American Journal of Medical Genetics Part C (Seminars in Medical Genetics) 166C:8–14 (2014)
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Genomic Medicine Implementation: Learning by Example MARC S. WILLIAMS*
Genomic Medicine is beginning to emerge into clinical practice. The National Human Genome Research Institute's Genomic Medicine Working Group consists of organizations that have begun to implement some aspect of genomic medicine (e.g., family history, systematic implementation of Mendelian disease program, pharmacogenomics, whole exome/genome sequencing). This article concisely reviews the working group and provides a broader context for the articles in the special issue including an assessment of anticipated provider needs and ethical, legal, and social issues relevant to the implementation of genomic medicine. The challenges of implementation of innovation in clinical practice and the potential value of genomic medicine are discussed. © 2014 Wiley Periodicals, Inc. KEY WORDS: genomic medicine; personalized medicine; family history; pharmacogenetics; implementation; ethical legal and social issues
How to cite this article: Williams MS. 2014. Genomic medicine implementation: Learning by example. Am J Med Genet Part C Semin Med Genet 166C:8–14.
INTRODUCTION In August of 2012, the National Human Genome Research Institute (NHGRI) published a definition of genomic medicine which is defined as, “An emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g., for diagnostic or therapeutic decision‐making) and the other implications of that clinical use.” [NHGRI, 2012] This definition is intentionally narrow and defines genomic as direct information about DNA or RNA, putting the study of more downstream products derived from the genome (i.e., proteomics, glycomics, metabolomics, etc.) as outside the definitional scope of genomic medicine for the activities of the NHGRI. One exception to this narrow definition is the inclusion of family history as being within the purview of genomic medicine implementation.
As noted in the article by Manolio and Green [2014] that opens this special issue of the Seminars, implementation of genomic medicine is represented on the NHGRI’s most recent strategic plan. There are a many issues involved in the use of genomic medicine in clinical care including but not limited to the evidence to support the role of genomic medicine interventions to improve outcomes for patients, patient, provider and health system readiness and education, ethical, legal, and social issues (ELSI) as well as considerations related to cost and reimbursement. Despite these issues some groups are deliberately moving forward with implementation of certain genomic medicine projects. In June of 2011, NHGRI convened the first of a series of genomic medicine meetings focused on implementation of genomic medicine. This meeting brought together 20 centers that were implementing some aspect of genomic
medicine (i.e., family history, genetics, and genomics). A series of meetings followed the topics of which can be found on the NHGRI website. [NHGRI, 2014a] At the Genomic Medicine IV meeting it was decided that publication of genomic medicine implementation projects was a desirable goal and the author/editor was charged to facilitate this activity. This has resulted in this special issue of Seminars in Medical Genetics devoted to the Implementation of Genomic Medicine.
THE SCIENCE OF IMPLEMENTATION Implementation of change in medical practice is challenging. The primary issues do not involve lack of evidence or recalcitrant providers although these can contribute. Rather the issue is introducing change into a large and complex system that due to sheer size, complexity
Conflict of interest: none. Dr. Williams has been director of Geisinger Health System's Genomic Medicine Institute, since 2012. He has led genomic medicine implementation projects at Geisinger and at his previous institution Intermountain Healthcare. He is a member of the National Human Genome Research Institute's Genomic Medicine Working Group steering committee. *Correspondence to: Marc S. Williams, M.D., Genomic Medicine Institute, Geisinger Health System, 100N Academy Drive, Mail Stop 26‐20, Danville, PA 17822‐2026. E‐mail: [email protected] DOI 10.1002/ajmg.c.31394 Article first published online in Wiley Online Library (wileyonlinelibrary.com): 10 March 2014
ß 2014 Wiley Periodicals, Inc.
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and momentum resists change even with a high likelihood of improved care. To try and understand the challenges, a new field of study, implementation science, has developed. Eccles and Mittman [2006] define implementation science as, ”… the scientific study of methods to promote the systematic uptake of research findings and other evidence‐ based practices into routine practice, and, hence, to improve the quality and effectiveness of health services and care. This field includes the study of influences on healthcare professional and organizational behavior.” Successful implementation requires attention to factors in three domains: predisposing factors (e.g., knowledge and attitudes in the target group), enabling factors (e.g., capacity, resources, access) and reinforcing factors (opinions and behaviors of others) [Grol and Wensing, 2004]. Key elements for successful implementation are outlined in Table I. An in depth discussion of the implementation of personalized medicine is available in Chapter 29 of Genomic and Personalized Medicine, 2nd edition [Williams, 2013]. Each of the articles in this special issue identify challenges that impacted the project as well as local solutions that can be mapped to the key elements outlined in Table I. Some have used tools of implementation science research including pragmatic clinical trials to formally study the genomic medicine implementation. These lessons learned should help to inform others exploring
opportunities to implement genomic medicine in their system.
GENOMIC MEDICINE IMPLEMENTATION EXAMPLES AND OPPORTUNITIES The paper by Lazaridis et al. [2014] describes the experience of a large health care system that has started an individualized medicine clinic that uses genomic data in conjunction with other relevant clinical data to individualize care for patients. The initial focus of the clinic was on two distinct patient populations, (1) patients with advanced cancer that have failed standard therapies and (2) patients with a suspected genetic condition that remains undiagnosed despite use of standard approaches to diagnosis. These groups were specifically selected because it was thought the value proposition for the use of individualized medicine would be highest for these patients. As with much of implementation the most important lesson learned was the importance of addressing issues related to the cultural change that a new program engenders. The authors note that creating the clinic as a separate stand‐alone entity allowed the creation of de novo patient care processes that were not tied to a specific pre‐existing medical or care delivery model. To develop the new model, the leadership of the clinic engaged a broad group of stakeholders to design the structures and
TABLE I. Selected Key Elements for Successful Implementation Desire to improve care Leadership commitment to quality Developing clinical champions Engagement of staff Meaningful problem solving Alignment of goals with organizational mission, vision, values and informal cultural values and norms Dedicated resources aligned with goals Active integration to bridge boundaries and barriers Operational functions aligned with initiatives, including establishing necessary infrastructure Adapted from Grol and Wensing [2004].
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processes of the clinic. In addition a broad representation of specialties was engaged as “lead” physicians in the clinic. The lead physician specialty would be matched to the presenting complaint of the patient in order to maximize content expertise that was anticipated to improve efficiency, diagnostic yield, and therapeutic effectiveness. Genetic counseling services are integrated into the clinic and certified genetic counselors serve as “shepherds” for the patients allowing the patients to have a single point of contact to assist with the complex evaluation. Multidisciplinary boards are used to interpret the genomic analyses that are performed as part of the clinic. The authors note that an ELSI research agenda is part of the clinic so that ELSI issues related to implementation can be formally studied.
