Nursing’s Role in Genomics and Information Technology for Precision Health


Nursing’s Role in Genomics and Information Technology for Precision Health

Kathleen A. McCormick / Kathleen A. Calzone


In a recent article, the new team required in precision health is described as doctors, nurses, pharmacists, geneticists, and genetic counselors (McCormick, 2017). This chapter describes precision health and updates the professional nursing role in genomics throughout the continuum of care. This chapter also describes new technologies to integrate genomics into the EHR and the nursing process in the EHR.

Nursing has a broad role in precision health encompassing preconception and prenatal assessment and counseling, newborn screening, risk identification, disease screening, disease prognosis, therapeutics, symptom science, and even utilization of omic data after death that will be described in this chapter. Pharmacogenomics provides a nursing role in precision health since it is relevant across the continuum of care from birth to death. Advanced practice nurses prescribe medications, and registered nurses administer medications and document medication outcomes and adverse drug reactions. This chapter focuses on documenting pharmacogenomics since this has clinical relevance to all nurses regardless of academic training.

This chapter describes four areas that the nursing professional can integrate precision health into nursing informatics documentation through the nursing process in the EHR to improve the quality of care and patient outcomes: (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity; (3) Medication Administration and Documentation, and (4) Evaluation of Medication Adverse Reactions. These recommendations have recently been included in a new policy brief recommended by the American Academy of Nursing (AAN), entitled “Strengthen Federal and Local Policies to Advance precision health Implementation and Nurses’ Impact on Healthcare Quality and Safety” (Starkweather et al., 2018). This chapter concludes with challenges going forward and the educational competencies recommended by the profession for nurses. Since genomics and nursing informatics are dynamic sciences, additional resources to keep up-to-date with information are provided in the chapter.

The relationship between omic science and nursing informatics is summarized later in this chapter in Fig. 39.4, depicting not only the computational biology for testing genomics, but also the need to integrate the information into clinical electronic health data and population health data. This figure has been recently developed by McCormick and published by Whende Carroll in 2020 in a book entitled Emerging Technologies for Nurses: Implications for Practice (McCormick, 2020).


In 2016 the 21st Century Cures Act was signed into law by Congress (NIH, 2017). The law supports the Department of Health and Human Services (HHS) to pursue precision medicine by advancing disease prevention, diagnosis, and treatment, as well as implementing greater data sharing of genomic information. A part of this mandate to the National Institutes of Health (NIH) is the All of Us Research that aims to collect clinical, personal, environmental, and genomic information from 1 million or more Americans from diverse ethnic backgrounds. The project will collect data on different lifestyles, environments, and biology to discover pathways toward the delivery of precision medicine. The project will accelerate precision health by using tracking wearables and other home devices and technologies to measure personal health and correlate them with health outcomes.

Precision health aims not only to cure diseases but also to prevent disease before it becomes manifest and improve symptom management during diagnosis and treatment in acute and chronic illness (NINR, 2017). This chapter adapts the model of health that Schroeder in his 2007 Shattuck Lecture has presented. It describes the contributions to Health versus Medicine as involving lifestyle and behaviors (40%) such as smoking, obesity, stress, nutrition, blood pressure, alcohol and drug use; genomics (30%) related to human biology; environment (20%) including social circumstances, and environmental exposure; and Access to Healthcare (10%) (Schroeder, 2007). Figure 39.1 depicts the contribution that genomics plays at the core of Precision Health, and the other contributors to health that require nursing care coordination.

Nursing’s History in Genomics Standards and Documentation Standards in Preparing for Precision Health

Nursing has not been a passive bystander in the history of genomics. Of relevance is the (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity are the American Nurses Association’s (ANA) addition of the concept of genomics to the third edition of the Nursing Informatics: Scope and Standards of Practice (ANA, 2014). These standards inform nurses that they must be able to “incorporate genetic and genomic technologies and informatics into practice” and “demonstrate in practice the importance of tailoring genetic and genomic information and services to clients based on their culture, religion, knowledge level, literacy, and preferred language.”

