Electronic Health Records and Genomics
2021; Elsevier BV; Volume: 24; Issue: 1 Linguagem: Inglês
10.1016/j.jmoldx.2021.09.009
ISSN1943-7811
AutoresAlexis B. Carter, Lynne V. Abruzzo, Julie Hirschhorn, Dan Jones, Danielle Jordan, Mehdi Nassiri, Shuji Ogino, Nimesh R. Patel, Christopher G. Suciu, Robyn L. Temple-Smolkin, Ahmet Zehir, Somak Roy,
Tópico(s)Ethics in Clinical Research
ResumoThe use of genomics in medicine is expanding rapidly, but information systems are lagging in their ability to support genomic workflows both from the laboratory and patient-facing provider perspective. The complexity of genomic data, the lack of needed data standards, and lack of genomic fluency and functionality as well as several other factors have contributed to the gaps between genomic data generation, interoperability, and utilization. These gaps are posing significant challenges to laboratory and pathology professionals, clinicians, and patients in the ability to generate, communicate, consume, and use genomic test results. The Association for Molecular Pathology Electronic Health Record Working Group was convened to assess the challenges and opportunities and to recommend solutions on ways to resolve current problems associated with the display and use of genomic data in electronic health records. The use of genomics in medicine is expanding rapidly, but information systems are lagging in their ability to support genomic workflows both from the laboratory and patient-facing provider perspective. The complexity of genomic data, the lack of needed data standards, and lack of genomic fluency and functionality as well as several other factors have contributed to the gaps between genomic data generation, interoperability, and utilization. These gaps are posing significant challenges to laboratory and pathology professionals, clinicians, and patients in the ability to generate, communicate, consume, and use genomic test results. The Association for Molecular Pathology Electronic Health Record Working Group was convened to assess the challenges and opportunities and to recommend solutions on ways to resolve current problems associated with the display and use of genomic data in electronic health records. The use of genomics in clinical medicine is expanding rapidly. Although many laboratories are implementing highly complex technology to perform next-generation sequencing (NGS), the utilization of genomic data is substantially hindered by a lack of standards in analytical pipelines, data interpretations, and reporting. As genomic data become more widely available, health care providers struggle to interpret genomic results because they are complex and highly variable between laboratories in scope of testing performed and degree to which variants of unknown significance are reported. They can also be difficult to locate in electronic health records (EHRs) and can be even more difficult to compare across different tests and panels over time. Genomic testing results are most often stored and displayed in EHRs as either flat text reports or portable document format (PDF) files only and without storage of discrete result data. Both of these report formats facilitate result communication and documentation, and PDFs can support hyperlinks to additional information. However, because discrete data cannot be easily extracted from these flat reports, neither format is optimal for correlating NGS results with other data that may be critical to patient care, such as cross-checking variants between different reports on the same patient, alerts for therapy that is contraindicated on the basis of the patient's genome, or cross-referencing the variant profiles across patients. Furthermore, because individual variants are buried within a text report, aggregate variant profiles, which gather variants from different assays on the same patient onto a single view, are generally not possible without significant effort and may result in errors associated with extracting discrete variant data via natural language processing. The Association for Molecular Pathology (AMP) has long recognized the importance of informatics to the success of genomic medicine. As the use of molecular pathology and genomic testing has increased, so have the complaints regarding the significant lack of functionality that necessitates the use of paper records and/or manual transfer of data to and from instrument software, laboratory information systems (LISs), and EHRs. In late 2019, the AMP Board of Directors called for the formation of an Electronic Health Record Working Group with expertise in molecular pathology and clinical informatics to recommend solutions to the AMP Board of Directors on ways to resolve current problems associated with the display and use of genomic data in EHRs. After it was convened in early 2020, the working group performed a comprehensive examination via environmental scan of current problems and barriers to the display and use of genomic data in EHRs. Multiple problems were described by the working group members, which were subsequently categorized and independently ranked using a form of multivoting (Agency for Healthcare Research and Quality, https://digital.ahrq.gov/health-it-tools-and-resources/evaluation-resources/workflow-assessment-health-it-toolkit/all-workflow-tools/multivoting, last accessed December 8, 2020). The results of the discussion and a description of current state are included in this article. The AMP working group had a narrow scope of activities, which are reflected in this article. The working group recognizes that all components of software and systems used to generate genomic