FAMILY HISTORY Family history remains an important tool in the genomic medicine armamentarium but a large body of literature indicates that it is not routinely collected as part of clinical care. Even when family history is obtained the collection is not systematic and the risk deriving from the family history is inconsistently calculated and used to guide care. Two articles in the special issue focus on the implementation of family history. The paper by Orlando et al. [2014] studied implementation of patient‐ entered family history in the adult primary care setting. The goals of the project were threefold: Encourage patients to be active participants in their health care through use of family history. Promote education of patients and their providers about the importance of family history. Improve adherence to risk stratified preventive care guidelines based on family history risk.
The research team created an electronic patient entered family history tool that performed risk stratification through the use of evidence‐based algorithms and presented this to
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providers in the electronic health record (EHR) coupled with clinical decision support (CDS) that assists the clinician to use the information to target preventive interventions to the risk level of the patient (rather than using population based risk estimates). The authors studied the effectiveness of the use of the family history tool using a pragmatic clinical trial design—a class of trial that is very well suited for the study of implementation. The paper emphasizes the importance of implementation research. The elements identified by the authors as key to the successful implementation of the family history tool included: Focus on top‐level evidence recommendations such as those from the United States Preventive Services Task Force. Early and repeated consultation with primary care provider end users. Perform extensive reliability and validity testing. Measure the clinical impact of the intervention as well as the impact on the primary care provider “resource.”
Perhaps the most important evidence of the success of the implementation was that the providers requested operational funding to keep the system in use after the research funding ended [Orlando, personal communication]. Edelman et al. [2014] studied family history at the other end of the lifespan— the prenatal visit. The study team implemented a patient‐entered electronic family health history tool and used a pragmatic pre‐ post‐implementation study design to measure the impact on care. As with the Orlando paper, risk algorithms tied into electronic CDS. The tool was developed using an onsite needs assessment involving the clinician end users followed by development and installation of the tool customized for the site’s clinical workflow. The output of the CDS was matched to the American Congress of Obstetricians and Gynecologists’ antepartum record [ACOG, 2012]. Post‐implementation assessment demonstrated significant improvement in documentation of the
required family history elements compared to baseline. Condition specific discussions and documentation also improved. Cystic fibrosis screening discussions improved in one implementation site, but was unchanged in two other sites that had high pre‐ implementation rates and one site that had persistently low rates pre and post suggesting other factors were impacting implementation. This illustrates the importance of studying implementation using rigorous study design. Hemoglobinopathy screening was also studied, but no sites showed improvement requiring additional study. One drawback of the tool was that the family history information was not stored in a structured format. This means that the information cannot be used for future pregnancies or other purposes. While this was not possible for this implementation, storage of data whether family history or genomic, in a structured form that can be accessed and analyzed by the EHR is desirable.
PHARMACOGENOMICS It has long been known that different patients with a given condition exposed to the same dose of the same medication can exhibit markedly different responses that impact both efficacy and susceptibility to adverse events. Up until recently this variability was unable to be explained, but some has now been shown to be due to variation in genes that code for drug metabolizing enzymes, receptors, and proteins that affect drug uptake and transport. Significant evidence regarding the clinical validity and utility has been developed leading to the ability to generate evidence‐based pharmacogenomic guidelines by groups such as the Clinical Pharmacogenetic Implementation Consortium [CPIC, 2014]. Given the relative maturity of the evidence compared to other areas of genomic medicine, pharmacogenomics has been a desirable implementation target for early adopters. Four papers in this issue focus on pharmacogenomics implementation. Three of these programs use a model of pharmacy leader-
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ship and preemptive genotyping which may represent key insights into successful implementation of pharmacogenomics programs. The fourth paper [Shuldiner et al., 2014] describes a hybrid system that resulted in success despite significant implementation barriers. The success of these programs may indicate that pharmacogenomics may reside more comfortably in the purview of pharmacists rather than physicians, at least as far as programmatic development and leadership are concerned. Hoffman et al. [2014] describes a comprehensive pharmacogenomics program in a children’s research hospital. The stated goal of this program is to, “… establish processes for using pharmacogenetic tests in the electronic health record (EHR) to preemptively guide prescribing.” The hospital had initiated a novel delivery model in the 1980s that created a pharmaceutical department that operates both the clinical and laboratory aspects of the clinical pharmacy service. This model is very well suited to implementation of new services such as pharmacogenomics. The program has incorporated many of the key elements noted in Table I including a strong commitment from institutional leadership, proactive education of clinical end users, leveraging existing infrastructure such the EHR and governance structures (e.g., the hospital’s pharmacy and therapeutics committee) and engagement with end users and stakeholders. In particular the group has actively sought input from patients and their families through a family advisory council that provides input and guidance on the program. Most importantly the group recognizes the “lifetime” nature of germline genetic information and has developed ways for the information to persist in the EHR environment so it can be reused as needed in the course of care. This improves the value of the initial investment in testing and reduces waste from repeating tests that only need to be done once over the course of a patient’s lifetime. The authors conclude that, “Our approach to preemptive clinical pharmacogenetics has proven feasible, clinically useful, and scalable.”
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The project described in the paper by Shuldiner et al. [2014] chose clopidogrel and CYP2C19 as the first drug gene pair for implementation. The decision to start here reflected a combination of an adequate evidence base with an established clinical workflow in the health system that facilitated successful implementation. In contrast to the other papers describing pharmacogenomics implementation, the leadership of this program is not the sole responsibility of the pharmacy department but consists of a subcommittee of the Pharmacy and Therapeutics Committee with diverse expertise that includes pharmacists, providers and other members of the Program for Personalized and Genomic Medicine. The implementation landscape was also not as conducive as in some of the other programs. The team encountered significant infrastructure gaps that did not support preemptive genotyping or full integration into the EHR with CDS. Nonetheless, the program was able to be implemented using genotyping with a rapid turn‐around time (average 5 hr from sample collection to return of results) and communication of results using a variety of communication modalities including phone calls, e‐mails, and faxes that allowed the program to go forward as enhancements to the EHR were being made that will ultimately allow electronic reporting with CDS. This demonstrates that implementation can be successful in environments that are less conducive if the key elements are addressed and modified to fit the existing system structure and clinical workflow. The paper by Weitzel et al. [2014] describes the University of Florida (UF) Health Personalized Medicine Program (PMP) a pharmacist‐led, multidisciplinary initiative created in 2011 within the UF Clinical Translational Science Institute. The initial focus of this pharmacist‐led program has been in pharmacogenomics. Guiding principles for the program include: Establishing a regulatory body to oversee PMP initiatives. Develop a clear process for evidence evaluation and program approval.
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Use of preemptive chip‐based genotyping. Development and deployment of specific informatics CDS within the EHR to provide interpretation and clinical recommendations to clinician end users.