Of relevance to (3) Medication Administration and Documentation, and 4) Evaluation of Medication Adverse Reactions implementing pharmacogenomics into nursing practice are the professional practice license mandates on medications administration ordered by a physician or nurse practitioners (NPs). ANA’s Principles for Nursing Documentation: Guidance for Registered Nurses nursing documentation standards indicate nurses must assess if the medication is appropriate to the patient’s diagnosis, if the dose is appropriate, what the reaction to the medication is, and whether there are adverse reactions to the medication (ANA, 2010).


• FIGURE 39.1. Contributors to Precision Health. (Adapted from Schroeder, S. A. (2007). Shattuck Lecture. We can do better—improving the health of the American people. New England Journal of Medicine, 357(12), 1221–1228.)


Figure 39.2 is an image of the continuum of care that nurses have a history of engaging in genomics and some of the health conditions and genomic variants most commonly detected (McCormick & Calzone, 2016). The more than 3.9 million nurses in the United States and most nurses worldwide are familiar most with the use of genomics in the preconception and prenatal healthcare period. Family history is vital when interviewing parents for health conditions that they and their families may carry which could be passed down to their children. In the preconception period, the use of genomics can include testing for carrier status before pregnancy, often for autosomal recessive disorders such as MUTYH-associated polyposis, beta-thalassemia, or sickle cell trait (Ioannides, 2017). Additionally, individuals found to harbor a highpenetrance pathogenic susceptibility genetic variant may consider preimplantation genetic screening/diagnosis to avoid passing that variant on to their children (SullivanPyke & Dokras, 2018). In the prenatal period, noninvasive prenatal screening now can include cell-free fetal DNA testing (Badeau et al., 2017).


• FIGURE 39.2. Nursing’s Engagement in Genomics throughout the Continuum of Care with Examples of Some Diseases, Symptoms, and Disorders.

Another area in the continuum that nurses are familiar with is newborn screening. Figure 39.2 lists some of the recommended screenings from the Health Resources and Services Administration 2018 Recommended Uniform Screening Panel (RUSP) (HRSA, 2018). About 3% of babies have a serious birth defect detected from newborn screening according to the Centers for Disease Control and Prevention (CDC) (CDC, 2018).

Genomics use in risk assessment identifies individuals with an inherited predisposition, screening using genomic technology such as stool DNA testing for colon cancer screening and diagnosis to confirm a suspected diagnosis have accelerated in labs throughout the United States and globally with some genomic tests approved by the Food and Drug Administration (FDA) in the United States. There are approximately 75,000 genetic tests available from laboratories that have sprung up in every state of the union (Phillips, Deverka, Hooker, & Douglas, 2018). It is estimated that there are at least 10 new tests available per week. The cost of sequencing the genome has decreased from $100 million in 2001 to less than $1000 in 2017 rivaling costs of other medical tests or procedures (NHGRI, 2019).

The genomics in the healthcare continuum also provides us with an improved understanding of the disease which informs disease prognosis such as tumor gene expression to inform recurrence risk for breast cancer and therapeutic decisions. Understanding the disease is the area where the genomics of the disease such as cancer is used to match to treatments targeting that genomic defect, a rapidly advancing field of precision medicine. Genomics also identifies potentials to develop new therapeutic approaches, and mechanisms to evaluate treatment responses. Some of the common health risks are also listed in Fig. 39.2. Targeting treatments is another area where considerable growth and discoveries are occurring. There is currently 10,703 expert reviewed human genomic variations in a database called ClinVar (Clinical Genome Resource, 2019), and 2.4 million molecular assays reported in the Database of Genotypes and Phenotypes (dbGaP) (dbGAP, 2019). To date, the use of genomic testing for prognostic or therapeutic purposes is occurring in most healthcare environments and is no longer limited to large academic and specialty care hospitals (Williams, 2019).

The first FDA-approved companion diagnostic testing with Medicare coverage covers all solid tumors including non-small-cell lung cancer (NSCLC), colorectal, breast, ovarian, and melanoma. FoundationOne CDx can detect genetic variants in 324 genes and two genomic signatures in any solid tumor type (FoundationOne CDx, 2020).

The next area of the continuum focuses on prognosis and therapeutic decisions.