test results for patients are important and has structured the document from test ordering through data generation and result display. LISs as well as other laboratory instrument software, for example, are critical to laboratory operations because they have many other functions in addition to those in EHRs. These include, but are not limited to, housing nucleic acid quantity and quality, aliquot-specific information, reagent calculations, batch information, various pipeline output files, variant databases, and a myriad of other functions. However, the scope of this working group was to specifically identify challenges with the use of EHRs for genomic data. Both germline and somatic variant types were included in the discussions, as were all molecular pathology and genomic testing methods. These include traditional assays, such as PCR, RT-PCR, Sanger sequencing, restriction fragment length polymorphism, Southern blots, gel electrophoresis, NGS, karyotyping, in situ hybridization, fluorescence in situ hybridization, single-nucleotide polymorphism microarrays, oligonucleotide microarrays, array comparative genomic hybridization, and others. The use of these methods in various types of laboratories was also discussed with regard to challenges in EHRs, including molecular and genetic laboratories; cytogenetics laboratories; and laboratories performing fluorescence in situ hybridization, tissue typing, histocompatibility, and immunogenetics (human leukocyte antigen testing). A brief discussion of distinct requirements for reporting of genomic data for infectious organisms is also included. Figure 1 presents a graphical representation of the current overall challenges and opportunities at a high level, which this article describes in detail. The reader is advised to refer to the figure throughout this article for reference. This article is organized by describing the caveats of the working group scope, other efforts in this area, the challenges and opportunities for genomics in EHRs in order of workflow (orders to results), and interoperability (Table 1).Table 1A Summary of Challenges and OpportunitiesChallengesOpportunitiesEHRs are not yet ready to send accurate, coded, and appropriately granular clinical history, signs, symptoms, family history, and other broad sets of data elements to laboratories without generating significant burden on providers.Develop stakeholder consensus-derived and standardized methods to apply accurate concept codes to the clinical notes, signs, symptoms, and family histories as well as specimen locations, such that necessary information could be sent by the provider using an automated or semi-automated import into the electronic order for genomic testing.EHRs lack sufficient information about genetic test orders and generally do not have discrete variant result data to facilitate appropriate test ordering and utilization.Absence of standardized genomic variant data structures from the LIS to the EHR.Establish a consensus standard for minimum discrete data required to define a genomic variant in an EHR.∗Areas where the Association for Molecular Pathology (AMP) is actively engaged with subject matter expert working groups addressing identified challenges and opportunities. Future recommendations, along with accompanying educational offerings, are being developed by the EHR Working Group and other AMP working groups to address these community needs.Establish a consensus standard for minimum discrete data needed to define a genetic/genomic test order.Interoperability standards for genomics are currently limited in several ways, including the use of syntax with limited hierarchy (HL7 version 2.x) and inadequate coding systems for genomic orders and results.Before mandating changes to interoperability requirements, governments and regulators should carefully review the cost and burden to laboratories as well as the safety of existing coding standards that are currently inadequate for genomic data. Such work should occur after establishment of a consensus standard for minimum discrete data required to define a genomic variant.Genomic reports between laboratories are variable in structure and content.Develop recommendations for content and structure of genomic reports that support providing the established consensus standard for minimum discrete data required to define a genomic variant in an EHR.∗Areas where the Association for Molecular Pathology (AMP) is actively engaged with subject matter expert working groups addressing identified challenges and opportunities. Future recommendations, along with accompanying educational offerings, are being developed by the EHR Working Group and other AMP working groups to address these community needs.Implementation of molecular pathology and genomics professional societies multi-organizational consensus standards and guidelines for variant nomenclature, hierarchical result structure, and genomic report formats has been modest.Encourage laboratories and EHRs to support and implement molecular pathology and genomics professional societies multi-organizational consensus standards and guidelines for report content and structure to standardize structure and content between laboratories.