Electronic health record design and implementation for pharmacogenomics: a local perspective

Genetically guided precision medicine clinical decision support tools: a systematic review

OBJECTIVES

Patient care using genetics presents complex challenges. Clinical decision support (CDS) tools are a potential solution because they provide patient-specific risk assessments and/or recommendations at the point of care. This systematic review evaluated the literature on CDS systems which have been implemented to support genetically guided precision medicine (GPM).

MATERIALS AND METHODS

A comprehensive search was conducted in MEDLINE and Embase, encompassing January 1, 2011-March 14, 2023. The review included primary English peer-reviewed research articles studying humans, focused on the use of computers to guide clinical decision-making and delivering genetically guided, patient-specific assessments, and/or recommendations to healthcare providers and/or patients.

RESULTS

The search yielded 3832 unique articles. After screening, 41 articles were identified that met the inclusion criteria. Alerts and reminders were the most common form of CDS used. About 27 systems were integrated with the electronic health record; 2 of those used standards-based approaches for genomic data transfer. Three studies used a framework to analyze the implementation strategy.

DISCUSSION

Findings include limited use of standards-based approaches for genomic data transfer, system evaluations that do not employ formal frameworks, and inconsistencies in the methodologies used to assess genetic CDS systems and their impact on patient outcomes.

CONCLUSION

We recommend that future research on CDS system implementation for genetically GPM should focus on implementing more CDS systems, utilization of standards-based approaches, user-centered design, exploration of alternative forms of CDS interventions, and use of formal frameworks to systematically evaluate genetic CDS systems and their effects on patient care.

Identification of Drugs Acting as Perpetrators in Common Drug Interactions in a Cohort of Geriatric Patients from Southern Italy and Analysis of the Gene Polymorphisms That Affect Their Interacting Potential

BACKGROUND

Pharmacogenomic factors affect the susceptibility to drug-drug interactions (DDI). We identified drug interaction perpetrators among the drugs prescribed to a cohort of 290 older adults and analysed the prevalence of gene polymorphisms that can increase their interacting potential. We also pinpointed clinical decision support systems (CDSSs) that incorporate pharmacogenomic factors in DDI risk evaluation.

METHODS

Perpetrator drugs were identified using the Drug Interactions Flockhart Table, the DRUGBANK website, and the Mayo Clinic Pharmacogenomics Association Table. Allelic variants affecting their activity were identified with the PharmVar, PharmGKB, dbSNP, ensembl and 1000 genome databases.

RESULTS

Amiodarone, amlodipine, atorvastatin, digoxin, esomperazole, omeprazole, pantoprazole, simvastatin and rosuvastatin were perpetrator drugs prescribed to >5% of our patients. Few allelic variants affecting their perpetrator activity showed a prevalence >2% in the European population: CYP3A4/5*22, *1G, *3, CYP2C9*2 and *3, CYP2C19*17 and *2, CYP2D6*4, *41, *5, *10 and *9 and SLC1B1*15 and *5. Few commercial CDSS include pharmacogenomic factors in DDI-risk evaluation and none of them was designed for use in older adults.

CONCLUSIONS

We provided a list of the allelic variants influencing the activity of drug perpetrators in older adults which should be included in pharmacogenomics-oriented CDSSs to be used in geriatric medicine.

Approaches and hurdles of implementing pharmacogenetic testing in the psychiatric clinic

Pharmacogenetic (PGx) testing has emerged as a tool for predicting a person's ability to process and react to drugs. Despite the growing evidence-base, enthusiasm, and successful efforts to implement PGx testing in psychiatry, a consensus on how best to implement PGx testing into practice has not been established and numerous hurdles to widespread adoption remain to be overcome. In this article, we summarize the most used approaches and commonly encountered hurdles when implementing PGx testing into routine psychiatric care. We also highlight effective strategies that have been used to overcome hurdles. These strategies include the development of user-friendly clinical workflows for test ordering, use, and communication of results, establishment of test standardization and reimbursement policies, and development of tailored curriculums for educating health-care providers and the public. Although knowledge and awareness of these approaches and strategies to overcome hurdles alone may not be sufficient for successful implementation, they are necessary to ensure the effective spread, scale, and sustainability of PGx testing in psychiatry and other areas of medicine.

CYP2D6 phenotypes and opioid metabolism: the path to personalized analgesia

INTRODUCTION

Opioids play a fundamental role in chronic pain, especially considering when 1 of 5 Europeans adults, even more in older females, suffer from it. However, half of them do not reach an adequate pain relief. Could pharmacogenomics help to choose the most appropriate analgesic drug?

AREAS COVERED

The objective of the present narrative review was to assess the influence of cytochrome P450 2D6 (CYP2D6) phenotypes on pain relief, analgesic tolerability, and potential opioid misuse. Until December 2021, a literature search was conducted through the MEDLINE, PubMed database, including papers from the last 10 years. CYP2D6 plays a major role in metabolism that directly impacts on opioid (tramadol, codeine, or oxycodone) concentration with differences between sexes, with a female trend toward poorer pain control. In fact, CYP2D6 gene variants are the most actionable to be translated into clinical practice according to regulatory drug agencies and international guidelines.

EXPERT OPINION

CYP2D6 genotype can influence opioids' pharmacokinetics, effectiveness, side effects, and average opioid dose. This knowledge needs to be incorporated in pain management. Environmental factors, psychological together with genetic factors, under a sex perspective, must be considered when you are selecting the most personalized pain therapy for your patients.