The first drug‐gene pair that was implemented was Clopidogrel and CYP2C19 because the CYP2C19 genotyping test was being incorporated as standard of care in the cardiac catheterization laboratory based on evidence of clinical utility and an existing clinical guideline [Scott et al., 2011]. Because of technical issues with the genotyping chip, this was implemented as a single stand‐alone test as opposed to use of a chip. The project was initially funded through research but was transitioned to clinical testing. The authors examined economic aspects of this testing including reimbursement. They report that 85% of payers reimbursed testing in the outpatient setting. A benefit cost‐analysis was performed and the authors note that, “… its conclusions were useful in demonstrating the short‐ and long‐term cost impacts of the program to hospital leadership.” They also used a number needed to genotype analysis to show that preemptive genotyping for a single drug‐gene pair is not a viable approach. These types of analyses are rarely performed for genetic testing but will need to be done going forward to understand the optimal approach and insure support from system leadership. The paper by O’Donnell et al. [2014] examines the usage of a genomic prescribing system. The authors point out the impact of adverse drug events, ineffective medications, and waste on the United States’ healthcare system an amount estimated to be in the billions of dollars annually. They note that pharmacogenomics has the potential to address some of these issues and potentially bring cost savings to the system. The authors describe the results of the first year of the “1200 Patients Project.” The project provides free pre‐emptive genotyping and uses that in the course of subsequent patient care through a combination of interventions such as reminders in the patient log in the EHR,
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as well as in person reminders and flags in paper charts. Prioritization of alerts and reminders used a red, yellow, green for high, medium, and low priority alerts, respectively. The authors note that the consent rate for patients approached for participation was very high, although the patient population tends to be more educated that the general population. Genotyping resulted in information that was relevant for 85% of clinic visits. Clinicians accessed information for all red alerts and 75% of yellow alerts and acceptance of recommendations related to the information was high. The biggest challenge for the program was validating the performance of the pharmacogenetic testing platform to meet the agreed upon quality threshold for clinical return of results.
WHOLE GENOME SEQUENCING The last 3–4 years has shown a dramatic increase in the application of next generation sequencing technologies in particular whole genome and whole exome sequencing (WGS). WGS raises a number of challenges including storage of large amounts of genome sequence, development, and application of robust bioinformatics pipelines, annotation of variant data for clinical use and preparation of providers and patients for use of the information in clinical care. Two papers in this issue address the latter two issues. Dorschner et al. [2014] addresses an important issue—how best to present the results of WGS results to clinicians. The research team used existing multi‐ gene panel reports as well as WGS reports from an outside laboratory to develop a draft report. The report was designed to include all mandated reporting elements as defined by the Clinical Laboratory Improvement Acts and the College of American Pathologists/American College of Medical Genetics and Genomics practice guidelines. This draft was then reviewed by both genetic professionals and non‐ genetic practitioners that were likely to receive WGS reports based on their specialty. Not unexpectedly, the non‐
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geneticists requested less information and detail in the reports than the genetics professionals. In addition the non‐ genetic clinicians requested links to additional information embedded in the report thus highlighting the inherent limitations of a narrative report compared with an interactive report for complex testing. The information obtained from the clinicians is used to design subsequent drafts and rapid cycle re‐iteration allows continuous improvement of the report. One of the biggest barriers to the use of WGS in clinical practice is the dearth of information on the clinical significance of individual variants in genes of medical interest. At present the repositories that aggregate variant level data associated with clinical phenotype (e.g., human gene mutation database, exome variant server) while useful, are incomplete and sometimes inaccurately annotated limiting their relevance for clinical care [Ramos et al., 2014]. The need for a clinical grade variant repository led the NHGRI and Wellcome Trust to, “… convene a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use.” The paper by Ramos et al. [2014] provides a commentary on the results of that meeting. A total of 18 recommendations
resulted that were aggregated into three broad categories: Identification and analysis of existing genetic variation resources. Identifying genetic variants for potential clinical action. Create a translation loop for genomic medicine.
Investigation of these three broad content areas identified significant deficiencies in currently available resources that were unlikely to be remedied in an incremental fashion. NHGRI has subsequently funded a consortium of research groups to develop the Clinical Genome Resource that will be designed to augment existing resources and provide clinically useful genomic information to clinician end users [NHGRI, 2014b].
ETHICAL, LEGAL, AND SOCIAL ISSUES One of the distinctive features of genomic research has been the intentional funding of research into the ethical, legal, and social issues that impact the use of genomic information in research and clinical care. Burke et al. [2014] review the pros and cons of the return of clinically actionable genomic results to research participants. They note that this is a debate that has been going on for decades with genomic
INTRODUCTION
analysis being the latest technology to raise these issues. They organize their paper around the question: “If clinical relevance is the motivation for returning research results, how does this process differ from returning test results in clinical care?” They note that two important trends make this question relevant and timely namely the increased use of WGS technology in research and the recognition that more and more genomic information has clinical utility. The paper focuses on several ethical principles that bear on both research and clinical care including a critically important legal question—does the act of returning a medically actionable result found in a research project transform the researcher/subject relationship into a provider/patient relationship? The issues raised in this paper are certain to be the subject of much debate and study in the future.
THE VALUE OF GENOMIC MEDICINE Value can be thought of as a relationship between outcomes and cost and this relationship can be represented (Fig. 1). The goal of implementing any intervention should be to improve outcomes while ideally reducing, or at the very least keeping the cost of care the same. By extension the goal of investing in genomic medicine implementation is to critically analyze emerging genomic
Figure 1. The value grid. Value is represented as a relationship between cost and outcomes of care. High value interventions are depicted in the green, lower value interventions in the yellow, and unacceptable value in the red. High value interventions should be rapidly implemented while unacceptable value interventions should be eliminated from clinical care.
INTRODUCTION
knowledge not only from the perspective of the science but more importantly with attention to the potential to improve patient‐centered outcomes and to the impact on the total cost of care. Three examples illustrate the potential impact of one type of genomic medicine, pharmacogenomics, on value. An example of good value is the use of HLA‐B 57:01 genotyping prior to initiation of the anti‐retroviral medication abacavir. Presence of this genotype drastically increases the risk for developing a severe hypersensitivy reaction leading to significant morbidity and mortality [Mallal et al., 2008]. Cost‐ effectiveness analysis of genotyping prior to use of abacavir has been shown to be cost saving under a broad range of assumptions [Kauf et al., 2010] thus would fit solidly in the upper left hand sector (Fig. 1 Box A). There are situations where an increase in cost of care can be justified by a dramatic improvement in outcomes. Consider the case of chronic myelogenous leukemia (CML) and imatinib. CML is a common hematologic malignancy for which there were no effective treatments. In 1960, CML was one of the first malignancies for which a specific genetic cause was identified, the Philadelphia chromosome. This chromosomal rearrangement results in fusion of two genes that leads to production of an abnormal signaling protein (a tyrosine kinase) that is always activated leading to unchecked cell proliferation. Recognition of the underlying cause allowed development of a drug, imatinib that targets and turns off the abnormal protein. Imatinib was successfully tested in clinical trials for CML patients and a treatment for the untreatable was brought into clinical use [Gambacorti‐ Passerini, 2008]. Imatinib is a very expensive medication but the improvement in patient outcomes would appear to justify the increased cost of care if the surrogate of payer reimbursement is used to measure the “value” of the service (Fig. 1 Box B). This situation is likely to be seen more and more frequently as more molecular mechanisms are identified and targeted by novel pharmaceutical agents.
