Clinical Decision Support for Pharmacogenomic Precision Medicine: Foundations and Implementation

Clinical Decision Support for Pharmacogenomic Precision Medicine: Foundations and Implementation offers overviews, methods and strategies for translating genomic medicine to clinical practice. The book's authors explore incorporating pharmacogenetics into electronic health records, CDS methods and infrastructure for delivery, economic evaluation, the hospital administrations’ role and needs in integration, and patient counseling aspects. The book empowers clinicians, researchers, translational scientists, and data and IT experts to effectively navigate the complex landscape of CDS for pharmacogenomic precision medicine. Illustrative case studies of existing gene networks include CSER, eMERGE, the IGNITE network, DIGITIZE, the CDS Learning Network (RTI), ClinGen, Ubiquitous and CDS Hooks.

• Offers an applied, case-driven discussion of CDS for pharmacogenomic precision medicine
• Illustrates key concepts, contemporary developments, and future directions using examples of existing gene networks
• Features contributions from leading voices in precision medicine and clinical decision support

• Language: English
• Publisher: Academic Press
• Release date: Jun 14, 2022
• ISBN: 9780128244548

Book preview

Section One

Foundations of clinical decision support and pharmacogenomic precision medicine

Outline

Chapter One. Overview of effective pharmacogenomic clinical decision support

Chapter Two. Scientific evidence and sources of knowledge for pharmacogenomics

Chapter Three. Laboratory considerations for pharmacogenomic testing

Chapter Four. Advancing equity in the promise of pharmacogenomics

Chapter One: Overview of effective pharmacogenomic clinical decision support

Richard David Boyce ¹ , Jhon Camacho ² , Wayne Liang ³ , Kristin Wiisanen ⁴ , and Beth Devine ⁵       ¹Biomedical Informatics and Clinical and Translational Science Institute, Secondary Appointment in the School of Pharmacy, Secondary Appointment in the Intelligent Systems Program, School of Computing and Information, Biomedical Informatics Training Program, University of Pittsburgh, Pittsburgh, PA, United States      ²Medical Informatics Solution, Architect I&E Meaningful Research, Bogotá, Colombia      ³Emory University School of Medicine, Pediatric Bone Marrow Transplant Physician and Physician Informaticist, Children’s Healthcare of Atlanta, Emory School of Medicine, Emory University, Atlanta, GA, United States      ⁴UF Health Precision Medicine Program, Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States      ⁵The Comparative Health Outcomes, Policy and Economics (CHOICE) Institute, School of Pharmacy; Department of Health Systems and Population Health, School of Public Health; Department of Biomedical Informatics, School of Medicine; Member, Institute for Public Health Genetics; Member, Plein Center for Geriatric Pharmacy Research, University of Washington, Seattle, WA, United States

Abstract

Pharmacogenomics is focused on how genomic variations among individuals affect their different responses to medications and is an important subfield of precision medicine, which centers on how individual variation in biology, environment, lifestyle, and preferences impacts the prevention, diagnosis, and treatment of diseases. A clinical decision support system is a computer program that is actively involved in the clinical decision-making process by delivering patient-specific recommendations to facilitate high-quality patient care. Taken together, these are essential elements of implementing pharmacogenomic precision medicine through clinical decision support. The design, implementation, and maintenance of successful pharmacogenomic clinical decision support involves many stakeholders and requires consideration of several topics ranging from knowledge sources to methods to clinical and counseling considerations. This chapter defines and introduces foundational concepts of pharmacogenomics and clinical decision support and serves as a guide to navigate this textbook.

Keywords

Clinical decision support; Five rights of clinical decision support; Genomics; Healthcare; Information systems; Personalized medicine; Pharmacogenomics; Pharmacy; Precision medicine

Introduction

Pharmacogenomics and precision medicine

Potential benefits of CDS

Role of CDS in pharmacogenomic precision medicine

Developing and implementing CDS for pharmacogenomic precision medicine

Other considerations for pharmacogenomic CDS

Overview of this book

Conclusion

References

Introduction

Pharmacogenomics is the field of study focused on how genomic variations among individuals affect their different responses to medications, with a particular focus on preventing adverse drug effects caused by genetic predisposition. It is an important subfield of precision (or personalized) medicine, the field of study that focuses on how individual variation in biology, environment, lifestyle, and preferences impacts the prevention, diagnosis, and treatment of diseases. The philosophy of precision medicine is to obtain a comprehensive description of an individual's specific traits and characteristics (i.e., a phenotype) using genomic and/or other advanced methods in order to provide the most appropriate medical intervention that optimizes benefit and minimizes negative consequences for the individual. This approach differs from conventional medicine that tends to provide medical interventions that, on average, benefit individuals within a population, without the use of phenotype data that predicts that an individual patient's likely response. Put simply, precision medicine aims to provide a patient with the most effective individualized treatment, while conventional medicine provides the patient with the treatment that is most effective on average. Box 1.1 provides a brief example of the precision medicine perspective on patient care.