Gene-drug pairings for antidepressants and antipsychotics: level of evidence and clinical application

Substantial inter-individual discrepancies exist in both therapeutic effectiveness and adverse effects of antidepressant and antipsychotic medications, which can, in part, be explained by genetic variation. Here, we searched the Pharmacogenomics Knowledge Base for gene-antidepressant and gene-antipsychotic pairs with the highest level of evidence. We then extracted and compared the associated prescribing recommendations for these pairs developed by the Clinical Pharmacogenomics Implementation Consortium, the Dutch Pharmacogenetics Working Group or approved product labels in the US, Canada, Europe, and Asia. Finally, we highlight key economical, educational, regulatory, and ethical issues that, if not appropriately considered, can hinder the implementation of these recommendations in clinical practice. Our review indicates that evidence-based guidelines are available to assist with the implementation of pharmacogenetic-guided antidepressant and antipsychotic prescribing, although the maximum impact of these guidelines on patient care will not be realized until key barriers are minimized or eliminated.

Building an information system to facilitate pharmacogenomics clinical translation with clinical decision support

Pharmacogenomics clinical decision support (PGx-CDS) is an important tool to incorporate PGx information into existing clinical workflows and facilitate PGx clinical translation. However, due to the lack of a computable formalization to represent the primary PGx knowledge, the complexity of genomics information and the lag of current commercial electronic health record (EHR) system for precision medicine, it is difficult to develop computerized PGx-CDS. Therefore, we explored a novel approach to build an information system, named the Pharmacogenomics Clinical Translation Platform (PCTP), for PGx clinical implementation. The PCTP can represent, store, and manage the primary PGx knowledge in a structured and computable format. Moreover, it has the potential to provide various PGx-CDS services and simplify the integration of PGx-CDS into EHRs.

PriME-PGx: La Princesa University Hospital Multidisciplinary Initiative for the Implementation of Pharmacogenetics

The implementation of clinical pharmacogenetics in daily practice is limited for various reasons. Today, however, it is a discipline in full expansion. Accordingly, in the recent times, several initiatives promoted its implementation, mainly in the United States but also in Europe. In this document, the genotyping results since the establishment of our Pharmacogenetics Unit in 2006 are described, as well as the historical implementation process that was carried out since then. Finally, this progress justified the constitution of La Princesa University Hospital Multidisciplinary Initiative for the Implementation of Pharmacogenetics (PriME-PGx), promoted by the Clinical Pharmacology Department of Hospital Universitario de La Princesa (Madrid, Spain). Here, we present the initiative along with the two first ongoing projects: the PROFILE project, which promotes modernization of pharmacogenetic reporting (i.e., from classic gene-drug pair reporting to complete pharmacogenetic reporting or the creation of pharmacogenetic profiles specific to the Hospital’s departments) and the GENOTRIAL project, which promotes the communication of relevant pharmacogenetic findings to any healthy volunteer participating in any bioequivalence clinical trial at the Clinical Trials Unit of Hospital Universitario de La Princesa (UECHUP).

Strategies and settings of clinical pharmacogenetic implementation: a scoping review of pharmacogenetics programs

Pharmacogenetic (PGx) literature has shown beneficial outcomes in safety, efficacy and cost when evidence-based gene-drug decision making is incorporated into clinical practice. PGx programs with successfully implemented clinical services have been published in a variety of settings including academic health centers and community practice. The primary objective was to systematically scope the literature to characterize the current trends, extent, range and nature of clinical PGx programs. Forty articles representing 19 clinical PGx programs were included in analysis. Most programs are in urban, academic institutions. Education, governance and workflow were commonly described while billing/reimbursement and consent were not. This review provides an overview of current PGx models that can be used as a reference for institutions beginning the implementation process.

Genetic Risk Assessment for Atherosclerotic Cardiovascular Disease: A Guide for the General Cardiologist

Genetic testing for cardiovascular (CV) disease has had a profound impact on the diagnosis and evaluation of monogenic causes of CV disease, such as hypertrophic and familial cardiomyopathies, long QT syndrome, and familial hypercholesterolemia (FH). The success in genetic testing for monogenic diseases has prompted special interest in utilizing genetic information in the risk assessment of more common diseases such as atherosclerotic cardiovascular disease (ASCVD). Polygenic risk scores (PRS) have been developed to assess the risk of coronary artery disease (CAD) that now include millions of single-nucleotide polymorphisms (SNPs) that have been identified through genome-wide association studies (GWAS). While these PRS have demonstrated a strong association with CAD in large cross-sectional population studies, there remains intense debate regarding the added value that PRS contribute to existing clinical risk prediction models such as the pooled cohort equations (PCEs). In this review, we provide a brief background of genetic testing for monogenic drivers of CV disease and then focus on the recent developments in genetic risk assessment of ASCVD, including the use of PRS. We outline the genetic testing that is currently available to all cardiologists in the clinic and discuss the evolving sphere of specialized cardiovascular genetics programs (CVGPs) that integrate the expertise of cardiologists, geneticists, and genetic counselors. Finally, we review the possible implications that PRS and pharmacogenomic data may soon have on clinical practice in the care for patients with or at risk of developing ASCVD.

Integrating pharmacogenomics into the electronic health record by implementing genomic indicators

Pharmacogenomics (PGx) clinical decision support integrated into the electronic health record (EHR) has the potential to provide relevant knowledge to clinicians to enable individualized care. However, past experience implementing PGx clinical decision support into multiple EHR platforms has identified important clinical, procedural, and technical challenges. Commercial EHRs have been widely criticized for the lack of readiness to implement precision medicine. Herein, we share our experiences and lessons learned implementing new EHR functionality charting PGx phenotypes in a unique repository, genomic indicators, instead of using the problem or allergy list. The Gen-Ind has additional features including a brief description of the clinical impact, a hyperlink to the original laboratory report, and links to additional educational resources. The automatic generation of genomic indicators from interfaced PGx test results facilitates implementation and long-term maintenance of PGx data in the EHR and can be used as criteria for synchronous and asynchronous CDS.