The final area of the continuum is monitoring disease progression through the use of new technology such as circulating tumor DNA and symptom management such as pharmacogenomics to inform pain management. Understanding and improving abilities to monitor treatment response and early evidence of disease recurrence are progressing using genomic technologies such as circulating DNA in cancer (Oellerich et al., 2019). Utilization of genomics to detect disease progression is also progressing, such as epigenetic changes in progressive Parkinson disease (Henderson-Smith et al., 2019). These discoveries help to inform not just the state of a given disease but provide the platform for the development of additional therapeutic options. Today, the research is progressing in almost all common health conditions including cardiovascular disease, stroke, cancer, arthritis, amyotrophic lateral sclerosis (ALS), HIV, multiple sclerosis (MS), type 1 and 2 diabetes, Parkinson disease, and depressive disorders.

Nursing has defined a special role in the precision medicine Initiative through nursing research in precision health. Nursing science develops and applies new knowledge in biology and behavior, including genomics and biomarker identification, to improve symptoms. The National Institute of Nursing Research (NINR) at the NIH focuses on nurses’ ability to better understand the symptoms of chronic illness, such as pain, dyspnea, fatigue, gastrointestinal disorders, impaired cognition and mood disorders, depression, traumatic brain injuries, and sleep disorders because of the advances in genomics (Cashion, Gill, Hawes, Henderson, & Saligan, 2016). The research agenda focuses on improved personalized strategies to treat with precise interventions and to prevent adverse symptoms of acute and chronic illness across the continuum of care for populations in diverse settings. The area of symptom science is promoting personalized health strategies through a strategic plan and national research agenda (Dorsey et al., 2019).


The biggest areas in the continuum that permeate all stages in the continuum of life and affect the largest numbers in the population are the areas of pharmacogenomics. Figure 39.2 depicts the importance of pharmacogenomics throughout the continuum beginning in infancy. In infancy, during attention deficit management, pain management in children, nursing mothers, and adults, clot management in cardiovascular and stroke disease, and chemotherapy, there are pharmacogenomic potentials for nursing assessment and observations of adverse drug reactions.

Variations in the human genome, specifically DNA sequence variants, could affect a drug’s pharmacokinetics (PK), pharmacodynamics (PD), efficacy, and safety. The genetic differences likely to be the most pertinent in nursing assessment are those associated with genes in four broad categories: (1) genes relevant to the drug’s PK related to absorption, distribution, metabolism (including formation of active metabolites), and excretion (ADME); (2) genes that code for intended or unintended drug targets and other pathways related to the drug’s pharmacological effect; (3) genes not directly related to a drug’s pharmacology that can predispose to toxicities such as immune reactions; and (4) genes that influence disease susceptibility or progression.

The fate of drugs in the body depends upon ADME. Pharmacogenomics combines the science of drugs and their metabolism, with the genetics of enzymes that metabolize drugs to develop effective medications, safe medications, and doses tailored to the person’s genetic profile. An excellent literature review summary of pharmacogenomics and its implications for nursing practice was published in 2015 (Cheek, Bashore, & Brazeau, 2015).

Since then, more precision has gone into the study of pharmacogenomics, and there are now 46 guidelines with sufficient evidence from systematic reviews of the literature to integrate into EHR and healthcare professional decision-making. Today, genomic testing in pharmacogenomics determines if it is the right drug, for the right person, at the right dose regardless of age (Collins & Varmus, 2015).

Pharmacogenomics and Nursing Documentation

Pharmacogenomics is an important factor in precision health translated to nursing documentation of medication administration and observation of adverse reactions. Thanks to the Pharmacogenomics Knowledge Base (PharmGKB) supported by the NIH, collaborations of scientists, researchers, pharmacists, and clinicians are collating data and disseminating information on the evidence between human genomic variation and individualized drug pharmacogenomics. Table 39.1 is a summary of the rating criteria for the level of evidence used to rate the systematic review of the literature and to recommend changing a prescription. The guideline methodology ranks the level of evidence like the methodology for developing clinical practice guidelines from the U.S. Preventative Services Task Force. Only those pharmacogenomic guidelines that are ready for implementation with level A evidence are recommended for translation into clinical practice.

