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.
Breast Cancer
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.
FHIR-Enhanced RealRisks to Improve Accuracy of Breast Cancer Risk Assessments
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Columbia University Irving Medical Center, New York, New York, United States, 10032
Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.
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35 Years to 74 Years
FEMALE
Yes
Columbia University,
Rita Kukafka, DrPH, MA, PRINCIPAL_INVESTIGATOR, Columbia University
2025-04-15