Pharmacogenomics Implementation and Hurdles to Overcome; In the Context of a Developing Country

Having multiple dimensions, uncertainties and several stakeholders, the costly pharmacogenomics (PGx) is associated with dynamic implementation complexities. Identification of these challenges is critical to harness its full potential, especially in developing countries with fragile healthcare systems and scarce resources. This is the first study aimed to identify most salient challenges related to PGx implementation, with respect to the experiences of early-adopters and local experts' prospects, in the context of a developing country in the Middle East. To perform a comprehensive reconnaissance on PGx adoption challenges a scoping literature review was conducted based on national drug policy components: efficacy/safety, access, affordability and rational use of medicine (RUM). Strategic option development and analysis workshop method with cognitive mapping as the technique was used to evaluate challenges in the context of Iran. The cognitive maps were face-validated and analyzed via Decision Explorer XML. The findings indicated a complex network of issues relative to PGx adoption, categorized in national drug policy indicators. In the rational use of medicine category, ethics, education, bench -to- bedside strategies, guidelines, compliance, and health system issues were found. Clinical trial issues, test's utility, and biomarker validation were identified in the efficacy group. Affordability included pricing, reimbursement, and value assessment issues. Finally, access category included regulation, availability, and stakeholder management challenges. The current study identified the most significant challenges ahead of clinical implementation of PGx in a developing country. This could be the basis of a policy-note development in future work, which may consolidate vital communication among stakeholders and accelerate the efficient implementation in developing new-comer countries.

A Tutorial for Pharmacogenomics Implementation Through End-to-End Clinical Decision Support Based on Ten Years of Experience from PREDICT

Vanderbilt University Medical Center implemented pharmacogenomics (PGx) testing with the Pharmacogenomic Resource for Enhanced Decisions in Care and Treatment (PREDICT) initiative in 2010. This tutorial reviews the laboratory considerations, technical infrastructure, and programmatic support required to deliver panel-based PGx testing across a large health system with examples and experiences from the first decade of the PREDICT initiative. From the time of inception, automated clinical decision support (CDS) has been a critical capability for delivering PGx results to the point-of-care. Key features of the CDS include human-readable interpretations and clinical guidance that is anticipatory, actionable, and adaptable to changes in the scientific literature. Implementing CDS requires that structured results from the laboratory be encoded in standards-based messages that are securely ingested by electronic health records. Translating results to guidance also requires an informatics infrastructure with multiple components: (1) to manage the interpretation of raw genomic data to "star allele" results to expected phenotype, (2) to define the rules that associate a phenotype with recommended changes to clinical care, and (3) to manage and update the knowledge base. Knowledge base management is key to processing new results with the latest guidelines, and to ensure that historical genomic results can be reinterpreted with revised CDS. We recommend that these components be deployed with institutional authorization, programmatic support, and clinician education to govern the CDS content and policies around delivery.

The inclusion of health data standards in the implementation of pharmacogenomics systems: a scoping review

Background: Despite potential benefits, the practice of incorporating pharmacogenomics (PGx) results in clinical decisions has yet to diffuse widely. In this study, we conducted a review of recent discussions on data standards and interoperability with a focus on sharing PGx test results among health systems. Materials & methods: We conducted a literature search for PGx clinical decision support systems between 1 January 2012 and 31 January 2020. Thirty-two out of 727 articles were included for the final review. Results: Nine of the 32 articles mentioned data standards and only four of the 32 articles provided solutions for the lack of interoperability. Discussions: Although PGx interoperability is essential for widespread implementation, a lack of focus on standardized data creates a formidable challenge for health information exchange. Conclusion: Standardization of PGx data is essential to improve health information exchange and the sharing of PGx results between disparate systems. However, PGx data standards and interoperability are often not addressed in the system-level implementation.

Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records

Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.

The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities

Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.

The Implementation Chasm Hindering Genome-informed Health Care

The promises of precision medicine are often heralded in the medical and lay literature, but routine integration of genomics in clinical practice is still limited. While the "last mile' infrastructure to bring genomics to the bedside has been demonstrated in some healthcare settings, a number of challenges remain - both in the receptivity of today's health system and in its technical and educational readiness to respond to this evolution in care. To improve the impact of genomics on health and disease management, we will need to integrate both new knowledge and new care processes into existing workflows. This change will be onerous and time-consuming, but hopefully valuable to the provision of high quality, economically feasible care worldwide.

Patients' willingness to reconsider cancer genetic testing after initially declining: Mention it again

Patients at risk for hereditary cancer syndromes sometimes decline clinically appropriate genetic testing. The purpose of the current study was to understand what preferences, concerns, and desires informed their refusal as well as their current level of interest in being tested. We interviewed patients who had been seen in a hereditary cancer clinic at Vanderbilt University Medical Center and had declined genetic testing. In all, 21 in-depth, semi-structured qualitative interviews were conducted. Although patients provided many reasons for declining testing, they most often cited their psychosocial state at the time of the initial invitation to participate in genetic testing as their reason for refusal. The majority (67%) said that they either would or had changed their mind about testing if/when their clinicians 'mentioned it again'. Patients at risk for hereditary cancer who refuse testing at the time of genetic counseling may later change their mind. In particular, if a patient declines testing around the time of a major medical diagnosis or intervention, clinicians who are providing ongoing care may want to raise the topic afresh after the patient has had time to recover from initial distress related to diagnosis or treatment. Strategies to prompt clinicians to have these conversations are suggested.

