Joseph Kannry

The Electronic Medical Records and Genomics (eMERGE) Network: past, present, and future

The Electronic Medical Records and Genomics Network is a National Human Genome Research Institute–funded consortium engaged in the development of methods and best practices for using the electronic medical record as a tool for genomic research. Now in its sixth year and second funding cycle, and comprising nine research groups and a coordinating center, the network has played a major role in validating the concept that clinical data derived from electronic medical records can be used successfully for genomic research. Current work is advancing knowledge in multiple disciplines at the intersection of genomics and health-care informatics, particularly for electronic phenotyping, genome-wide association studies, genomic medicine implementation, and the ethical and regulatory issues associated with genomics research and returning results to study participants. Here, we describe the evolution, accomplishments, opportunities, and challenges of the network from its inception as a five-group consortium focused on genotype–phenotype associations for genomic discovery to its current form as a nine-group consortium pivoting toward the implementation of genomic medicine.Genet Med 15 10, 761–771.Genetics in Medicine (2013); 15 10, 761–771. doi:10.1038/gim.2013.72

The CLIPMERGE PGx Program: Clinical Implementation of Personalized Medicine Through Electronic Health Records and Genomics–Pharmacogenomics

The exponential rise in genomics research over the past decade has yielded a growing number of sequence variants associated with medication response that may have clinical utility. Despite existing barriers, attention is turning to strategies that integrate these data into clinical care. The CLIPMERGE PGx Program is establishing a best‐practices infrastructure for the implementation of genome‐informed prescribing using a biobank‐derived clinical cohort, preemptive genetic testing, and real‐time clinical decision support deployed through the electronic health record.

Opportunities for genomic clinical decision support interventions

The development and availability of genomic applications for use in clinical care is accelerating rapidly. the routine use of genomic information, however, is beyond most health-care providers’ formal training, and the challenges of understanding and interpreting genomic data are compounded by the demands of clinical practice. nearly all physicians, for example, agree that genetic variations may influence drug response, but only a small fraction feel adequately informed about pharmacogenomic testing.1 Clinical decision support (CDS) embedded into clinical information systems, such as the electronic health record (EHR) and the personal health record (PHR), is recognized as being necessary to facilitate the appropriate use of genomic applications.2–4 CDS provides clinical knowledge and patient-specific information, filtered or presented at particular times to enhance clinical care.5 CDS solutions can assist clinical-care providers with personalizing care and can incorporate the preferences of health-care consumers. EHRs and PHRs theoretically may support access to and storage of genetic data. These systems may also support data exchange between repositories and enable CDS embedment and linkage. The use of EHRs and PHRs in this manner depends on characteristics of the underlying health information technology (IT) infrastructure. This article seeks to provide a common ground for discussing CDS for genetic testing and for data access processes among heterogeneous health IT infrastructures. There are many lessons learned from more than five decades of experience with CDS that can be applied to CDS implementation in the era of genomic data. Indeed, existing CDS technologies already play a role in supporting genetic testing and data access processes. In the following sections, we provide an overview of existing frameworks for local evaluation of health IT infrastructures for CDS, processes for genetic testing and data access, and the rationale behind the Electronic Medical Records and Genomics (eMERGE) Network’s6 work on establishing a common ground for discussing CDS solutions among heterogeneous IT infrastructures. We also provide examples from eMERGE to illustrate that we can characterize genomic CDS using frameworks from the pregenomic CDS era, and outline lessons learned from implementing pregenomic CDS that can account for variation in health IT infrastructure. Finally, we propose a framework to describe opportunities for genomic CDS that can support provider- and consumer-initiated genetic testing and data access processes. The work in this article is complementary to that of the Clinical Sequence Exploratory Research Electronic Records Working Group, also in this special issue.7 The Working Group’s manuscript surveys the six current Clinical Sequence Exploratory Research sites on the processes used for variant annotation, curation, report generation, and integration into the EHR, in order to determine commonalities, determine gaps, and to suggest future directions. This article takes a more top–down approach to system desiderata.

Efficacy of an Evidence-Based Clinical Decision Support in Primary Care Practices: A Randomized Clinical Trial

IMPORTANCE There is consensus that incorporating clinical decision support into electronic health records will improve quality of care, contain costs, and reduce overtreatment, but this potential has yet to be demonstrated in clinical trials. OBJECTIVE To assess the influence of a customized evidence-based clinical decision support tool on the management of respiratory tract infections and on the effectiveness of integrating evidence at the point of care. DESIGN, SETTING, AND PARTICIPANTS In a randomized clinical trial, we implemented 2 well-validated integrated clinical prediction rules, namely, the Walsh rule for streptococcal pharyngitis and the Heckerling rule for pneumonia. INTERVENTIONS AND MAIN OUTCOMES AND MEASURES: The intervention group had access to the integrated clinical prediction rule tool and chose whether to complete risk score calculators, order medications, and generate progress notes to assist with complex decision making at the point of care. RESULTS The intervention group completed the integrated clinical prediction rule tool in 57.5% of visits. Providers in the intervention group were significantly less likely to order antibiotics than the control group (age-adjusted relative risk, 0.74; 95% CI, 0.60-0.92). The absolute risk of the intervention was 9.2%, and the number needed to treat was 10.8. The intervention group was significantly less likely to order rapid streptococcal tests compared with the control group (relative risk, 0.75; 95% CI, 0.58-0.97; P= .03). CONCLUSIONS AND RELEVANCE The integrated clinical prediction rule process for integrating complex evidence-based clinical decision report tools is of relevant importance for national initiatives, such as Meaningful Use. TRIAL REGISTRATION clinicaltrials.gov Identifier: NCT01386047.