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Intelligent application of new technology can dramatically improve the value of the technology. Another novel oncologic agent recently introduced into care for patients with metastatic colorectal cancer is cetuximab, a monoclonal antibody that targets the extracellular domain of epidermal growth factor receptor (EGFR). Tumor response to this agent is predicted by the presence or absence of mutations in the KRAS gene in the tumor. Presence of a mutation (which occurs in about 85% of patients) confers resistance to the medication. Routine testing for relevant KRAS mutations in the tumor allows for use of this medication only in those tumors that are predicted to be sensitive [Nakadate et al., 2013] (Fig. 1 Box B) dramatically improving the relative value of the treatment through avoiding increasing the cost of care by using the medication in patients that are predicted not to benefit from the therapy (Fig. 1 Box C). The final article in the issue [Wade et al., 2014] provides a viewpoint from a leader of a large health care delivery system that has invested in genomic medicine implementation. Investment in an innovative technology can be justified if it is consistent with the mission, vision and values of the organization, is able to create value and positions the organization to better adapt to a rapidly changing healthcare environment. Successful implementation, “… takes a multidisciplinary team approach that involves a broad range of expertise including quality improvement, systems re‐engineering, informatics, health economics, bioethics, and healthcare policy. Success is defined by realizing value for our patients utilizing approaches that are generalizable to other health care systems.”
CONCLUSION Genomic medicine is moving into the clinic. Careful attention to the lessons of successful implementations, such as the examples in this issue can increase the likelihood of success and achieve value for patients and the healthcare system.
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REFERENCES ACOG. Practice bulletin: Clinical management guidelines for obstetrician‐gynecologists. http://www.acog.org//media/List%20of %20Titles/PBListOfTitles.pdf 2012. Last accessed 1/14/2014. Burke W, Evans BJ, Jarvik GP. 2014. Return of results: Research versus clinical care. Am J Med Genet C 166C[information from the issue]. CPIC. 2014. Dosing Guidelines. http://www. pharmgkb.org/view/dosing‐guidelines.do? source¼CPIC# Last accessed 1/14/2014. Dorschner MO, Amendola LM, Shirts BH, Kiedrowski L, Salama J, Gordon AS, Fullerton SM, Tarczy‐Hornoch P, Byers PH, Jarvik GP. 2014. Refining the structure and content of clinical genomic reports. Am J Med Genet C 166C[information from the issue]. Eccles MP, Mittman BS. 2006. Welcome to implementation science. Implement Sci 1:1–3. Edelman EA, Lin BK, Doksum T, Drohan B, Edelson V, Dolan SM, Hughes K, O’Leary J, Vasquez L, Copeland S, Galvin S, De Groat N, Pardanani S, Feero WG, Adams C, Jones R, Scott J. 2014. Evaluation of a novel electronic genetic screening and clinical decision support tool in prenatal clinical settings. Am J Med Genet C 166C[information from the issue]. Gambacorti‐Passerini C. 2008. Part I: Milestones in personalised medicine–imatinib. Lancet Oncol 9:600. Grol R, Wensing R. 2004. What drives change? Barriers to and incentives for achieving evidence‐based practice. Med J Aust 180: 557–560. Hoffman JM, Haidar CE, Wilkinson MR, Crews KR, Baker DK, Kornegay NM, Yang W, Pui CH, Reiss UM, Gaur AH, Howard SC, Evans WE, Broeckel U, Relling MV. 2014. PG4KDS: A model for the clinical implementation of pre‐emptive pharmacogenetics. Am J Med Genet C 166C[information from the issue]. Kauf TL, Farkouh RA, Earnshaw SR, Watson ME, Maroudas P, Chambers MG. 2010. Economic efficiency of genetic screening to inform the use of abacavir sulfate in the treatment of HIV. Pharmacoeconomics 28:1025–1039. Lazaridis K, McAllister T, Babovic‐Vuksanovic D, Beck S, Borad M, Bryce A, Chanan‐Khan A, Ferber M, Fonseca R, Johnson K, Klee E, Lindor N, McCormick J, McWilliams R, Parker A, Riegert‐Johnson D, Rohrer Vitek C, Schahl K, Schultz C, Stewart K, Then G, Wieben E, Farrugia G. 2014. Implementing individualized medicine into the medical practice. Am J Med Genet C 166C[information from the issue]. Mallal S, Phillips E, Carosi G, Molina JM, Workman C, Tomazic J, Jägel‐Guedes E, Rugina S, Kozyrev O, Cid JF, Hay P, Nolan D, Hughes S, Hughes A, Ryan S, Fitch N, Thorborn D, Benbow A, PREDICT‐1 Study Team. 2008. HLA‐B 5701 screening for hypersensitivity to abacavir. N Engl J Med 358:568–579. Manolio TA, Green ED. 2014. Leading the Way to Genomic Medicine. Am J Med Genet 166C: This issue.