Current practices in the delivery of pharmacogenomics: Impact of the recommendations of the Pharmacy Practice Model Summit

PURPOSE

This report examines and evaluates pharmacogenomics as an emerging science as it relates to the Practice Advancement Initiative and its predecessor the Pharmacy Practice Model Initiative's consensus statements for optimal pharmacy practice models.

SUMMARY

Pharmacogenomics is one of many emerging sciences to impact medication management and delivery of patient care. Increasingly, biomarkers are included in drug labeling and can assist pharmacists with personalizing medicine to optimize patient therapies and avoid adverse effects. The 2011 ASHP Pharmacy Practice Model Summit generated a list of 147 consensus statements for optimal pharmacy practice. Of these, 1 statement explicitly describes adjustment of drug regimens based on genetic factors as an essential activity of pharmacist-provided drug regimens, and 9 other statements provide additional support for incorporation of this emerging science into all aspects of patient care provided by pharmacists. We describe 4 institutions that have made significant inroads to implementing pharmacogenomics, to provide a framework and serve as resources for other institutions initiating their own pharmacogenomics implementation journeys.

CONCLUSION

Through prioritized efforts of the pharmacy profession and health care institutions, pharmacogenomics will be disseminated and implemented, and the goal of the Pharmacy Practice Model Initiative's consensus statements of improving health care using patients' genetic characteristics will be realized.

Pharmacogenomic clinical decision support design and multi-site process outcomes analysis in the eMERGE Network

To better understand the real-world effects of pharmacogenomic (PGx) alerts, this study aimed to characterize alert design within the eMERGE Network, and to establish a method for sharing PGx alert response data for aggregate analysis. Seven eMERGE sites submitted design details and established an alert logging data dictionary. Six sites participated in a pilot study, sharing alert response data from their electronic health record systems. PGx alert design varied, with some consensus around the use of active, post-test alerts to convey Clinical Pharmacogenetics Implementation Consortium recommendations. Sites successfully shared response data, with wide variation in acceptance and follow rates. Results reflect the lack of standardization in PGx alert design. Standards and/or larger studies will be necessary to fully understand PGx impact. This study demonstrated a method for sharing PGx alert response data and established that variation in system design is a significant barrier for multi-site analyses.

[Pharmacogenetics for patient care in France: A discipline that evolves!]

Pharmacogenetics, which concepts are known for a long time, is entering a new period at least as far as its practical applications for patients are concerned. In recent years there have been more and more initiatives to promote widespread dissemination, and health authorities are increasingly incorporating these concepts into drug labels. In France, the national network of pharmacogenetics (RNPGx) works to promote these activities, both with health actors (biologists, clinicians) and health authorities. This article reviews the current situation in France and the milestones of the year 2018. It highlights recent advances in this field, in terms of currently recommended analyses, sharing of information or technological developments, and the prospects for future developments in the near future from targeted pharmacogenetics to eventually preemptive approaches.

Precision Pharmacotherapy: Integrating Pharmacogenomics into Clinical Pharmacy Practice

Precision pharmacotherapy encompasses the use of therapeutic drug monitoring; evaluation of liver and renal function, genomics, and environmental and lifestyle exposures; and analysis of other unique patient or disease characteristics to guide drug selection and dosing. This paper articulates real-world clinical applications of precision pharmacotherapy, focusing exclusively on the emerging field of clinical pharmacogenomics. This field is evolving rapidly, and clinical pharmacists now play an invaluable role in the clinical implementation, education, and research applications of pharmacogenomics. This paper provides an overview of the evolution of pharmacogenomics in clinical pharmacy practice, together with recommendations on how the American College of Clinical Pharmacy (ACCP) can support the advancement of clinical pharmacogenomics implementation, education, and research. Commonalities among successful clinical pharmacogenomics implementation and education programs are identified, with recommendations for how ACCP can leverage and advance these common themes. Opportunities are also provided to support the research needed to move the practice and application of pharmacogenomics forward.

The Genomic Medicine Integrative Research Framework: A Conceptual Framework for Conducting Genomic Medicine Research

Conceptual frameworks are useful in research because they can highlight priority research domains, inform decisions about interventions, identify outcomes and factors to measure, and display how factors might relate to each other to generate and test hypotheses. Discovery, translational, and implementation research are all critical to the overall mission of genomic medicine and prevention, but they have yet to be organized into a unified conceptual framework. To fill this gap, our diverse team collaborated to develop the Genomic Medicine Integrative Research (GMIR) Framework, a simple but comprehensive tool to aid the genomics community in developing research questions, strategies, and measures and in integrating genomic medicine and prevention into clinical practice. Here we present the GMIR Framework and its development, along with examples of its use for research development, demonstrating how we applied it to select and harmonize measures for use across diverse genomic medicine implementation projects. Researchers can utilize the GMIR Framework for their own research, collaborative investigations, and clinical implementation efforts; clinicians can use it to establish and evaluate programs; and all stakeholders can use it to help allocate resources and make sure that the full complexity of etiology is included in research and program design, development, and evaluation.

Genomics and electronic health record systems

Several reviews and case reports have described how information derived from the analysis of genomes are currently included in electronic health records (EHRs) for the purposes of supporting clinical decisions. Since the introduction of this new type of information in EHRs is relatively new (for instance, the widespread adoption of EHRs in the United States is just about a decade old), it is not surprising that a myriad of approaches has been attempted, with various degrees of success. EHR systems undergo much customization to fit the needs of health systems; these approaches have been varied and not always generalizable. The intent of this article is to present a high-level view of these approaches, emphasizing the functionality that they are trying to achieve, and not to advocate for specific solutions, which may become obsolete soon after this review is published. We start by broadly defining the end goal of including genomics in EHRs for healthcare and then explaining the various sources of information that need to be linked to arrive at a clinically actionable genomics analysis using a pharmacogenomics example. In addition, we include discussions on open issues and a vision for the next generation systems that integrate whole genome sequencing and EHRs in a seamless fashion.