Box 1.1

Precision medicine clinical vignette

A 60-year-old Asian woman presents to an emergency room with acute, severe chest pain. She underwent a medical workup and was diagnosed with an acute myocardial infarction due to a severe blockage in a major coronary artery. She underwent emergent cardiac catheterization to open up the blockage and was started on clopidogrel, a glycoprotein (GP) IIb/IIIa inhibitor with antiplatelet activity. A CYP1C19 pharmacogenomic panel test was ordered as part of the institutional clopidogrel clinical decision support order set. The patient was admitted to the hospital. Three days later, the treating physician received an electronic critical laboratory value alert. The pharmacogenomic panel test revealed that the patient has a homozygous CYP2C19∗2/∗2 loss of function variant, resulting in a poor metabolizer phenotype associated with decreased effectiveness of clopidogrel in preventing major cardiac events following an acute myocardial infarction. The alert also provided guidelines-based recommendations for switching to an alternative antiplatelet agent unaffected by this variant. The patient was switched to an alternative antiplatelet agent and was discharged from the hospital. She continues to take this medication and has not experienced any additional cardiovascular events.

Pharmacogenomics and precision medicine

In pharmacogenomic medicine, healthcare providers obtain a profile of a patient's pharmacogenomic variants (i.e., genomic variants relevant to their response to specific medications), in order to select the right pharmacological intervention predicted to maximize therapeutic benefit and minimize harm from lack of effectiveness or from excessive toxicity. Individuals who receive therapeutic benefit are termed responders, while those who experience lack of effectiveness are termed under- or nonresponders, and those who experience excessive toxicity are termed toxic responders. Other subfields of precision medicine not covered in this textbook include precision diagnostics (i.e., obtaining and defining a more accurate or precise diagnosis of an individual's disease through advanced methods such as genomic testing), targeted therapies (i.e., developing and providing treatments tailored to an individual's biological profile), and gene therapy (i.e., altering a patient's genetic or genomic makeup for therapeutic effect).

Pharmacogenomic science is frequently applied to patient care through clinical guidelines. First, new pharmacogenomic variant knowledge is discovered through basic genomic research. Next, newly discovered pharmacogenomic variants are reported in the scientific literature and in variant databases. Expert panels then evaluate the scientific evidence and produce clinical guidelines to guide clinical decision-making. Collaboratives such as the Clinical Pharmacogenetics Implementation Consortium ([CPIC] (Relling & Klein, 2011); and the Dutch Pharmacogenetics Working Group (DPWG) are leaders in publishing openly available, peer-reviewed, and evidence-based pharmacogenomic clinical practice guidelines. These guidelines may then be adopted by healthcare providers and health provider organizations into clinical practice. Patient outcomes data may be collected and analyzed to generate new pharmacogenomic knowledge within the genomic learning healthcare system, or one in which clinical practice and research influence each other with the goal of improving the efficiency and effectiveness of disease prevention, diagnosis, and treatment (Genomics-Enabled Learning Health Care Systems: Gathering and Using Genomic Information to Improve Patient Care and Research: Workshop Summary, 2015). In general, a learning health system (LHS) approach is where a community forms and commits to learn and then apply new insights continuously. As Fig. 1.1 shows, a genomic learning healthcare system seeks to learn and apply new insights on how to best apply genomic information to arrive at the best clinical outcomes possible (Green et al., 2020). Pharmacogenomic knowledge is generated through basic genomic research, and is integrated into clinical care through practice innovations such as pharmacogenomic computerized clinical decision support (CDS) within electronic health record (EHR).

Figure 1.1  Genomic research and learning healthcare system virtual cycles.Basic genomic research informs genomic clinical care, and clinical care informs research. From Green, E. D., Gunter, C., Biesecker, L. G., Di Francesco, V., Easter, C. L., Feingold, E. A., Felsenfeld, A. L., Kaufman, D. J., Ostrander, E. A., Pavan, W. J., Phillippy, A. M., Wise, A. L., Dayal, J. G., Kish, B. J., Mandich, A., Wellington, C. R., Wetterstrand, K. A., Bates, S. A., Leja, D., … Manolio, T. A. (2020). Strategic vision for improving human health at The Forefront of Genomics. Nature, 586(7831), 683–692. https://doi.org/10.1038/s41586-020-2817-4.