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Nakadate Y, Kodera Y, Kitamura Y, Shirasawa S, Tachibana T, Tamura T, Koizumi F. 2013. KRAS mutation confers resistance to antibody‐dependent cellular cytotoxicity of cetuximab against human colorectal cancer cells. Int J Cancer DOI: 10.1002/ijc.28550 [Epub ahead of print]. NHGRI. 2012. http://www.google.com/url? sa¼t&rct¼j&q¼&esrc¼s&frm¼1&source¼ web&cd¼1&ved¼0CCwQFjAA&url¼http %3A%2F%2Fwww.genome.gov%2Fpages% 2FAbout%2FNACHGR%2FSept2012Agenda Documents%2FGenomic_Medicine_Definition_ 080112_RChisolm.pdf&ei¼HU3UUr‐vCuq 0sQSS14DwCw&usg¼AFQjCNFCo_iTb‐_ Hc1Gq9fJ1a_jB3qTCFQ&sig2¼D6Qf_ laHFPutbMJjwIUwpw Last accessed 1/13/ 2014. NHGRI. 2014a. http://www.genome.gov/ 27549225 Last accessed 1/13/2014. NHGRI. 2014b. http://www.nih.gov/news/ health/sep2013/nhgri‐25.htm Last accessed 1/14/2014. O’Donnell PH, Danahey K, Jacobs M, Wadhwa NR, Yuen S, Bush A, Sacro Y, Sorrentino MJ, Siegler M, Harper W, Warrick A, Das S, Saner D, Corless CL, Ratain MJ. 2014. Adoption of a clinical pharmacogenomics implementation program during outpatient care–initial results of the University of
Chicago “1200 Patients Project.” Am J Med Genet C 166C[information from the issue]. Orlando LA, Wu RR, Himmel T, Buchanan A, Powell KP, Hauser E, Henrich VC, Ginsburg GS. 2014. Health services impact of implementing family health history risk stratification in primary care. Am J Med Genet C 166C[information from the issue]. Ramos EM, Din‐Lovinescu C, Berg JS, Brooks LD, Duncanson A, Dunn M, Good P, Hubbard T, Jarvik GP, O’Donnell C, Sherry ST, Aronson N, Biesecker LG, Blumberg B, Calonge N, Colhoun HM, Epstein RS, Flicek P, Gordon ES, Green ED, Green RC, Hurles M, Kawamoto K, Knaus W, Ledbetter DH, Levy HP, Lyon E, Maglott D, McLeod HL, Rahman N, Randhawa G, Wicklund C, Manolio TA, Chisholm RL, Williams MS. 2014. Characterizing genetic variants for clinical action. Am J Med Genet C 166C[information from the issue]. Scott SA, Sangkuhl K, Gardner EE, Stein CM, Hulot J‐S, Johnson JA, Roden DM, Klein TM, Shuldiner AR. 2011. Clinical pharmacogenetics implementation consortium guidelines for cytochrome P450‐ 2C19 (CYP2C19) genotype and clopidogrel therapy. Clin Pharmacol Ther 90:328– 332.
INTRODUCTION Shuldiner AR, Palmer K, Pakyz RE, Alestock TD, Maloney KA, O’Neill C, Bhatty S, Schub J, Overby CL, Horenstein RB, Pollin TI, Keleman MD, Beitelshees AL, Robinson SW, Blitzer MG, McArdle PF, Brown L, Jeng LJB, Zhao RY, Ambulos N, Vesely MR. 2014. Implementation of genomic medicine and pharmacogenomics: The University of Maryland program for personalized and genomic medicine. Am J Med Genet C 166C[information from the issue]. Wade JE, Ledbetter DH, Williams MS. 2014. Implementation of genomic medicine in a health care delivery system: A value proposition? Am J Med Genet C 166C[information from the issue]. Weitzel KW, Elsey AR, Langaee TY, Burkley B, Nessl DR, Obeng AO, Staley BJ, Dong HJ, Allan RW, Liu F, Cooper‐DeHoff RM, Anderson RD, Conlon M, Clare‐Salzler MJ, Nelson DR, Johnson JA. 2014. Clinical pharmacogenetics implementation: Approaches, successes, and challenges. Am J Med Genet C 166C[information from the issue]. Williams MS. 2013. Delivery of personalized medicine in an integrated healthcare system. In: Ginsburg GS Willard HF, editors. Genomic and personalized medicine, 2nd edition. London: Academic Press. pp 340–352.
I N T R O D U C T I O N
Genomic Medicine Implementation: Learning by Example MARC S. WILLIAMS*
Genomic Medicine is beginning to emerge into clinical practice. The National Human Genome Research Institute's Genomic Medicine Working Group consists of organizations that have begun to implement some aspect of genomic medicine (e.g., family history, systematic implementation of Mendelian disease program, pharmacogenomics, whole exome/genome sequencing). This article concisely reviews the working group and provides a broader context for the articles in the special issue including an assessment of anticipated provider needs and ethical, legal, and social issues relevant to the implementation of genomic medicine. The challenges of implementation of innovation in clinical practice and the potential value of genomic medicine are discussed. © 2014 Wiley Periodicals, Inc. KEY WORDS: genomic medicine; personalized medicine; family history; pharmacogenetics; implementation; ethical legal and social issues
How to cite this article: Williams MS. 2014. Genomic medicine implementation: Learning by example. Am J Med Genet Part C Semin Med Genet 166C:8–14.
INTRODUCTION In August of 2012, the National Human Genome Research Institute (NHGRI) published a definition of genomic medicine which is defined as, “An emerging medical discipline that involves using genomic information about an individual as part of their clinical care (e.g., for diagnostic or therapeutic decision‐making) and the other implications of that clinical use.” [NHGRI, 2012] This definition is intentionally narrow and defines genomic as direct information about DNA or RNA, putting the study of more downstream products derived from the genome (i.e., proteomics, glycomics, metabolomics, etc.) as outside the definitional scope of genomic medicine for the activities of the NHGRI. One exception to this narrow definition is the inclusion of family history as being within the purview of genomic medicine implementation.
As noted in the article by Manolio and Green [2014] that opens this special issue of the Seminars, implementation of genomic medicine is represented on the NHGRI’s most recent strategic plan. There are a many issues involved in the use of genomic medicine in clinical care including but not limited to the evidence to support the role of genomic medicine interventions to improve outcomes for patients, patient, provider and health system readiness and education, ethical, legal, and social issues (ELSI) as well as considerations related to cost and reimbursement. Despite these issues some groups are deliberately moving forward with implementation of certain genomic medicine projects. In June of 2011, NHGRI convened the first of a series of genomic medicine meetings focused on implementation of genomic medicine. This meeting brought together 20 centers that were implementing some aspect of genomic
medicine (i.e., family history, genetics, and genomics). A series of meetings followed the topics of which can be found on the NHGRI website. [NHGRI, 2014a] At the Genomic Medicine IV meeting it was decided that publication of genomic medicine implementation projects was a desirable goal and the author/editor was charged to facilitate this activity. This has resulted in this special issue of Seminars in Medical Genetics devoted to the Implementation of Genomic Medicine.