Challenges of Identifying Clinically Actionable Genetic Variants for Precision Medicine

Advances in genomic medicine have the potential to change the way we treat human disease, but translating these advances into reality for improving healthcare outcomes depends essentially on our ability to discover disease- and/or drug-associated clinically actionable genetic mutations. Integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a big data infrastructure can provide an efficient and effective way to identify clinically actionable genetic variants for personalized treatments and reduce healthcare costs. We review bioinformatics processing of next-generation sequencing (NGS) data, bioinformatics infrastructures for implementing precision medicine, and bioinformatics approaches for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Qualitative user evaluation of a revised pharmacogenetic educational toolkit

Introduction

Pharmacogenetic (PGx) testing is a leading application for personalized and precision medicine; however, there are barriers, including limited provider and patient understanding, which affect its uptake. There is a need for tools that can enhance the patient and provider experience with testing and promoting the shared and informed decision-making.

Materials and methods

In this study, we sought to gather additional feedback on a PGx toolkit comprised of four educational tools that had been previously evaluated through an online survey by pharmacists. Specifically, we conducted semi-structured interviews with pharmacists and members of the public regarding their understanding and utility of the toolkit and its individual components.

Results

Participants found three of the four toolkit components, a test information sheet, flipbook, and results sheet, to be useful and important. The fourth component, results card, was viewed less favorably. Participants differed in their preference for medical jargon and detailed results nomenclature (namely star * alleles).

Conclusion

User input during the development of educational materials is essential for optimizing utilization, effectiveness, and comprehension.

Implementing pharmacogenomics decision support across seven European countries: The Ubiquitous Pharmacogenomics (U-PGx) project

Clinical pharmacogenomics (PGx) has the potential to make pharmacotherapy safer and more effective by utilizing genetic patient data for drug dosing and selection. However, widespread adoption of PGx depends on its successful integration into routine clinical care through clinical decision support tools, which is often hampered by insufficient or fragmented infrastructures. This paper describes the setup and implementation of a unique multimodal, multilingual clinical decision support intervention consisting of digital, paper-, and mobile-based tools that are deployed across implementation sites in seven European countries participating in the Ubiquitous PGx (U-PGx) project.

Benefit of Preemptive Pharmacogenetic Information on Clinical Outcome

The development of new knowledge around the genetic determinants of variable drug action has naturally raised the question of how this new knowledge can be used to improve the outcome of drug therapy. Two broad approaches have been taken: a point-of-care approach in which genotyping for specific variant(s) is undertaken at the time of drug prescription, and a preemptive approach in which multiple genetic variants are typed in an individual patient and the information archived for later use when a drug with a "pharmacogenetic story" is prescribed. This review addresses the current state of implementation, the rationale for these approaches, and barriers that must be overcome. Benefits to pharmacogenetic testing are only now being defined and will be discussed.

Integrating pharmacogenomics into electronic health records with clinical decision support

PURPOSE

Existing pharmacogenomic informatics models, key implementation steps, and emerging resources to facilitate the development of pharmacogenomic clinical decision support (CDS) are described.

SUMMARY

Pharmacogenomics is an important component of precision medicine. Informatics, especially CDS in the electronic health record (EHR), is a critical tool for the integration of pharmacogenomics into routine patient care. Effective integration of pharmacogenomic CDS into the EHR can address implementation challenges, including the increasing volume of pharmacogenomic clinical knowledge, the enduring nature of pharmacogenomic test results, and the complexity of interpreting results. Both passive and active CDS provide point-of-care information to clinicians that can guide the systematic use of pharmacogenomics to proactively optimize pharmacotherapy. Key considerations for a successful implementation have been identified; these include clinical workflows, identification of alert triggers, and tools to guide interpretation of results. These considerations, along with emerging resources from the Clinical Pharmacogenetics Implementation Consortium and the National Academy of Medicine, are described.

CONCLUSION

The EHR with CDS is essential to curate pharmacogenomic data and disseminate patient-specific information at the point of care. As part of the successful implementation of pharmacogenomics into clinical settings, all relevant clinical recommendations pertaining to gene-drug pairs must be summarized and presented to clinicians in a manner that is seamlessly integrated into the clinical workflow of the EHR. In some situations, ancillary systems and applications outside the EHR may be integrated to augment the capabilities of the EHR.

Simplifying the use of pharmacogenomics in clinical practice: Building the genomic prescribing system

BACKGROUND

A barrier to the use of genomic information during prescribing is the limited number of software solutions that combine a user-friendly interface with complex medical data. We built and designed an online, secure, electronic custom interface termed the Genomic Prescribing System (GPS).

METHODS

Actionable pharmacogenomic (PGx) information was reviewed, collected, and stored in the back-end of GPS to enable creation of customized drug- and variant-specific clinical decision support (CDS) summaries. The database architecture utilized the star schema to store information. Patient raw genomic data underwent transformation via custom-designed algorithms to enable gene and phenotype-level associations. Multiple external data sets (PubMed, The Systematized Nomenclature of Medicine (SNOMED), National Drug File - Reference Terminology (ND-FRT), and a publically-available PGx knowledgebase) were integrated to facilitate the delivery of patient, drug, disease, and genomic information. Institutional security infrastructure was leveraged to securely store patient genomic and clinical data on a HIPAA-compliant server farm.