TABLE 39.1. Levels of Evidence for CPIC Guidelines


Currently the A-level of evidence on several drug categories is published in the Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines. There are currently 24 CPIC guidelines that have 62 medications associated with them (Clinical Pharmacogenetics Implementation Consortium [CPIC] Guidelines, 2020). Table 39.2 is the current list of practice guidelines by categories of drug type, drugs, and genes regulating their metabolism. The list includes many common drugs used in clinical practice. The guidelines are meant to help clinicians optimize drug therapy based on available genetic test results and observe for adverse reactions if drugs cannot be metabolized by individuals.

TABLE 39.2. List of 24 CPIC Guidelines, 62 Drugs Associated with Them, and Genes



On the PharmGKB Web site ( there are also 93 guidelines listed from the Royal Dutch Association for the Advancement of Pharmacy–Pharmacogenetics Working Group (DPWG), 8 guidelines from the Canadian Pharmacogenomics Network for Drug Safety (CPNDS), and 17 other guidelines (from professional organizations) (PharmGKB, 2020). Besides CPIC consortia, international consortia are summarizing the body of evidence for similar drugs listed in the CPIC guidelines, and additional drugs and genes found in their countries and populations.

Also, the FDA has a list of Pharmacogenomic Biomarkers in Drug Labeling. There are currently about 404 drugs with genetic biomarkers that are included in existing drug labeling (FDA, 2020). One might expect that these will be studied with sufficient evidence to incorporate into the CPIC clinical guidelines in the future.

From CPIC Guidelines to Dissemination, Implementation, and Measuring the Quality of Care and Cost Impact

Just as there are teams of scientists and clinicians working on the development of CPIC guidelines, there are also teams disseminating and implementing the CPIC guidelines. A model for implementation described by Hicks et al. at the Moffitt Cancer Center, North Shore University, Boston Children’s Hospital, and St. Jude includes the transfer of a guideline into a workflow algorithm, then incorporates the guideline into the EHR via Computer Decision Support (CDS) (Hicks, Dunnenberger, Gumpper, Haidar, & Hoffman, 2016; Hoffman, et al, 2016). A group has also published similar integration and evaluation of the CPIC guidelines into CDS and the EHR at Mayo Clinic (Caraballo, Bielinski, St Sauver, & Weinshilboum, 2017). The evaluations of the outcome of implementing the guidelines on patient quality, safety, and costs are ongoing but the reviews are being summarized in Webcasts and knowledge presentations throughout the country, and the variance on patient race and ethnicity are being reported in publications.

Levels of evidence translated into clinical practice and impacting costs of healthcare are very promising. While these studies have not included nursing impact per se, their effects of the patients’ quality and healthcare have been documented. A recent review of studies, developed by scientists for Translational Software, summarizes the economics of pharmacogenomics in several categories: Clopidogrel and Percutaneous Coronary Intervention, Psychiatric Pharmacogenomics, Polypharmacy, DPYD and Fluoropyrimidines, and Abacavir. The implications from reviews of 24 international studies were that pharmacogenomic testing was not only cost-effective but often cost-saving when drugs on the list of CPIC guidelines were studied (Translational Software, 2019). The most significant benefit was demonstrated in Psychiatric Pharmacogenomics. The mean cost savings in depression when pharmacogenomic testing was used, instead of trial and error drug treatments, was $3,000 per patient per year. Multiplying that cost by the number of people who are diagnosed with depression per year, that could be a cost savings of several billion dollars per year. Another area of cost savings included reductions in adverse drug reactions. The authors acknowledge that further testing is required in many categories of pharmacogenomic tests and guidelines.


Four ways that nursing informatics can participate in Precision Health are (1) Documentation of a Rapid Risk Assessment; (2) Family History and Ethnicity; (3) Medication Administration and Documentation, and (4) Evaluation of Medication Adverse Reactions. These are depicted in Fig. 39.3 integrated with the current Nursing Process, proposed in a recent publication by McCormick and recommended in the AAN policy brief recommendations (McCormick, 2017; Starkweather et al., 2018). These areas can improve care quality and patient outcomes, and safety which are also the future goals of the Quadruple Aim.