THE SCIENCE OF IMPLEMENTATION Implementation of change in medical practice is challenging. The primary issues do not involve lack of evidence or recalcitrant providers although these can contribute. Rather the issue is introducing change into a large and complex system that due to sheer size, complexity
Conflict of interest: none. Dr. Williams has been director of Geisinger Health System's Genomic Medicine Institute, since 2012. He has led genomic medicine implementation projects at Geisinger and at his previous institution Intermountain Healthcare. He is a member of the National Human Genome Research Institute's Genomic Medicine Working Group steering committee. *Correspondence to: Marc S. Williams, M.D., Genomic Medicine Institute, Geisinger Health System, 100N Academy Drive, Mail Stop 26‐20, Danville, PA 17822‐2026. E‐mail: [email protected] DOI 10.1002/ajmg.c.31394 Article first published online in Wiley Online Library (wileyonlinelibrary.com): 10 March 2014
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and momentum resists change even with a high likelihood of improved care. To try and understand the challenges, a new field of study, implementation science, has developed. Eccles and Mittman [2006] define implementation science as, ”… the scientific study of methods to promote the systematic uptake of research findings and other evidence‐ based practices into routine practice, and, hence, to improve the quality and effectiveness of health services and care. This field includes the study of influences on healthcare professional and organizational behavior.” Successful implementation requires attention to factors in three domains: predisposing factors (e.g., knowledge and attitudes in the target group), enabling factors (e.g., capacity, resources, access) and reinforcing factors (opinions and behaviors of others) [Grol and Wensing, 2004]. Key elements for successful implementation are outlined in Table I. An in depth discussion of the implementation of personalized medicine is available in Chapter 29 of Genomic and Personalized Medicine, 2nd edition [Williams, 2013]. Each of the articles in this special issue identify challenges that impacted the project as well as local solutions that can be mapped to the key elements outlined in Table I. Some have used tools of implementation science research including pragmatic clinical trials to formally study the genomic medicine implementation. These lessons learned should help to inform others exploring
opportunities to implement genomic medicine in their system.
GENOMIC MEDICINE IMPLEMENTATION EXAMPLES AND OPPORTUNITIES The paper by Lazaridis et al. [2014] describes the experience of a large health care system that has started an individualized medicine clinic that uses genomic data in conjunction with other relevant clinical data to individualize care for patients. The initial focus of the clinic was on two distinct patient populations, (1) patients with advanced cancer that have failed standard therapies and (2) patients with a suspected genetic condition that remains undiagnosed despite use of standard approaches to diagnosis. These groups were specifically selected because it was thought the value proposition for the use of individualized medicine would be highest for these patients. As with much of implementation the most important lesson learned was the importance of addressing issues related to the cultural change that a new program engenders. The authors note that creating the clinic as a separate stand‐alone entity allowed the creation of de novo patient care processes that were not tied to a specific pre‐existing medical or care delivery model. To develop the new model, the leadership of the clinic engaged a broad group of stakeholders to design the structures and
TABLE I. Selected Key Elements for Successful Implementation Desire to improve care Leadership commitment to quality Developing clinical champions Engagement of staff Meaningful problem solving Alignment of goals with organizational mission, vision, values and informal cultural values and norms Dedicated resources aligned with goals Active integration to bridge boundaries and barriers Operational functions aligned with initiatives, including establishing necessary infrastructure Adapted from Grol and Wensing [2004].
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processes of the clinic. In addition a broad representation of specialties was engaged as “lead” physicians in the clinic. The lead physician specialty would be matched to the presenting complaint of the patient in order to maximize content expertise that was anticipated to improve efficiency, diagnostic yield, and therapeutic effectiveness. Genetic counseling services are integrated into the clinic and certified genetic counselors serve as “shepherds” for the patients allowing the patients to have a single point of contact to assist with the complex evaluation. Multidisciplinary boards are used to interpret the genomic analyses that are performed as part of the clinic. The authors note that an ELSI research agenda is part of the clinic so that ELSI issues related to implementation can be formally studied.
FAMILY HISTORY Family history remains an important tool in the genomic medicine armamentarium but a large body of literature indicates that it is not routinely collected as part of clinical care. Even when family history is obtained the collection is not systematic and the risk deriving from the family history is inconsistently calculated and used to guide care. Two articles in the special issue focus on the implementation of family history. The paper by Orlando et al. [2014] studied implementation of patient‐ entered family history in the adult primary care setting. The goals of the project were threefold: Encourage patients to be active participants in their health care through use of family history. Promote education of patients and their providers about the importance of family history. Improve adherence to risk stratified preventive care guidelines based on family history risk.
The research team created an electronic patient entered family history tool that performed risk stratification through the use of evidence‐based algorithms and presented this to
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providers in the electronic health record (EHR) coupled with clinical decision support (CDS) that assists the clinician to use the information to target preventive interventions to the risk level of the patient (rather than using population based risk estimates). The authors studied the effectiveness of the use of the family history tool using a pragmatic clinical trial design—a class of trial that is very well suited for the study of implementation. The paper emphasizes the importance of implementation research. The elements identified by the authors as key to the successful implementation of the family history tool included: Focus on top‐level evidence recommendations such as those from the United States Preventive Services Task Force. Early and repeated consultation with primary care provider end users. Perform extensive reliability and validity testing. Measure the clinical impact of the intervention as well as the impact on the primary care provider “resource.”
Perhaps the most important evidence of the success of the implementation was that the providers requested operational funding to keep the system in use after the research funding ended [Orlando, personal communication]. Edelman et al. [2014] studied family history at the other end of the lifespan— the prenatal visit. The study team implemented a patient‐entered electronic family health history tool and used a pragmatic pre‐ post‐implementation study design to measure the impact on care. As with the Orlando paper, risk algorithms tied into electronic CDS. The tool was developed using an onsite needs assessment involving the clinician end users followed by development and installation of the tool customized for the site’s clinical workflow. The output of the CDS was matched to the American Congress of Obstetricians and Gynecologists’ antepartum record [ACOG, 2012]. Post‐implementation assessment demonstrated significant improvement in documentation of the
required family history elements compared to baseline. Condition specific discussions and documentation also improved. Cystic fibrosis screening discussions improved in one implementation site, but was unchanged in two other sites that had high pre‐ implementation rates and one site that had persistently low rates pre and post suggesting other factors were impacting implementation. This illustrates the importance of studying implementation using rigorous study design. Hemoglobinopathy screening was also studied, but no sites showed improvement requiring additional study. One drawback of the tool was that the family history information was not stored in a structured format. This means that the information cannot be used for future pregnancies or other purposes. While this was not possible for this implementation, storage of data whether family history or genomic, in a structured form that can be accessed and analyzed by the EHR is desirable.