RESULTS

As of May 17, 2016, the GPS back-end housed 257 CDS encompassing 112 genetic variants, 42 genes, and 46 PGx-actionable drugs. The GPS user interface presented patient-specific CDS alongside a recognizable traffic light symbol (green/yellow/red), denoting PGx risk for each genomic result. The number of traffic lights per visit increased with the corresponding increase in the number of available PGx-annotated drugs over time. An integrated drug and disease search functionality, links to primary literature sources, and potential alternative PGx drugs were indicated. The system, which was initially used as stand-alone CDS software within our clinical environment, was then integrated with the institutional electronic medical record for enhanced usability. There have been nearly 2000 logins in 43months since inception, with usage exceeding 56 logins per month and system up-times of 99.99%. For all patient-provider visits encompassing >3years of implementation, unique alert click-through rates corresponded to genomic risk: red lights clicked 100%, yellow lights 79%, green lights 43%.

CONCLUSIONS

Successful deployment of GPS by combining complex data and recognizable iconography led to a tool that enabled point-of-care genomic delivery with high usability. Continued scalability and incorporation of additional clinical elements to be considered alongside PGx information could expand future impact.

The Pharmacogenomics Research Network Translational Pharmacogenetics Program: Outcomes and Metrics of Pharmacogenetic Implementations Across Diverse Healthcare Systems

Numerous pharmacogenetic clinical guidelines and recommendations have been published, but barriers have hindered the clinical implementation of pharmacogenetics. The Translational Pharmacogenetics Program (TPP) of the National Institutes of Health (NIH) Pharmacogenomics Research Network was established in 2011 to catalog and contribute to the development of pharmacogenetic implementations at eight US healthcare systems, with the goal to disseminate real-world solutions for the barriers to clinical pharmacogenetic implementation. The TPP collected and normalized pharmacogenetic implementation metrics through June 2015, including gene-drug pairs implemented, interpretations of alleles and diplotypes, numbers of tests performed and actionable results, and workflow diagrams. TPP participant institutions developed diverse solutions to overcome many barriers, but the use of Clinical Pharmacogenetics Implementation Consortium (CPIC) guidelines provided some consistency among the institutions. The TPP also collected some pharmacogenetic implementation outcomes (scientific, educational, financial, and informatics), which may inform healthcare systems seeking to implement their own pharmacogenetic testing programs.

The Relationship of Genetics, Nursing Practice, and Informatics Tools in 6-Mercaptopurine Dosing in Pediatric Oncology [Formula: see text]

An antileukemic agent prescribed for pediatric oncology patients during the maintenance phase of therapy for acute lymphoblastic leukemia, 6-mercaptopurine (6-MP), is highly influenced by genetic variations in the thiopurine S-methyltransferase enzyme. As such, 6-MP must be dosed so that patients with 1 or 2 inactive thiopurine S-methyltransferase alleles will not incur an increased risk for myelosuppression or other toxicities. Informatics tools such as clinical decision support systems are useful for the application of this and similar pharmacogenetics information to the realm of nursing and clinical practice for safe and effective patient care. This article will discuss pharmacogenetics and the associated use of 6-MP; present implications for nursing practice; identify informatics tools such as clinical decision support systems, which can greatly enhance the care of patients whose treatment is based on critical genetic information; and examine the relationship of genetics, nursing practice, and informatics for 6-MP dosing in pediatric oncology.

Healthcare provider education to support integration of pharmacogenomics in practice: the eMERGE Network experience

Ten organizations within the Electronic Medical Records and Genomics Network developed programs to implement pharmacogenomic sequencing and clinical decision support into clinical settings. Recognizing the importance of informed prescribers, a variety of strategies were used to incorporate provider education to support implementation. Education experiences with pharmacogenomics are described within the context of each organization's prior involvement, including the scope and scale of implementation specific to their Electronic Medical Records and Genomics projects. We describe common and distinct education strategies, provide exemplars and share challenges. Lessons learned inform future perspectives. Future pharmacogenomics clinical implementation initiatives need to include funding toward implementing provider education and evaluating outcomes.

Methods to analyze big data in pharmacogenomics research

The scale and scope of pharmacogenomics research continues to expand as the cost and efficiency of molecular data generation techniques advance. These new technologies give rise to enormous opportunity for the identification of important genetic and genomic factors important for drug treatment response. With this opportunity come significant challenges. Most of these can be categorized as 'big data' issues, facing not only pharmacogenomics, but other fields in the life sciences as well. In this review, we describe some of the analysis techniques and tools being implemented for genetic/genomic discovery in pharmacogenomics.

Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development

Background

Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS.

Methods

We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified.

Results

Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype.

Conclusions

Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping.

Electronic supplementary material

The online version of this article (doi:10.1186/s12911-017-0480-y) contains supplementary material, which is available to authorized users.

Precision Medicine at the University of Alabama at Birmingham: Laying the Foundational Processes Through Implementation of Genotype-Guided Antiplatelet Therapy

Precision medicine entails tailoring treatment based on patients' unique characteristics. As drug therapy constitutes the cornerstone of treatment for most chronic diseases, pharmacogenomics (PGx), the study of genetic variation influencing individual response to drugs, is an important component of precision medicine. Over the past decade investigations have identified genes and single-nucleotide polymorphisms (SNPs) and quantified their effect on drug response. Parallel development of point-of-care (POC) genotyping platforms has enabled the interrogation of the genes/SNPs within a timeline conducive to the provision of care. Despite these advances, the pace of integration of genotype-guided drug therapy (GGTx) into practice has faced significant challenges. These include difficulty in identifying SNPs with sufficiently robust evidence to guide clinical decision making, lack of clinician training on how to order and use genotype data, lack of clinical decision support (CDS) to guide treatment, and limited reimbursement. The University of Alabama at Birmingham's (UAB) efforts in precision medicine were initiated to address these challenges and improve the health of the racially diverse patients we treat.