Documentation of Rapid Risk Assessment

Putting genomics and pharmacogenomics advances into nursing practice includes enhancing the assessment by putting a RAPID risk assessment into the nursing process (Maradiegue &Edwards, 2016). The RAPID risk assessment includes the following: (1) assess the family history (usually recommended for at least three generations). Assess if they or anyone in their family has had a problem metabolizing drugs. (2) Identify the patient’s ancestry and ethnicity. This is becoming more important because, for example, patients from Ethiopia have an increased risk of toxicities based upon how they metabolize codeine. (3) Establish the probability of genetic condition or predisposition to an adverse drug reaction. Consult with a geneticist, genetic counselor, genetic nurse, pharmacist, and physician to determine a possible susceptibility to an adverse drug reaction after consulting the CPIC guidelines.

Documentation of the Family History and Ethnicity

The family history is also known as a family health portrait or pedigree map. Family history is a record of first-, second-, and third-degree family and their medical information about an individual including the age of onset of health conditions, race and ethnicity, and age and cause of death in their biological family. Human genetic data of family members is becoming more prevalent and more accessible to obtain because of direct-to-consumer (DTC) screening and ancestry testing (FDA, December 2019). These data are being used to identify the risks of developing common diseases and a genetic disease that runs in families. Specific to cancer, the assessment of risk is thoroughly discussed in the NCI Physician Data Query (PDQ) site for professionals (NCI, 2019). The United States Public Health Service (USPHS) Surgeon General recommends that during Thanksgiving Dinner, each family determines the history of family illnesses to add to the family history map. The facility to create a free Family History is available in a tool on the CDC Web site (CDC, 2019).


• FIGURE 39.3. Integration of Nursing Process with Pharmacogenomics and Genomic RAPID Risk Assessment. (Adapted, with permission, from McCormick, K.A. (2017). Together into the future… Pharmacogenomics and documentation. Nursing Management, 48(5), 32–40.)

Family gatherings present a time when families sometimes disclose ancestry discrepancies, or paternity hidden secrets including if the children are adopted or there is misattributed paternity. Approximately 28% to 30% of the time it is not the biological father who is the perceived father in the family (McCormick & Hoffman, 2006). If and/or when they share their findings with nurses, who are still considered the most trusted healthcare professionals, ethics counselors and lawyers may have to be consulted.

But how are the nurses going to record family history and ethnicity if there is no place in the EHR to record it? In a recent study of Magnet® hospitals, nursing administrators played a critical role in including the ability to document family history which includes ethnicity in the EHR (Calzone, Jenkins, Culp, & Badzek, 2018). One example of nurses integrating Family History, ethnicity, and pharmacogenomics into an EHR is occurring at the NIH Clinical Center (CC). In a March 2018 presentation at the HIMSS 2018 annual conference, two nurses from the NIH CC presented the plans for integration of genomics and Family History into their EHR. The NIH CC uses Allscripts with precision medicine functionality from the 2bPrecise precision medicine Knowledge Hub, a technology platform that integrates genomics with phenotypic data and plans on integrating with clinical workflow (Wallen & Lardner, 2018). They recommended a twopronged approach: (1) assess the limitations of the EHR for genomics, and (2) evaluate the preparedness of the nurses for genomics. While they found it was harder than they thought initially to integrate the family history, they also stressed the importance of the nurse’s role in expanding the family history in nursing documentation to include a family history or pedigree map in the EHR. In preparing nurses they recommend the Method of Introducing a New Competency (MINC) Implementation Model (MINC, 2019) that includes assessment of knowledge by nurses in genomics, providing staff development, assessment of hospital policy, providing staff knowledge, conducting professional development, anticipating obstacles and challenges, planning for integration into the EHR, and educating nurses how to use the tools (Wallen & Lardner, 2018).

The Need to Document Ancestry and Ethnicity The need to document ancestry and ethnicity in the EHR is becoming more critical as we examine the genetic differences in metabolizing drugs (as well as risk, tumor identification, and treatment) in populations throughout the globe (Manolio et al., 2015). Centers around the world are identifying ethnically related diseases associated with ethnic groups, and deficiencies in enzymes that help metabolize certain drugs in the CPIC guidelines and those under investigation by the FDA (FDA, 2019). For nurses working in the United States, ethnicity is relevant because the population of patients we see in hospitals, outpatient services, and retail pharmacies is a mix of many ethnicities.