PHARMACOGENOMICS It has long been known that different patients with a given condition exposed to the same dose of the same medication can exhibit markedly different responses that impact both efficacy and susceptibility to adverse events. Up until recently this variability was unable to be explained, but some has now been shown to be due to variation in genes that code for drug metabolizing enzymes, receptors, and proteins that affect drug uptake and transport. Significant evidence regarding the clinical validity and utility has been developed leading to the ability to generate evidence‐based pharmacogenomic guidelines by groups such as the Clinical Pharmacogenetic Implementation Consortium [CPIC, 2014]. Given the relative maturity of the evidence compared to other areas of genomic medicine, pharmacogenomics has been a desirable implementation target for early adopters. Four papers in this issue focus on pharmacogenomics implementation. Three of these programs use a model of pharmacy leader-
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ship and preemptive genotyping which may represent key insights into successful implementation of pharmacogenomics programs. The fourth paper [Shuldiner et al., 2014] describes a hybrid system that resulted in success despite significant implementation barriers. The success of these programs may indicate that pharmacogenomics may reside more comfortably in the purview of pharmacists rather than physicians, at least as far as programmatic development and leadership are concerned. Hoffman et al. [2014] describes a comprehensive pharmacogenomics program in a children’s research hospital. The stated goal of this program is to, “… establish processes for using pharmacogenetic tests in the electronic health record (EHR) to preemptively guide prescribing.” The hospital had initiated a novel delivery model in the 1980s that created a pharmaceutical department that operates both the clinical and laboratory aspects of the clinical pharmacy service. This model is very well suited to implementation of new services such as pharmacogenomics. The program has incorporated many of the key elements noted in Table I including a strong commitment from institutional leadership, proactive education of clinical end users, leveraging existing infrastructure such the EHR and governance structures (e.g., the hospital’s pharmacy and therapeutics committee) and engagement with end users and stakeholders. In particular the group has actively sought input from patients and their families through a family advisory council that provides input and guidance on the program. Most importantly the group recognizes the “lifetime” nature of germline genetic information and has developed ways for the information to persist in the EHR environment so it can be reused as needed in the course of care. This improves the value of the initial investment in testing and reduces waste from repeating tests that only need to be done once over the course of a patient’s lifetime. The authors conclude that, “Our approach to preemptive clinical pharmacogenetics has proven feasible, clinically useful, and scalable.”
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The project described in the paper by Shuldiner et al. [2014] chose clopidogrel and CYP2C19 as the first drug gene pair for implementation. The decision to start here reflected a combination of an adequate evidence base with an established clinical workflow in the health system that facilitated successful implementation. In contrast to the other papers describing pharmacogenomics implementation, the leadership of this program is not the sole responsibility of the pharmacy department but consists of a subcommittee of the Pharmacy and Therapeutics Committee with diverse expertise that includes pharmacists, providers and other members of the Program for Personalized and Genomic Medicine. The implementation landscape was also not as conducive as in some of the other programs. The team encountered significant infrastructure gaps that did not support preemptive genotyping or full integration into the EHR with CDS. Nonetheless, the program was able to be implemented using genotyping with a rapid turn‐around time (average 5 hr from sample collection to return of results) and communication of results using a variety of communication modalities including phone calls, e‐mails, and faxes that allowed the program to go forward as enhancements to the EHR were being made that will ultimately allow electronic reporting with CDS. This demonstrates that implementation can be successful in environments that are less conducive if the key elements are addressed and modified to fit the existing system structure and clinical workflow. The paper by Weitzel et al. [2014] describes the University of Florida (UF) Health Personalized Medicine Program (PMP) a pharmacist‐led, multidisciplinary initiative created in 2011 within the UF Clinical Translational Science Institute. The initial focus of this pharmacist‐led program has been in pharmacogenomics. Guiding principles for the program include: Establishing a regulatory body to oversee PMP initiatives. Develop a clear process for evidence evaluation and program approval.
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Use of preemptive chip‐based genotyping. Development and deployment of specific informatics CDS within the EHR to provide interpretation and clinical recommendations to clinician end users.
The first drug‐gene pair that was implemented was Clopidogrel and CYP2C19 because the CYP2C19 genotyping test was being incorporated as standard of care in the cardiac catheterization laboratory based on evidence of clinical utility and an existing clinical guideline [Scott et al., 2011]. Because of technical issues with the genotyping chip, this was implemented as a single stand‐alone test as opposed to use of a chip. The project was initially funded through research but was transitioned to clinical testing. The authors examined economic aspects of this testing including reimbursement. They report that 85% of payers reimbursed testing in the outpatient setting. A benefit cost‐analysis was performed and the authors note that, “… its conclusions were useful in demonstrating the short‐ and long‐term cost impacts of the program to hospital leadership.” They also used a number needed to genotype analysis to show that preemptive genotyping for a single drug‐gene pair is not a viable approach. These types of analyses are rarely performed for genetic testing but will need to be done going forward to understand the optimal approach and insure support from system leadership. The paper by O’Donnell et al. [2014] examines the usage of a genomic prescribing system. The authors point out the impact of adverse drug events, ineffective medications, and waste on the United States’ healthcare system an amount estimated to be in the billions of dollars annually. They note that pharmacogenomics has the potential to address some of these issues and potentially bring cost savings to the system. The authors describe the results of the first year of the “1200 Patients Project.” The project provides free pre‐emptive genotyping and uses that in the course of subsequent patient care through a combination of interventions such as reminders in the patient log in the EHR,
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as well as in person reminders and flags in paper charts. Prioritization of alerts and reminders used a red, yellow, green for high, medium, and low priority alerts, respectively. The authors note that the consent rate for patients approached for participation was very high, although the patient population tends to be more educated that the general population. Genotyping resulted in information that was relevant for 85% of clinic visits. Clinicians accessed information for all red alerts and 75% of yellow alerts and acceptance of recommendations related to the information was high. The biggest challenge for the program was validating the performance of the pharmacogenetic testing platform to meet the agreed upon quality threshold for clinical return of results.
WHOLE GENOME SEQUENCING The last 3–4 years has shown a dramatic increase in the application of next generation sequencing technologies in particular whole genome and whole exome sequencing (WGS). WGS raises a number of challenges including storage of large amounts of genome sequence, development, and application of robust bioinformatics pipelines, annotation of variant data for clinical use and preparation of providers and patients for use of the information in clinical care. Two papers in this issue address the latter two issues. Dorschner et al. [2014] addresses an important issue—how best to present the results of WGS results to clinicians. The research team used existing multi‐ gene panel reports as well as WGS reports from an outside laboratory to develop a draft report. The report was designed to include all mandated reporting elements as defined by the Clinical Laboratory Improvement Acts and the College of American Pathologists/American College of Medical Genetics and Genomics practice guidelines. This draft was then reviewed by both genetic professionals and non‐ genetic practitioners that were likely to receive WGS reports based on their specialty. Not unexpectedly, the non‐
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geneticists requested less information and detail in the reports than the genetics professionals. In addition the non‐ genetic clinicians requested links to additional information embedded in the report thus highlighting the inherent limitations of a narrative report compared with an interactive report for complex testing. The information obtained from the clinicians is used to design subsequent drafts and rapid cycle re‐iteration allows continuous improvement of the report. One of the biggest barriers to the use of WGS in clinical practice is the dearth of information on the clinical significance of individual variants in genes of medical interest. At present the repositories that aggregate variant level data associated with clinical phenotype (e.g., human gene mutation database, exome variant server) while useful, are incomplete and sometimes inaccurately annotated limiting their relevance for clinical care [Ramos et al., 2014]. The need for a clinical grade variant repository led the NHGRI and Wellcome Trust to, “… convene a workshop to consider the processes and resources needed to: (1) identify clinically valid genetic variants; (2) decide whether they are actionable and what the action should be; and (3) provide this information for clinical use.” The paper by Ramos et al. [2014] provides a commentary on the results of that meeting. A total of 18 recommendations
resulted that were aggregated into three broad categories: Identification and analysis of existing genetic variation resources. Identifying genetic variants for potential clinical action. Create a translation loop for genomic medicine.