The experience of physicians in pharmacogenomic clinical decision support within eight German university hospitals

Aim: The aim of this study was to assess the physicians’ attitude, their knowledge and their experience in pharmacogenomic clinical decision support in German hospitals. Materials & methods: We conducted an online survey to address physicians of 13 different medical specialties across eight German university hospitals. In total, 564 returned questionnaires were analyzed. Results: The remaining knowledge gap, the uncertainty of test reimbursement and the physicians’ lack of awareness of existing pharmacogenomic clinical decision support systems (CDSS) are the major barriers for implementing pharmacogenomic CDSS into German hospitals. Furthermore, pharmacogenomic CDSS are most effective in the form of real-time decision support for internists. Conclusion: Physicians in German hospitals require additional education of both genetics and pharmacogenomics. They need to be provided with access to relevant pharmacogenomic CDSS.

Portable devices and mobile instruments for infectious diseases point-of-care testing

INTRODUCTION

Rapidity, simplicity, and portability are highly desirable characteristics of tests and devices designed for performing diagnostics at the point of care (POC), either near patients managed in healthcare facilities or to offer bioanalytical alternatives in external settings. By reducing the turnaround time of the diagnostic cycle, POC diagnostics can reduce the dissemination, morbidity, and mortality of infectious diseases and provide tools to control the global threat of antimicrobial resistance. Areas covered: A literature search of PubMed and Google Scholar, and extensive mining of specialized publications, Internet resources, and manufacturers' websites have been used to organize and write this overview of the challenges and requirements associated with the development of portable sample-to-answer diagnostics, and showcase relevant examples of handheld devices, portable instruments, and less mobile systems which may or could be operated at POC. Expert commentary: Rapid (<1 h) diagnostics can contribute to control infectious diseases and antimicrobial resistant pathogens. Portable devices or instruments enabling sample-to-answer bioanalysis can provide rapid, robust, and reproducible testing at the POC or close from it. Beyond testing, to realize some promises of personalized/precision medicine, it will be critical to connect instruments to healthcare data management systems, to efficiently link decentralized testing results to the electronic medical record of patients.

Primary care providers' use of pharmacist support for delivery of pharmacogenetic testing

AIM

To investigate provider utilization of pharmacist support in the delivery of pharmacogenetic testing in a primary care setting.

METHODS

Two primary care clinics within Duke University Health System participated in the study between December 2012 and July 2013. One clinic was provided with an in-house pharmacist and the second clinic had an on-call pharmacist.

RESULTS

Providers in the in-house pharmacist arm consulted with the pharmacist for 13 of 15 cases, or about one of every four patients tested compared with one of every 7.5 patients in the on-call pharmacist arm. A total of 63 tests were ordered, 48 by providers in the pharmacist-in-house arm.

CONCLUSION

These findings suggest that the availability of an in-house pharmacist increases the likelihood of pharmacogenetic test utilization.

Big Data Analytics for Genomic Medicine

Genomic medicine attempts to build individualized strategies for diagnostic or therapeutic decision-making by utilizing patients’ genomic information. Big Data analytics uncovers hidden patterns, unknown correlations, and other insights through examining large-scale various data sets. While integration and manipulation of diverse genomic data and comprehensive electronic health records (EHRs) on a Big Data infrastructure exhibit challenges, they also provide a feasible opportunity to develop an efficient and effective approach to identify clinically actionable genetic variants for individualized diagnosis and therapy. In this paper, we review the challenges of manipulating large-scale next-generation sequencing (NGS) data and diverse clinical data derived from the EHRs for genomic medicine. We introduce possible solutions for different challenges in manipulating, managing, and analyzing genomic and clinical data to implement genomic medicine. Additionally, we also present a practical Big Data toolset for identifying clinically actionable genetic variants using high-throughput NGS data and EHRs.

Standardizing terms for clinical pharmacogenetic test results: consensus terms from the Clinical Pharmacogenetics Implementation Consortium (CPIC)

INTRODUCTION

Reporting and sharing pharmacogenetic test results across clinical laboratories and electronic health records is a crucial step toward the implementation of clinical pharmacogenetics, but allele function and phenotype terms are not standardized. Our goal was to develop terms that can be broadly applied to characterize pharmacogenetic allele function and inferred phenotypes.

MATERIALS AND METHODS

Terms currently used by genetic testing laboratories and in the literature were identified. The Clinical Pharmacogenetics Implementation Consortium (CPIC) used the Delphi method to obtain a consensus and agree on uniform terms among pharmacogenetic experts.

RESULTS

Experts with diverse involvement in at least one area of pharmacogenetics (clinicians, researchers, genetic testing laboratorians, pharmacogenetics implementers, and clinical informaticians; n = 58) participated. After completion of five surveys, a consensus (>70%) was reached with 90% of experts agreeing to the final sets of pharmacogenetic terms.

DISCUSSION

The proposed standardized pharmacogenetic terms will improve the understanding and interpretation of pharmacogenetic tests and reduce confusion by maintaining consistent nomenclature. These standard terms can also facilitate pharmacogenetic data sharing across diverse electronic health care record systems with clinical decision support.Genet Med 19 2, 215-223.

Evaluation of a Prediction Model for the Development of Atrial Fibrillation in a Repository of Electronic Medical Records

Importance

Atrial fibrillation (AF) contributes to substantial morbidity, mortality, and health care expenditures. Accurate prediction of incident AF would enhance AF management and potentially improve patient outcomes.

