FHIR-Enhanced RealRisks to Improve Accuracy of Breast Cancer Risk Assessments

NCT ID: NCT05810025

Last Updated: 2026-01-05

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 55 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2023-06-01
• Study Completion Date: 2025-12-31

Brief Summary

Electronic health records (EHRs) are an increasingly common source for populating risk models, but whether used to populate validated risk assessment models or to de-facto build risk prediction models, EHR data presents several challenges. The purpose of this study is to assess how the integration of patient generated health data (PGHD) and EHR data can generate more accurate risk prediction models, advance personalized cancer prevention, improve digital access to health data in an equitable manner, and advance policy goals for Patient Generated Health Data (PGHD) and EHR interoperability.

Detailed Description

While breast cancer (BC) mortality has declined, this decline has begun to plateau, particularly among racial/ethnic minorities. Women identified as high-risk for BC may benefit from chemoprevention, testing for BC susceptibility genes, screening, and other personalized risk reducing strategies; however, barriers exist including the time required to conduct risk assessment of each woman in a population. Electronic health records (EHRs), a common source for populating risk assessment models present challenges, including missing data, and data type more accurate when provided by patients compared to EHRs. The investigators previously extracted EHR data on age, race/ethnicity, family history of BC, benign breast disease, and breast density to calculate BC risk according to the Breast Cancer Surveillance Consortium (BCSC) model among 9,514 women. Comparing self-reported and EHR data, more women with a first-degree family history of BC (14.6% vs. 4.4%) and benign breast biopsies (21.3% vs. 11.3%) were identified with patient reported data, but EHR data identified more women with atypia or lobular carcinoma in situ (1.1% vs. 2.3%). The EHR had missing data on race/ethnicity for 26.8% of women and on first-degree family history of BC for 87.2%. Opportunely, Fast Healthcare Interoperability Resources (FHIR), application programming interfaces (APIs), and new legislation offer an elegant solution for automated BC risk assessment that integrates both patient-generated health data and EHR data to harness the strengths of each approach. In prior work, the investigators developed the RealRisks decision aid using an iterative design process to equitably maximize acceptability, and usability. RealRisks promotes understanding of BC risk and collects patient-entered data to calculate BC risk according to the Gail model, BCSC, and BRCAPRO. When FHIR became available, the investigators updated RealRisks to automatically populate information for BC risk calculation from the EHR, and designed a prototype interface that shows this data to patients with a request to review and modify data before running the risk assessments. The investigators recently conducted a feasibility study to demonstrate that EHR data from FHIR could be incorporated into automated BC risk calculation. To increase the likelihood of developing disseminatable and equitable strategies that integrate EHR and PGHD data for risk assessment and personalized BC risk-reduction, the focus is to refine and test our approach among diverse multiethnic women. The aims are: 1) conduct user evaluations to refine FHIR-enhanced RealRisks; 2) assess the effect of the FHIR-enhanced RealRisks on patient activation, risk perception, and usability in a pilot study of multiethnic high-risk women; and 3) identify multilevel barriers to implementing FHIR-enhanced RealRisks into clinical care. Given the mortality associated with BC, focused efforts are needed to provide accurate risk assessment and shared decision-making about risk-reducing strategies, especially in minority women who are more likely to be diagnosed with advanced stage BC. If successful, the approach tested in this application may provide a roadmap for broadly improving digital access to health data and reducing BC mortality in an equitable manner.

The investigators will conduct a pre-/post- feasibility study of 55 high-risk diverse multiethnic women with follow-up to assess accuracy of breast cancer risk perception (perceived lifetime risk minus actual risk according to the Gail model) and patient activation.

Conditions

Breast Cancer

Study Design

• Allocation Method: NA
• Intervention Model: SINGLE_GROUP
• Primary Study Purpose: PREVENTION
• Blinding Strategy: NONE

Study Groups

FHIR-Enhanced RealRisks

Participants will self-administer FHIR-enhanced RealRisks with access to risk communication games, family history pedigree and modules on chemoprevention and genetics testing, if relevant to them based on their risk and family history. The investigators are interested in gaining short-term feedback on patient activation and other patient reported outcomes, which will be assessed before and within 2 weeks after using RealRisks.

Group Type: EXPERIMENTAL

RealRisks

Intervention Type: BEHAVIORAL

RealRisks is a web-based patient-centered decision aid (DA) designed to improve: 1) accuracy of breast cancer risk perceptions; 2) chemoprevention knowledge, and 3) informed choice. The DA includes audio and modules about breast cancer risk (including interactive games on risk communication) and chemoprevention. Through RealRisks, the investigators will collect information on breast cancer risk factors to calculate a patient's BCSC breast cancer risk score and also factors that influenced decision-making about chemoprevention through the preference elicitation game. RealRisks generates an action plan for patients summarizing their personalized breast cancer risk profile and preference elicitation for chemoprevention. Of note, the tool is designed for patients with varying levels of health literacy and numeracy and is available in English and Spanish.

Interventions

RealRisks

RealRisks is a web-based patient-centered decision aid (DA) designed to improve: 1) accuracy of breast cancer risk perceptions; 2) chemoprevention knowledge, and 3) informed choice. The DA includes audio and modules about breast cancer risk (including interactive games on risk communication) and chemoprevention. Through RealRisks, the investigators will collect information on breast cancer risk factors to calculate a patient's BCSC breast cancer risk score and also factors that influenced decision-making about chemoprevention through the preference elicitation game. RealRisks generates an action plan for patients summarizing their personalized breast cancer risk profile and preference elicitation for chemoprevention. Of note, the tool is designed for patients with varying levels of health literacy and numeracy and is available in English and Spanish.

Intervention Type: BEHAVIORAL

Other Intervention Names

FHIR-Enhanced RealRisks

Eligibility Criteria

Inclusion Criteria

• Women, age 35-74 years
• High-risk defined as 5-year invasive breast cancer risk ≥1.7% or 10 risk ≥20% according to the BCSC or GAIL models
• English- or Spanish-speaking
• Able to sign informed consent.

Exclusion Criteria

• Women with a personal history of breast cancer
• Women who previously participated in a sub-study (Aim 1) of the awarded grant.

• Minimum Eligible Age: 35 Years
• Maximum Eligible Age: 74 Years
• Eligible Sex: FEMALE
• Accepts Healthy Volunteers: Yes

Sponsors

National Institute on Minority Health and Health Disparities (NIMHD)

NIH

Sponsor Role: collaborator

Columbia University

OTHER

Sponsor Role: lead

Responsible Party

Rita Kukafka

Professor of Biomedical Informatics and Sociomedical Sciences; Department of Biomedical Informatics Chief Diversity Officer

Responsibility Role: PRINCIPAL_INVESTIGATOR

Principal Investigators

Rita Kukafka, DrPH, MA

Role: PRINCIPAL_INVESTIGATOR

Columbia University

Locations

Columbia University Irving Medical Center

New York, New York, United States

Countries

United States

Other Identifiers

R21MD017654

Identifier Type: NIH

Identifier Source: secondary_id

View Link

AAAU1629

Identifier Type: -

Identifier Source: org_study_id