∗Areas where the Association for Molecular Pathology (AMP) is actively engaged with subject matter expert working groups addressing identified challenges and opportunities. Future recommendations, along with accompanying educational offerings, are being developed by the EHR Working Group and other AMP working groups to address these community needs.Professional organizations should consider further developing consensus report structures that use sound principles of human factors engineering and usability.There are no standards for how to display aggregated variant data over time and between sample sources, tests, and laboratories that keep clinical context and associated interpretation intact.Standards for aggregation of genomic data over time and between sample sources, tests, and laboratories should be developed that keep clinical context and associated interpretation intact.Consensus guidelines on best practice for requesting and providing reclassification of variants is not currently available.Establish consensus guidelines on best practice for requesting and providing reclassification of variants.∗Areas where the Association for Molecular Pathology (AMP) is actively engaged with subject matter expert working groups addressing identified challenges and opportunities. Future recommendations, along with accompanying educational offerings, are being developed by the EHR Working Group and other AMP working groups to address these community needs.Genomic reports are difficult for medical personnel and patients to understand, and in the United States, patients have the right to immediately access their genomic test reports on request.Professional organizations should consider further developing consensus report structures that use sound principles of human factors engineering and usability that enable understanding by most patients.Absence of national and international standards for CDS rules.Establish evidence-based international recommendations for CDS in genomic test orders and in drug orders impacted by genomic results for safety and consistency in practice with a prerequisite minimum discrete genomic variant data consensus standard established.Currently, textual data from genomic reports are exported and released to authorized parties, and analysis of such data is limited by its highly variable format and lack of structure.Establish and integrate appropriate, safe, and functional international standards for interoperability and data retrieval.Current technology lacks sufficient functionality to ensure that release of genomic data to appropriate health care organizations, research studies, or clinical repositories requires and receives informed consent from the patient or legal guardian, and/or institutional board review.Develop technology and cybersecurity functions in EHRs that ensure that genomic data are released as authorized after informed consent from the patient along with tools to educate patients about informed consent.Current coding and interoperability standards are not adequate for genomic data.Develop safe, complete, and accurate standards for coding and interoperability of genomic data, per the future established minimum discrete variant data standard.CDS, clinical decision support; EHR, electronic health record; HL7, Health Level Seven; LIS, laboratory information system.∗ Areas where the Association for Molecular Pathology (AMP) is actively engaged with subject matter expert working groups addressing identified challenges and opportunities. Future recommendations, along with accompanying educational offerings, are being developed by the EHR Working Group and other AMP working groups to address these community needs. Open table in a new tab CDS, clinical decision support; EHR, electronic health record; HL7, Health Level Seven; LIS, laboratory information system. This AMP working group recognizes that no EHR has all of the capabilities desired by either this working group or others. The purpose of this document is to provide a framework for future work by this working group and others for guidelines and standards to help improve EHRs in the future. It is important to start by recognizing and briefly describing other efforts in the area of enabling or facilitating the use of genomic data in EHRs, some of which are long-standing, and which have attempted to tackle the difficulties of accurate and standardized transfers and displays of complex data, including genomics, in the EHR. A collaborative effort on the standard representation and integration of genomics into EHRs was initiated as part of the Roundtable on Genomics and Precision Health at National Academies of Sciences, Engineering, and Medicine during 2013 to 2014 (Institute of Medicine of the National Academies, https://www.nationalacademies.org/our-work/roundtable-on-genomics-and-precision-health, last accessed June 4, 2021). This initiative, "Displaying and Integrating Genetic Information through the EHR" (DIGITizE), transitioned from the National Academies to the Health Level Seven (HL7) Fast Healthcare Interoperability Resources (FHIR) Foundation to continue its mission. The DIGITizE initiative produced a limited number of documents and models regarding pharmacogenomics, laboratory connectivity, and clinical decision support (CDS; National Academies of Sciences Engineering Medicine, https://www.nationalacademies.org/our-work/digitize-displaying-and-integrating-genetic-information-through-the-ehr-action-collaborative, last accessed October 2, 2020). The Electronic Medical Records and Genomics (eMERGE) Network, initiated in 2007, is a NIH-organized and funded consortium of US medical research institutions that combines DNA biorepositories with EHRs for large-scale, high-throughput genetic research in support of implementing genomic medicine1Wiesner G.L. Rahm A.K. Appelbaum P. Aufox S. Bland S.T. Blout C.L. Christensen K.D. Chung W.K. Clayton E.W. Green R.C. Harr M.H. Henrikson N. Hoell C. Holm I.A. Jarvik G.P. Kullo I.J. Lammers P.E. Larson E.B. Lindor N.M. Marasa M. Myers M.F. Peterson J.F. Prows