Physician Attitudes toward Adopting Genome-Guided Prescribing through Clinical Decision Support

This study assessed physician attitudes toward adopting genome-guided prescribing through clinical decision support (CDS), prior to enlisting in the Clinical Implementation of Personalized Medicine through Electronic Health Records and Genomics pilot pharmacogenomics project (CLIPMERGE PGx). We developed a survey instrument that includes the Evidence Based Practice Attitude Scale, adapted to measure attitudes toward adopting genome-informed interventions (EBPAS-GII). The survey also includes items to measure physicians’ characteristics (awareness, experience, and perceived usefulness), attitudes about personal genome testing (PGT) services, and comfort using technology. We surveyed 101 General Internal Medicine physicians from the Icahn School of Medicine at Mount Sinai (ISMMS). The majority were residency program trainees (~88%). Prior to enlisting into CLIPMERGE PGx, most physicians were aware of and had used decision support aids. Few physicians, however, were aware of and had used genome-guided prescribing. The majority of physicians viewed decision support aids and genotype data as being useful for making prescribing decisions. Most physicians had not heard of, but were willing to use, PGT services and felt comfortable interpreting PGT results. Most physicians were comfortable with technology. Physicians who perceived genotype data to be useful in making prescribing decisions, had more positive attitudes toward adopting genome-guided prescribing through CDS. Our findings suggest that internal medicine physicians have a deficit in their familiarity and comfort interpreting and using genomic information. This has reinforced the importance of gathering feedback and guidance from our enrolled physicians when designing genome-guided CDS and the importance of prioritizing genomic medicine education at our institutions.

Practical considerations in genomic decision support: The eMERGE experience

Background: Genomic medicine has the potential to improve care by tailoring treatments to the individual. There is consensus in the literature that pharmacogenomics (PGx) may be an ideal starting point for real-world implementation, due to the presence of well-characterized drug-gene interactions. Clinical Decision Support (CDS) is an ideal avenue by which to implement PGx at the bedside. Previous literature has established theoretical models for PGx CDS implementation and discussed a number of anticipated real-world challenges. However, work detailing actual PGx CDS implementation experiences has been limited. Anticipated challenges include data storage and management, system integration, physician acceptance, and more. Methods: In this study, we analyzed the experiences of ten members of the Electronic Medical Records and Genomics (eMERGE) Network, and one affiliate, in their attempts to implement PGx CDS. We examined the resulting PGx CDS system characteristics and conducted a survey to understand the unanticipated implementation challenges sites encountered. Results: Ten sites have successfully implemented at least one PGx CDS rule in the clinical setting. The majority of sites elected to create an Omic Ancillary System (OAS) to manage genetic and genomic data. All sites were able to adapt their existing CDS tools for PGx knowledge. The most common and impactful delays were not PGx-specific issues. Instead, they were general IT implementation problems, with top challenges including team coordination/communication and staffing. The challenges encountered caused a median total delay in system go-live of approximately two months. Conclusions: These results suggest that barriers to PGx CDS implementations are generally surmountable. Moreover, PGx CDS implementation may not be any more difficult than other healthcare IT projects of similar scope, as the most significant delays encountered were not unique to genomic medicine. These are encouraging results for any institution considering implementing a PGx CDS tool, and for the advancement of genomic medicine.

Uptake of hepatitis C screening, characteristics of patients tested, and intervention costs in the BEST-C study.

From December 2012 to March 2014, three randomized trials, each implementing a unique intervention in primary care settings (repeated mailing, an electronic health record best practice alert [BPA], and patient solicitation), evaluated hepatitis C virus (HCV) antibody testing, diagnosis, and costs for each of the interventions compared with standard‐of‐care testing. Multilevel multivariable models were used to estimate the adjusted risk ratio (aRR) for receiving an HCV antibody test, and costs were estimated using activity‐based costing. The goal of this study was to estimate the effects of interventions conducted as part of the Birth‐Cohort Evaluation to Advance Screening and Testing for Hepatitis C study on HCV testing and costs among persons of the 1945‐1965 birth cohort (BC). Intervention resulted in substantially higher HCV testing rates compared with standard‐of‐care testing (26.9% versus 1.4% for repeated mailing, 30.9% versus 3.6% for BPA, and 63.5% versus 2.0% for patient solicitation) and significantly higher aRR for testing after controlling for sex, birth year, race, insurance type, and median household income (19.2 [95% confidence interval (CI), 9.7–38.2] for repeated mailing, 13.2 [95% CI, 3.6–48.6] for BPA, and 32.9 [95% CI, 19.3–56.1] for patient solicitation). The BPA intervention had the lowest incremental cost per completed test (

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial.

24 with fixed startup costs,

An Electronic Health Record–based Intervention to Promote Hepatitis C Virus Testing Among Adults Born Between 1945 and 1965: A Cluster-randomized Trial

3 without) and also the lowest incremental cost per new case identified after omitting fixed startup costs (

Patient and clinician perspectives on the outpatient after-visit summary: a qualitative study to inform improvements in visit summary design

1691). Conclusion: HCV testing interventions resulted in an increase in BC testing compared with standard‐of‐care testing but also increased costs. The effect size and incremental costs of BPA intervention (excluding startup costs) support more widespread adoption compared with the other interventions. (Hepatology 2017;65:44‐53).