Nursing Role in Medication Administration and Documentation

As previously stated, the nursing profession has license requirements and professional standards for nursing medication administration and documentation. The previous standards charged nurses with the five rights: the right patient, right dose, right drug, right route, and right time. Today with the CPIC guideline implementation and the foundation of pharmacogenomics in Precision Health in diverse ethnic populations, it is the Right Drug, for the Right Person, at the Right Dose regardless of age (Collins & Varmus, 2015).

At the HIMSS annual conference in March 2018, Dr. McKeeby, the CIO of the NIH CC, and Dr. Jhanana Patel, Pharmacy Information Officer, described the integration of Pharmacogenomics within the EHR (McKeeby & Patel, 2018). Most patients at the NIH CC are genotyped because they are on complex research protocols for diagnosis and treatment of disease. The study determined how individual genetic inheritance affected the individual patient’s response to medications. They developed Computer Decision Support (CDS) to integrate the Pharmacogenomic testing to provide personized drugs for greater efficiency and safety of outcomes. Key to implementation was the composition of their team that included doctors, pharmacists, laboratory medicine personnel, nursing, and IT representatives. The project is determining which drugs require a point of contact decision support tool and further recommendations on who should receive Pharmacogenomic (PGx) testing.

A review paper summarizing the integration of pharmacogenomics and decision support tools has been developed by the Translational Pharmacogenetic Program (TPP), a subgroup of the Pharmacogenomics Research Network. This group includes Mayo Clinic, Ohio State University, St. Jude Children’s Research Hospital, University of Florida, University of Maryland, Vanderbilt University Medical Center, the University of Chicago and Brigham and Women’s Hospital. Their goal is to determine models for implementing pharmacogenomics in diverse healthcare system environments with diverse patient populations (Dunnenberger et al., 2014). TPP is among the first groups to identify and overcome real-world barriers to adoption of evidence-based pharmacogenetics and to propose solutions to broad-based dissemination to healthcare professionals.

Clinicians published a recent paper representing the integration of preemptive genomics and pharmacogenomics into the EHR used for decision-making at the University of Chicago (Borden & O’Donnell, 2018). They developed a genomic prescribing system. Unlike the CC at NIH, they were trying to determine which patients should have genetic testing matched to pharmacogenomic guidelines. Since the prescribing clinicians were not familiar with genomics or pharmacogenomics, they used red light, yellow light, and green light to determine the risk of a patient receiving a drug that they could not metabolize. They analyzed 2200 outpatients, and 546 had genotyping. They found that one-third of the medications that the patients were taking were associated with pharmacogenomic information. Medication change rates occurred when the genomic prescribing system alerted clinicians to red lights; that is, there was an indication that the drug should not be given. Indications that a drug should not be given occurred in 26 patients. Not only did clinicians feel they were delivering quality care, but patients were pleased with the clinician determining what drugs they should not take. Like the NIH CC, the University of Chicago created their interpretation of the genetic tests to make them more understandable to clinicians.

Evaluation of Medication Adverse Reactions

The drugs listed in the CPIC guidelines are linked to adverse reactions when the drug does not work the same way in all persons. Medications are broken down in the liver by enzymes that may be affected by genomics. For example, in some persons, the enzyme is defective, or the person does not make the enzyme at all. In pain management, this is known to happen in persons taking codeine, who do not have the liver enzyme that converts codeine to morphine. A gene called CYP2D6 produces the enzyme that can convert codeine to morphine. Some people have variations in CYP2D6, so they don’t produce the enzyme at all. The codeine then cannot effectively help manage the pain. Persons from Ethiopia have a higher likelihood of having CYP2D6 variations that result in enzyme deficiencies.

Another drug that is commonly used as a blood thinner after myocardial infarction, valve repairs, recent stroke, thrombus, heart transplant, or other coronary events is clopidogrel. The enzyme CYP2C19 has ultrarapid, extensive, intermediate, and poor subtypes (CPIC Guidelines, 2020). A person who cannot metabolize it may return to the emergency room or doctor’s office with recurrent blood clots and may need to be managed with prasugrel which would not interfere with the enzyme deficiency. Related to ethnicity, variants in CYP2C19 are common in persons with Asian ancestry.

