Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery models

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SUPPLEMENT ABSTRACT This application is being submitted in response to NOT-OD-22-026 as a proposed administrative supplement to the University of Utah (Utah)/New York University (NYU) U01 entitled “Leveraging an electronic medical record infrastructure to identify primary care patients eligible for genetic testing for hereditary cancer and evaluate novel cancer genetics service delivery models” (U01 CA232826). The parent U01 is employing an electronic health record (EHR)-based clinical decision support (CDS) infrastructure to: (i) identify unaffected primary care patients in the Utah and NYU healthcare systems who qualify for cancer genetics services based on current guidelines (Aim 1); and (ii) compare two models of cancer genetics services delivery for identified patients in a randomized clinical trial (Aims 2 and 3). In the parent study, we have identified a cohort of 22,208 primary care patients in the two healthcare systems who are eligible for cancer genetic services, and we are planning for sustainability of this project. However, our prior data has shown disparities by race, ethnicity, and language preference in the pool identified by the current CDS algorithm compared to the underlying primary care patient populations. We have found that at least one contributing factor is systematic disparities in availability and comprehensiveness of cancer family history information available in the structured EHR fields upon which the current algorithm is based. These disparities raise critical ethical issues related to bias resulting from integration of the CDS algorithm. We therefore propose to: (Supplemental Aim 1) Investigate whether incorporating natural language processing (NLP) tools into the CDS algorithm reduces disparities in identification of eligible primary care patients. We have created an NLP-augmented algorithm that incorporates free-text comments and identifies 54% more patients than the current CDS algorithm. We will examine whether use of the NLP-augmented algorithm impacts disparities in identification by race, ethnicity, language preference, and rural/frontier residence compared with the underlying patient population. (Supplemental Aim 2) Identify patients who nearly meet criteria for cancer genetic evaluation and explore acceptability of outreach to those patients from medically underserved communities. We will develop a statistical model that can identify patients who nearly meet criteria for cancer genetic evaluation, which would allow us to target outreach to collect additional family history information. We will also conduct two community engagement studios, one in English and one in Spanish, to examine the acceptability of direct outreach after identification by an algorithm among individuals from medically underserved communities. Together these supplemental aims will investigate two potential approaches to address observed disparities in patient identification. These findings will directly inform the development of policies that explicitly include monitoring the impact of CDS algorithms and AI tools on patient outcomes and disparities in those outcomes during iterative phases of testing and implementation.