Leveraging the learning health care model to improve equity in the age of genomic medicine

Abstract

To fully achieve the goals of a genomics-enabled learning health care system, purposeful efforts to understand and reduce health disparities and improve equity of care are essential. This paper highlights 3 major challenges facing genomics-enabled learning health care systems, as they pertain to ancestrally diverse populations: inequality in the utility of genomic medicine; lack of access to pharmacogenomics in clinical care; and inadequate incorporation of social and environmental data into the electronic health care record. We advance a framework that cannot only be used to directly improve care for all within the learning health system but can also be used to focus on the needs to address racial and ethnic health disparities and improve health equity. KEYWORDShealth equity, health disparities, genomic medicine, precision medicine, learning health care system 1 | INTRODUCTION Electronic health records (EHRs) are revolutionizing the practice of medicine. Not only does the digitalization and standardization of medical records help to improve patient outcomes by facilitating integrated care within complex medical settings, 1 these properties also enable the improvement of the health care system itself 2-4 by simplifying the periodic assessment of system function and quality, 5,6 the socalled learning health system model. With the adoption of EHRs also comes changes to the content of the medical record. For example, the burgeoning ubiquity of genomic information in the clinical setting, from the increased use of genetic tests as an element of clinical care to direct to consumer testing results provided by patients, has galvanized efforts to integrate these data into the medical record. 7,8 This surge has also spurred the development of means of providing of relevant clinical knowledge and patient-specific information related to medical genomics, known as genomic clinical decision support (CDS). [9][10][11] However, the foundations for most integration and genomic CDS efforts, and indeed for the basis for many clinical genetic tests, rests in a body of work severely limited by the diversity of its participant populations. 12 In some cases, these shortcomings have been identified; yet without adequate data on diverse populations, we cannot truly know the extent of these limitations, potentially compounding already gaping disparities in health.Genomic integration into EHRs therefore comes with both significant caveats and substantial opportunity. On the one hand, care must be taken to assess the utility of genomic testing and the meaning of genomic results in specific contexts. 11,[13][14][15][16] On the other, the nature of EHRs and the facility with which they can be analyzed as a dataset allows health care systems to evaluate the effects of genetically enabled care on health outcomes, which have the potential to add not only to the improvement of patient care directly, through the refinement of clinical practices, but also indirectly through enhancement of the body of knowledge upon which biomedical discov...

Enhancing Diversity to Reduce Health Information Disparities and Build an Evidence Base foR Genomic Medicine

“…This is clearly an important step forward[15] although the limited scope of required race and ethnicity measures has been criticized for not allowing sufficient granularity to self-report and distinguish among racial or ethnic groups with different health outcomes[16]. The lack of required environmental and social measures is another limitation[17]. With an eye toward incentivizing the inclusion of specific social and behavioral domains in electronic health records, the National Academy of Sciences Board of Population Health and Public Health Practice convened a committee in 2013 to recommend essential data elements for a robust LHS[18,19].…”

Towards a Responsible Transition to Learning Healthcare Systems in Precision Medicine: Ethical Points to Consider

“…In genomics-guided precision medicine, this means that data collection ideally comprises entire exome or genome sequences (if available) [38,39]. Subsequently, genomic data need to be linked with broad and granular data encompassing entire clinical trajectories, from patient characteristics via diagnostics and treatment decisions to outcomes [21,40]. All of these factors render precision medicine data more difficult to de-identify and also easier to re-identify, increasing the risk of privacy violations.…”

Electronic health records for the diagnosis of rare diseases

“…Furthermore Blizinsky et al developed a framework to take into account the variability of population, in particular the ancestral, social and environmental factors that must be included in the genomic-enabled learning health system. They highlight the potentiality of learning health system to improve equity of care in the age of precision medicine 113 .…”