Investigation of these three broad content areas identified significant deficiencies in currently available resources that were unlikely to be remedied in an incremental fashion. NHGRI has subsequently funded a consortium of research groups to develop the Clinical Genome Resource that will be designed to augment existing resources and provide clinically useful genomic information to clinician end users [NHGRI, 2014b].
ETHICAL, LEGAL, AND SOCIAL ISSUES One of the distinctive features of genomic research has been the intentional funding of research into the ethical, legal, and social issues that impact the use of genomic information in research and clinical care. Burke et al. [2014] review the pros and cons of the return of clinically actionable genomic results to research participants. They note that this is a debate that has been going on for decades with genomic
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analysis being the latest technology to raise these issues. They organize their paper around the question: “If clinical relevance is the motivation for returning research results, how does this process differ from returning test results in clinical care?” They note that two important trends make this question relevant and timely namely the increased use of WGS technology in research and the recognition that more and more genomic information has clinical utility. The paper focuses on several ethical principles that bear on both research and clinical care including a critically important legal question—does the act of returning a medically actionable result found in a research project transform the researcher/subject relationship into a provider/patient relationship? The issues raised in this paper are certain to be the subject of much debate and study in the future.
THE VALUE OF GENOMIC MEDICINE Value can be thought of as a relationship between outcomes and cost and this relationship can be represented (Fig. 1). The goal of implementing any intervention should be to improve outcomes while ideally reducing, or at the very least keeping the cost of care the same. By extension the goal of investing in genomic medicine implementation is to critically analyze emerging genomic
Figure 1. The value grid. Value is represented as a relationship between cost and outcomes of care. High value interventions are depicted in the green, lower value interventions in the yellow, and unacceptable value in the red. High value interventions should be rapidly implemented while unacceptable value interventions should be eliminated from clinical care.
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knowledge not only from the perspective of the science but more importantly with attention to the potential to improve patient‐centered outcomes and to the impact on the total cost of care. Three examples illustrate the potential impact of one type of genomic medicine, pharmacogenomics, on value. An example of good value is the use of HLA‐B 57:01 genotyping prior to initiation of the anti‐retroviral medication abacavir. Presence of this genotype drastically increases the risk for developing a severe hypersensitivy reaction leading to significant morbidity and mortality [Mallal et al., 2008]. Cost‐ effectiveness analysis of genotyping prior to use of abacavir has been shown to be cost saving under a broad range of assumptions [Kauf et al., 2010] thus would fit solidly in the upper left hand sector (Fig. 1 Box A). There are situations where an increase in cost of care can be justified by a dramatic improvement in outcomes. Consider the case of chronic myelogenous leukemia (CML) and imatinib. CML is a common hematologic malignancy for which there were no effective treatments. In 1960, CML was one of the first malignancies for which a specific genetic cause was identified, the Philadelphia chromosome. This chromosomal rearrangement results in fusion of two genes that leads to production of an abnormal signaling protein (a tyrosine kinase) that is always activated leading to unchecked cell proliferation. Recognition of the underlying cause allowed development of a drug, imatinib that targets and turns off the abnormal protein. Imatinib was successfully tested in clinical trials for CML patients and a treatment for the untreatable was brought into clinical use [Gambacorti‐ Passerini, 2008]. Imatinib is a very expensive medication but the improvement in patient outcomes would appear to justify the increased cost of care if the surrogate of payer reimbursement is used to measure the “value” of the service (Fig. 1 Box B). This situation is likely to be seen more and more frequently as more molecular mechanisms are identified and targeted by novel pharmaceutical agents.
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Intelligent application of new technology can dramatically improve the value of the technology. Another novel oncologic agent recently introduced into care for patients with metastatic colorectal cancer is cetuximab, a monoclonal antibody that targets the extracellular domain of epidermal growth factor receptor (EGFR). Tumor response to this agent is predicted by the presence or absence of mutations in the KRAS gene in the tumor. Presence of a mutation (which occurs in about 85% of patients) confers resistance to the medication. Routine testing for relevant KRAS mutations in the tumor allows for use of this medication only in those tumors that are predicted to be sensitive [Nakadate et al., 2013] (Fig. 1 Box B) dramatically improving the relative value of the treatment through avoiding increasing the cost of care by using the medication in patients that are predicted not to benefit from the therapy (Fig. 1 Box C). The final article in the issue [Wade et al., 2014] provides a viewpoint from a leader of a large health care delivery system that has invested in genomic medicine implementation. Investment in an innovative technology can be justified if it is consistent with the mission, vision and values of the organization, is able to create value and positions the organization to better adapt to a rapidly changing healthcare environment. Successful implementation, “… takes a multidisciplinary team approach that involves a broad range of expertise including quality improvement, systems re‐engineering, informatics, health economics, bioethics, and healthcare policy. Success is defined by realizing value for our patients utilizing approaches that are generalizable to other health care systems.”
CONCLUSION Genomic medicine is moving into the clinic. Careful attention to the lessons of successful implementations, such as the examples in this issue can increase the likelihood of success and achieve value for patients and the healthcare system.
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INTRODUCTION Shuldiner AR, Palmer K, Pakyz RE, Alestock TD, Maloney KA, O’Neill C, Bhatty S, Schub J, Overby CL, Horenstein RB, Pollin TI, Keleman MD, Beitelshees AL, Robinson SW, Blitzer MG, McArdle PF, Brown L, Jeng LJB, Zhao RY, Ambulos N, Vesely MR. 2014. Implementation of genomic medicine and pharmacogenomics: The University of Maryland program for personalized and genomic medicine. Am J Med Genet C 166C[information from the issue]. Wade JE, Ledbetter DH, Williams MS. 2014. Implementation of genomic medicine in a health care delivery system: A value proposition? Am J Med Genet C 166C[information from the issue]. Weitzel KW, Elsey AR, Langaee TY, Burkley B, Nessl DR, Obeng AO, Staley BJ, Dong HJ, Allan RW, Liu F, Cooper‐DeHoff RM, Anderson RD, Conlon M, Clare‐Salzler MJ, Nelson DR, Johnson JA. 2014. Clinical pharmacogenetics implementation: Approaches, successes, and challenges. Am J Med Genet C 166C[information from the issue]. Williams MS. 2013. Delivery of personalized medicine in an integrated healthcare system. In: Ginsburg GS Willard HF, editors. Genomic and personalized medicine, 2nd edition. London: Academic Press. pp 340–352.