Despite the availability of clinical guidelines, pharmacogenomics is not yet widely adopted within mainstream clinical practice, preventing many patients from fully benefitting from pharmacogenomic medicine. There are several factors that have slowed the adoption of pharmacogenomics. Healthcare payers have been slow to add reimbursement for pharmacogenomic testing and consultation because they require that such procedures have demonstrated analytical and clinical validity as well as clinical utility. Complicated reimbursement policies make it difficult for providers to know which of the dozens of pharmacogenomics tests will be covered by insurance. Prescribers tend to have a favorable perception of the potential value of pharmacogenomics testing (Amara et al., 2018; Deininger et al., 2020; Jameson et al., 2021). However, they express concerns about how to best apply the results to improve care, uncertainty about which sources of clinical pharmacogenomics knowledge are trustworthy, and concerns about the potential negative financial impact on patients. While pharmacists can help provide prescribing guidance specific to clinical phenotypes, pharmacogenomics training has only recently been added to pharmacy curriculums and many pharmacists find it hard to find relevant information in drug product labeling (Romagnoli et al., 2016).

Potential benefits of CDS

A clinical decision support (CDS) system is a computer program that is actively involved in the clinical decision-making process by delivering patient-specific recommendations to facilitate high-quality patient care. As computer programs, CDS systems are distinct from noncomputerized clinical decision aids. Therefore, clinical practice guidelines and other documents (even in digital format) are not considered CDS. A CDS system is active in the decision-making process of the clinician for each patient, as opposed to passively presenting normative information. Systems that fall outside this criterion could include a static flowchart depicting a decision algorithm or a mobile application that lists a series of recommendations but does not filter them (or otherwise process them) depending on each patient's condition. This does not mean that the CDS system needs to interact, call attention, or interrupt the health provider every time a decision needs to be made. Instead, a least-disruptive CDS should process a patient's health information in the background and present recommendations only when warranted.

CDS has the potential to address several barriers to broaden pharmacogenomics adoption. It could help to fill in clinicians' knowledge gaps about pharmacogenomic phenotypes and enable them to apply the most relevant care guidelines for each specific patient. The widespread adoption of EHRs that are capable of providing patient phenotype data is important for providing clinicians with contextualized and actionable pharmacogenomics alerts. It is also possible that the clinical actions suggested by a CDS tool could be linked to current information about reimbursement, thereby addressing cost concerns. Pharmacogenomic CDS could be directed at prescribers, to assist with decision-making during ordering, and also pharmacists, as another layer of protection for patients when prescription orders are filled and dispensed.

CDS involves delivery of patient-specific recommendations to facilitate high-quality patient care. Patient-specific recommendations mean that CDS's output needs to apply to the patient receiving care at the time. This requirement excludes systems that present reminders (or suggest materials to review) that apply to a population but not necessarily to the patient at hand. A result of this criterion is that the value a CDS system adds to a clinical process is directly related to the relevance of the advice it provides. Therefore, successful CDS is expected to deliver recommendations that apply to the current patient and the specific decision being made. This does not mean that a CDS has to make itself noticed every time a decision is made, but should provide advice only when it is relevant. In other words, it should interrupt the health provider decision process only when such interruption has a reasonable likelihood of improving patient care. How to estimate that probability, set the threshold, and provide advice in a least-disruptive manner are major challenges in CDS design.

A CDS system can be stand-alone or integrated within other systems, and passive (requiring user retrieval) or active (automatically triggered without requiring user retrieval). CDS can be delivered in a variety of formats, including reference materials, data reports, electronic alerts, order sets, documentation templates, and others. Whenever possible, a CDS system is integrated with other information systems such as an electronic health records (EHR) and computerized physician order entry (CPOE). Such an integration carries two significant benefits. First, it reduces the disruption of the clinical process, allowing the CDS to operate without user intervention. Autonomous CDS operation means that the system does not necessarily add new work to the health providers. Second, integration improves the quality of the input data, reducing the likelihood of producing incorrect recommendations. An important requirement of CDS system is that it provides clinically relevant information to the clinician at the right location in the workflow of clinicians. The CDS service must run efficiently to meet clinician expectations and not interrupt from the clinical workflow. Delays in presenting CDS information may lead to unsuccessful CDS adoption and clinician frustration.

A CDS system disseminates and implements guidelines and protocols that affect clinical processes directly. It operates just-in-time, i.e., at the time where clinical recommendations are most needed. Moreover, well-designed CDS systems implement evidence-based recommendations, which implies the formalization of unstructured narrative statements into computable decision support logic. This addresses a common criticism of clinical guideline documents as being ambiguous, unclear, and complex (Lee et al., 2015). By helping clinicians to adhere to best practice clinical guidelines, a CDS system can reduce unnecessary orders and discourage ineffective treatments (Sutton et al., 2020). It might also reduce provider workload by automating tasks involved in decision-making, such as searching the patient's EHR data for conditions and treatments that have a bearing on potential outcomes. Such a reduction in provider workload is predicated on a CDS design that maximizes the relevance of CDS-provided information and minimizes workflow disruption. The improvement in clinical decision-making achieved with CDS can result in several benefits for patients including a reduction in medical errors and adverse events (Helmons et al., 2015). Additionally, CDS can improve timeliness in treating diagnoses requiring urgent attention, reducing the likelihood of complications or death (Clinical Decision Support Decreases Sepsis Mortality in AL,