Warfarin is another drug that is used to prevent clotting in persons with arrhythmias, deep vein thrombus, after coronary surgery, and extensive orthopedic surgery. The CYP2C9, VKORC1, and CYP4F2 are genes known to be associated with responses leading to excessive bleeding in patients (CPIC Guidelines, 2020). These may result in the nurse observing bloody nose, blood in urine, and excessive bleeding events in patients.

Abacavir is an antiretroviral drug used alone or in combination with other drugs in the treatment of HIV-1 infection (CPIC Guidelines, 2020). The HLA genes are specific for abacavir and can result in drug hypersensitivity reactions that can range from skin reactions such as eczema, urticaria, and angioedema, to severe reactions like severe cutaneous adverse reactions (SCARs). The sensitivities may be life-threatening and include drug reactions with eosinophilia and systemic symptoms DRESS/DIHS and Stevens–Johnson syndrome/toxic epidermal necrolysis (SJS/TEN). Sometimes the hypersensitivity reactions result in fever or rash or can affect the gastrointestinal tract and include nausea, diarrhea, vomiting, and stomach pain. In patients with hypersensitivity to abacavir, they could also have respiratory symptoms of cough, shortness of breath, and sore throat within the 6 weeks of treatment.

In each of the examples, nursing assessments and observations are critical in determining if adverse drug reactions are occurring. New studies need to be conducted on nursing process documentation to determine retrospectively if adverse reactions observations were noted, and prospectively if adverse reactions could be prevented if guidelines were linked to computerized decision support systems in the EHR.

One public database that nursing informatics should link to is the FDA Adverse Event Reporting System (FAERS) from the public dashboard. The name of the drug is entered, and the information on adverse reactions is reported (FDA, 2020). Another database that nurses in informatics may want to consider using is a terminology database of adverse reactions called Common Terminology Criteria for Adverse Events (CTCAE) (CTEP, 2019). Although developed for cancer and chemotherapy drugs, the site has a wide range of drug adverse event terms. The earlier terms were linked to MedDRA® v21 which has been used to codify terminology for drug and adverse event coding and is used globally.


Going forward Precision Health will be a challenge to integrate the necessary information for nursing assessment, documentation, and assessment of adverse drug reactions and outcomes into the EHR. The technologies involve enormous amounts of data integrated into networks of health information. These data require additional storage (clouds), levels of security, unique patient identifiers, computer decision support tools, artificial intelligence, machine learning, and analytics to evaluate the quality and outcomes of these big data analytics. The concepts of artificial intelligence, machine learning, and analytics are described in Chapter 37 of this book by Koski and Murphy. Further research is required on the cost impact and cost savings of using pharmacogenomic decisions and symptom management in nursing care.

In an effort to visualize the components of all of the information technology that incorporates the biotechnology informatics of the omics (whether genomes are from a person, a cancer tumor, or a virus of a pandemics), McCormick developed a diagram to incorporate the systems required for integration. The diagram in Fig. 39.4 includes population health captured from surveillance data to personal health data integrated into the EHR and personal health records. The omics components require testing and computational biology (bioinformatics) to determine cell signaling and function. The elements of the diagram are more complexly described by McCormick (2020). The omics components are adapted from a publication by Regan, Engler, Coleman, Daack-Hirsch, and Calzone (2018).

In the Definitive Healthcare 2019 Survey of precision medicine in Healthcare, providers identified their top challenges concerning implementing precision medicine initiatives (Definitive Healthcare, 2019). Those challenges, in order of significance, were identified as (1) cost; (2) reimbursement challenges; and (3) patient compliance. Of those surveyed, 33% cited a lack of expertise as a barrier in going forward with a precision medicine program (Definitive Healthcare, 2019).



As previously mentioned, the first FDA-approved genomic tumor profiling with Medicare coverage includes all solid tumors, including non-small-cell lung cancer (NSCLC), colorectal, breast, ovarian, and melanoma. On March 16, 2018, the Centers for Medicare & Medicaid Services (CMS) announced CMS reimburses for 324 genes and two genomic signatures in any solid tumor so that therapies can be targeted (CMS, March 16, 2018). CMS took action to advance innovative personalized medicine for Medicare patients with cancer. CMS finalized the National Coverage Determination that covers diagnostic laboratory tests using Next Generation Sequencing (NGS) for patients with advanced cancer (i.e., recurrent, metastatic, relapsed, refractory, or stage III or IV cancer). CMS attests that when these tests are used as a companion diagnostic to identify patients with specific genetic mutations that may benefit from FDA-approved treatments; these tests can assist patients and their oncologists in making more informed treatment decisions. Additionally, when a known cancer mutation cannot be matched to treatment then results from the diagnostic lab test using NGS can help determine a patient’s candidacy for cancer clinical trials.


