive Services Task Force (USPSTF) recommends low-dose computed tomography (LDCT) screening--a test for the early detection of lung cancer among individuals with a history of heavy smoking–to increase appropriate screening. However, use of the test requires a risk-benefit discussion between patients and their physicians prior to ordering since there is variation in expected benefits versus harms (e.g., biopsy complications after false-positive screens). The use of a shared decision making (SDM) tool can support decision making to determine LDCT screening; however, these tools are often standalone clinical decision support (CDS) tools that require manual data entry and are not directly integrated into clinical workflows. Further, even when these tools are developed within a specific organization’s electronic health record (EHR) system, they are difficult to disseminate across different health systems and EHR platforms. These challenges limit the adoption of CDS to support SDM lung cancer screening; less than 5 percent of eligible patients are screened via LDCT every year.

In previous research, a group of researchers from the University of Utah, the University of Michigan, and Intermountain Healthcare developed and evaluated a standalone SDM tool for lung cancer screening called Decision Precision (available at 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 Administration (VA) medical centers, decision making improved about LDCT screenings among at-risk patients. In this followup study, researchers adapted Decision Precision into a shareable tool that can be integrated into any EHR system leveraging standards-based interoperability. This new tool, Decision Precision+, pulls various patient data from a given EHR to provide individualized benefit and risk information for lung cancer screening, allowing patients and their providers to make informed, patient-centered decisions regarding this potentially lifesaving test.

The specific aims were as follows:

  • Adapt the standalone Decision Precision LDCT SDM tool into a standards-based 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 and impact.

The project team successfully developed Decision Precision+ as a SMART on FHIR app that 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. Decision Precision+ is now offered to other healthcare organizations as a free tool that can be downloaded and used within any EHR system that supports the SMART on FHIR framework. Technical integration of Decision Precision+ was completed with the Epic EHR and the Fujion Clinical EHR system. Early technical integration of Decision Precision+ was conducted with the VA Computerized Patient Record System EHR system and the Cerner EHR system.

Following its design and development, Decision Precision+ was implemented and evaluated at the primary care clinics of University of Utah Health. This evaluation found that introduction of the intervention was associated with significant increases in LDCT ordering and completion according to USPSTF guidelines. If scaled nationally, optimized LDCT screening could prevent as many as 10,000 lung cancer deaths annually, while minimizing adverse events associated with screening. Already, adoption of the CDS tool is in process with multiple healthcare systems.

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

Theme:

Supporting Health Systems in Advancing Care Delivery

Subtheme:

Scaling Effective Digital Healthcare Interventions Across Healthcare Systems

A shared decision-making tool to support the appropriate use of low-dose computed tomography screening has the potential to prevent 10,000 or more lung cancer deaths annually in the United States.

Low-dose computed tomography is an effective but underused tool for identifying lung cancer

Lung cancer is the leading cause of cancer-related deaths in the United States among both men and women. 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). And while LDCT could reduce lung cancer deaths by about 20 percent, or more than 10,000 lives per year in the United States, its use is pretty abysmal; less than 10 percent of eligible patients are screened using LDCT every year.

Dr. Kensaku Kawamoto and a group of researchers from the University of Utah, the University of Michigan, and Intermountain Healthcare wanted to find a better way to integrate the potentially life-saving screening into clinical workflows. The team adapted a previously developed standalone shared decision-making tool for lung cancer screening called Decision Precision, which is available at https://screenlc.com. This clinical decision support (CDS) tool incorporates the USPSTF guidelines for LDCT screening and provides patient-specific information on the expected benefits and harms of screening, such as false positives that can result in unnecessary biopsies and possible complications. While standalone web-based CDS tools like Decision Precision 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. Further, even when these tools are developed within a specific organization’s electronic health record (EHR) system, they can be difficult to disseminate across different health systems and EHR platforms.

Decision Precision+ is a CDS SMART on FHIR app that improves lung cancer screening

In this followup study, the team adapted Decision Precision into a shareable tool that can be integrated into any EHR system that leverages standards-based interoperability. The new tool, Decision Precision+, is a SMART on Fast Healthcare Interoperability Resources (FHIR) app that 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 studied Decision Precision+ in conjunction with EHR prompts to consider lung cancer screening at University of Utah Health primary care clinics. Through this study, they found that rates of lung cancer screening orders and completion increased remarkably for eligible patients.

“We found that the DP+ and the EHR prompts had a pretty substantial impact. Our rate for screening orders for eligible patients as well as patients getting actually screened both increased over threefold. And in a clinical space, that's considered pretty substantial because we typically see a 5 to 10 percent increase. So, an over 300 percent increase is quite promising.”- Dr. Kensaku Kawamoto

Additional research is improving and scaling DP+

Decision Precision+ is now offered to healthcare organizations as a free tool that can be downloaded and used within any EHR system that supports the SMART on FHIR framework. Already, adoption of the CDS tool is in process with multiple healthcare systems. In addition, Dr. Kawamoto has received followup funding from AHRQ to further improve DP+ and promote real-world dissemination and implementation of the tool. The followup research is using a user-centered design approach to supplement DP+ with a patient-facing SMART on FHIR application called MyLungHealth. The app integrates with patient portals and can be used directly by patients so they can learn about lung cancer screening and review their individualized estimates of the benefits of screening. In addition, the researchers are studying how best to help other health systems implement these tools and developing self-service resources to assist with implementation. By doing so, this research will widely disseminate patient-centered tools to help improve lung cancer screening at scale and reduce lung cancer deaths.

This Research is Featured in

Principal Investigator

Kawamoto, Kensaku

Organization

University of Utah

Research Profile

Funded Amount

$1,184,380

Research Type

Completed

This Research is Featured in

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

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

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

Principal Investigator: Kawamoto, Kensaku
Organization: University of Utah
Research Profile: Scalable Decision Support and Shared Decision Making for Lung Cancer Screening
Funded Amount: $1,184,380

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.