Objective

To validate the AF risk prediction model originally developed by the Cohorts for Heart and Aging Research in Genomic Epidemiology-Atrial Fibrillation (CHARGE-AF) investigators using a large repository of electronic medical records (EMRs).

Design, Setting, and Participants

In this prediction model study, deidentified EMRs of 33 494 individuals 40 years or older who were white or African American and had no history of AF were reviewed and analyzed. The participants were followed up in the internal medicine outpatient clinics at Vanderbilt University Medical Center for incident AF from December 31, 2005, until December 31, 2010. Adjusting for differences in baseline hazard, the CHARGE-AF Cox proportional hazards model regression coefficients were applied to the EMR cohort. A simple version of the model with no echocardiographic variables was also evaluated. Data were analyzed from October 31, 2013, to January 31, 2014.

Main Outcomes and Measures

Incident AF. Predictors in the model included age, race, height, weight, systolic and diastolic blood pressure, treatment for hypertension, smoking status, type 2 diabetes, heart failure, history of myocardial infarction, left ventricular hypertrophy, and PR interval.

Results

Among the 33 494 participants, the median age was 57 (interquartile range, 49-67) years; 57% of patients were women, 43% were men, 85.7% were white, and 14.3% were African American. During the mean (SD) follow-up of 4.8 (0.9) years, 2455 individuals (7.3%) developed AF. Both models had poor calibration in the EMR cohort, with underprediction of AF among low-risk individuals and overprediction of AF among high-risk individuals (10th and 90th percentiles for predicted probability of incident AF, 0.005 and 0.179, respectively). The full CHARGE-AF model had a C index of 0.708 (95% CI, 0.699-0.718) in our cohort. The simple model had similar discrimination (C index, 0.709; 95% CI, 0.699-0.718; P = .70 for difference between models).

Conclusions and Relevance

Despite reasonable discrimination, the CHARGE-AF models showed poor calibration in this EMR cohort. This study highlights the difficulties of applying a risk model derived from prospective cohort studies to an EMR cohort and suggests that these AF risk prediction models be used with caution in the EMR setting. Future risk models may need to be developed and validated within EMR cohorts.

Systematic Molecular Phenotyping: A Path Toward Precision Emergency Medicine?

Precision medicine is an emerging approach to disease treatment and prevention that considers variability in patient genes, environment, and lifestyle. However, little has been written about how such research impacts emergency care. Recent advances in analytical techniques have made it possible to characterize patients in a more comprehensive and sophisticated fashion at the molecular level, promising highly individualized diagnosis and treatment. Among these techniques are various systematic molecular phenotyping analyses (e.g., genomics, transcriptomics, proteomics, and metabolomics). Although a number of emergency physicians use such techniques in their research, widespread discussion of these approaches has been lacking in the emergency care literature and many emergency physicians may be unfamiliar with them. In this article, we briefly review the underpinnings of such studies, note how they already impact acute care, discuss areas in which they might soon be applied, and identify challenges in translation to the emergency department (ED). While such techniques hold much promise, it is unclear whether the obstacles to translating their findings to the ED will be overcome in the near future. Such obstacles include validation, cost, turnaround time, user interface, decision support, standardization, and adoption by end-users.

Multidisciplinary model to implement pharmacogenomics at the point of care

Purpose

Despite potential clinical benefits, implementation of pharmacogenomics (PGx) faces many technical and clinical challenges. These challenges can be overcome by a comprehensive and systematic implementation model.

Methods

The development and implementation of PGx was organized into eight interdependent components addressing resources, governance, clinical practice, education, testing, knowledge translation, clinical decision support (CDS) and maintenance. Several aspects of the implementation were assessed including adherence to the model, production of PGx-CDS interventions and access to educational resources.

Results

Between 8/2012 and 6/2015, 21 specific drug-gene interactions were reviewed and 18 of them were implemented in the electronic medical record as PGx-CDS interventions. There was complete adherence to the model with variable production time (98 to 392 days) and delay time (0 to 148 days). The implementation impacted approximately 1247 unique providers and 3788 unique patients. A total of 11 educational resources complementary to the drug-gene interactions and 5 modules specific for pharmacists were developed and implemented.

Conclusion

A comprehensive operational model can support PGx implementation into routine prescribing. Institutions can use this model as a roadmap to support similar efforts. However, we also identified challenges that will require major multidisciplinary and multi-institutional efforts to make PGx a universal reality.

Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision

OBJECTIVE

The objective of this review is to summarize the state of the art of clinical decision support (CDS) circa 1990, review progress in the 25 year interval from that time, and provide a vision of what CDS might look like 25 years hence, or circa 2040.

METHOD

Informal review of the medical literature with iterative review and discussion among the authors to arrive at six axes (data, knowledge, inference, architecture and technology, implementation and integration, and users) to frame the review and discussion of selected barriers and facilitators to the effective use of CDS.

RESULT

In each of the six axes, significant progress has been made. Key advances in structuring and encoding standardized data with an increased availability of data, development of knowledge bases for CDS, and improvement of capabilities to share knowledge artifacts, explosion of methods analyzing and inferring from clinical data, evolution of information technologies and architectures to facilitate the broad application of CDS, improvement of methods to implement CDS and integrate CDS into the clinical workflow, and increasing sophistication of the end-user, all have played a role in improving the effective use of CDS in healthcare delivery.

CONCLUSION

CDS has evolved dramatically over the past 25 years and will likely evolve just as dramatically or more so over the next 25 years. Increasingly, the clinical encounter between a clinician and a patient will be supported by a wide variety of cognitive aides to support diagnosis, treatment, care-coordination, surveillance and prevention, and health maintenance or wellness.