• FIGURE 39.4. Relationship Between Omics Science and Informatics. (With permission from McCormick, K. A. (2020). Precision health and genomics. In W. Carroll, Ed., Emerging technologies for nurses: Implications for practice (pp. 155–184). New York, NY: Springer. Omics adapted from Regan, M., Engler, M. B., Coleman, B., Daack-Hirsch, S., & Calzone, K. A. (2018). Establishing the genomic knowledge matrix for nursing science. Journal of Nursing Scholarship, 51, 50–57.)

Coverage decisions were made following the parallel review with the FDA, which granted its approval of the FoundationOne CDx (F1CDx™) test on November 30, 2017 (FDA, November 30, 2017). At the same time, CMS issued a proposed National Coverage Determination for NGS cancer diagnostics. F1CDx™ is the first breakthrough-designated, NGS-based in vitro diagnostic test that is a companion diagnostic for 15 targeted therapies as well as can detect genetic mutations in 324 genes and two genomic signatures in any solid tumor.

Relevant to the impact of pharmacogenomics on Precision Health, a map of the United States and the CMS Medicare Administrative Contractors (MAC) who administrate reimbursement is provided on the IGNITE map page. At this time all the regions are currently reimbursing for the following genes: CYP2C19 for patients undergoing Percutaneous Coronary Intervention for stent procedures following up with Clopidogrel therapy, CYP2D6 for therapy with Amitriptyline/Nortriptyline (for depression) or Tetrabenazine, CYP2C9 for Warfarin treatment in anticoagulation, and VKORC1 for anticoagulation therapy (IGNITE, 2019).

As the science moves further into areas as rheumatology, cardiovascular disease, neurological diseases, and behaviors, the challenge of funding will have to be reevaluated.


The privacy and discrimination concerns regarding the use of genetic and genomic data in healthcare raise new ethical and legal concerns where the potentials for genomics are being used for not only treatment but also enhancements to embryos and humans. The Genetic Information Nondiscrimination Act of 2008 (referred to as GINA) is a federal law that was enacted to prevent discrimination in health insurers and employers based upon genomic information. After GINA was passed, it is recognized that the current law does not include military personnel, nor does it cover persons acquiring life insurance, disability insurance, and long-term care insurance. The AAN Policy Brief entitled “Strengthen Federal and Local Policies to Advance Precision Health Implementation and Nurses’ Impact on Healthcare Quality and Safety” recommends enhancements to Health Insurance Portability and Accountability Act (HIPAA) and GINA that are appropriate to Precision Health Implementation (Starkweather et al., 2018). HIPAA does not address the broader security needed in patient records that include genomic information. Some of these ethical issues are being discussed by NIH and HHS for research subjects. The nursing professionals need to be vigilant, monitoring the policies and laws governing genomic data that protect the healthcare consumers.

Educating Nurses to Achieve Genomic Competency in the Era of Precision Health

The rapidity of discoveries and uptake of the genomics into healthcare and society is driving the need for nurses competent in genomics in academia, practice, research, and education. The AAN policy brief recommended the following: sufficient education and continuing education on genomics and implementing Precision Health; the integration of data sources into the information technology infrastructure to provide clinical support for healthcare providers to document a rapid risk assessment, ethnicity, and family history; including CPIC guidelines for clinicians and computerized decision supports; and the ability to document adverse drug reactions in addition to other recommendations outlined in the policy (Starkweather et al., 2018). Additional resources to gain competency in genomics for nursing are presented in Table 39.3.

TABLE 39.3. Genomic Education Resources for Nurses

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Jul 29, 2021 | Posted by in NURSING | Comments Off on Nursing’s Role in Genomics and Information Technology for Precision Health

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