C.A. Ralston J.D. Rasouly H.M. Sharp R.R. Smith M.E. Van Driest S.L. Williams J.L. Williams M.S. Wynn J. Leppig K.A. Returning results in the genomic era: initial experiences of the emerge network.J Pers Med. 2020; 10: 30Crossref PubMed Scopus (17) Google Scholar (National Human Genome Research Institute, https://www.genome.gov/Funded-Programs-Projects/Electronic-Medical-Records-and-Genomics-Network-eMERGE, last accessed October 2, 2020). The consortium brings together investigators with a wide range of expertise in genomics, statistics, ethics, informatics, and clinical medicine from leading medical research institutions across the country. One of the major goals of the eMERGE network is the development of methods and best practices for integration of genetic clinical testing results and other genomics information with clinical data in the EHR, enabling clinical decision support systems, and improving accessibility and usability by clinicians for optimizing patient care. During phase I to III implementation, the eMERGE network included 68 electronic phenotype algorithms and >100,000 participants with genomic data deployed across nine study centers and two genotyping facilities. Returning genetic clinical results has been implemented across the network, including data related to pharmacogenomics and genes associated with breast and colorectal cancers. A recent report summarizing the network's experience on return of genetic results to the EHR at the participating sites noted heterogeneity and real-world problems associated with returning genetic testing results. The report emphasized the need for developing new mechanisms to support return of genetics results and focusing on required elements to achieve that goal2Gottesman O. Kuivaniemi H. Tromp G. Faucett W.A. Li R. Manolio T.A. Sanderson S.C. Kannry J. Zinberg R. Basford M.A. Brilliant M. Carey D.J. Chisholm R.L. Chute C.G. Connolly J.J. Crosslin D. Denny J.C. Gallego C.J. Haines J.L. Hakonarson H. Harley J. Jarvik G.P. Kohane I. Kullo I.J. Larson E.B. McCarty C. Ritchie M.D. Roden D.M. Smith M.E. Böttinger E.P. Williams M.S. The Electronic Medical Records and Genomics (eMERGE) network: past, present, and future.Genet Med. 2013; 15: 761-771Abstract Full Text Full Text PDF PubMed Scopus (411) Google Scholar (eMERGE Network, https://emerge-network.org/about-emerge; National Human Genome Research Institute, https://www.genome.gov/Funded-Programs-Projects/Electronic-Medical-Records-and-Genomics-Network-eMERGE, both last accessed October 2, 2020). More recently, Sync for Genes is described as a technology-based effort to gather health data from individuals in the United States and make that information available to researchers in a standardized format. One of the tools cited for use by this effort is the HL7's FHIR as a method to standardize the health data, including genomic data (The Office of the National Coordinator for Health Information Technology, https://www.healthit.gov/sites/default/files/sync_for_genes_report_november_2017.pdf, last accessed October 2, 2020). The American College of Medical Genetics and Genomics has issued a points to consider statement on genomic information within the EHR,3Grebe T.A. Khushf G. Chen M. Bailey D. Brenman L.M. Williams M.S. Seaver L.H. The interface of genomic information with the electronic health record: a points to consider statement of the American College of Medical Genetics and Genomics (ACMG).Genet Med. 2020; 22: 1431-1436Abstract Full Text Full Text PDF PubMed Scopus (16) Google Scholar defining the scope of genomic data and raising important points regarding access to data and social justice. They emphasize that genomic data should be looked at more broadly, as they include interpretation of testing results documented in clinical notes, excerpting of results into different areas of the medical records, and possibly direct-to-consumer testing results that end up in the EHR. In addition, it is important for patients to have access to genomic data in a Health Insurance Portability and Accountability Act–compliant manner, with the ability to continue to receive results as they are updated and to transfer those results to other health care facilities (optimally achieved electronically through EHRs with the utilization of standards). Finally, because of unique features (such as the predictive nature of results for later risks, impact on family members, changing societal perspectives, and the dynamic nature of knowledge that allows for reinterpretation of previously reported genomic variants), providers, EHR vendors, and health information exchanges should develop mechanisms to protect sensitive genomic information in the EHR.4Kannry J.M. Williams M.S. Integration of genomics into the electronic health record: mapping terra incognita.Genet Med. 2013; 15: 757-760Abstract Full Text Full Text PDF PubMed Scopus (25) Google Scholar,5Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.Genome Med. 2016; 8: 113Crossref PubMed Scopus (42) Google Scholar Several medical institutions have made efforts to work with EHR vendors and laboratories to operationalize some of the American College of Medical Genetics and Genomics guidelines5Warner J.L. Jain S.K. Levy M.A. Integrating cancer genomic data into electronic health records.Genome Med. 2016; 8: 113Crossref PubMed Scopus (42) Google Scholar,6Lau-Min K.S. Asher S.B. Chen J. Domchek S.M. Feldman M. Joffe S. Landgraf J. Speare V. Varughese L.A. Tuteja S. VanZandbergen C. Ritchie M.D. Nathanson K.L. Real-world integration of genomic data into the electronic health record: the PennChart Genomics Initiative.Genet Med. 2021; 23: 603-605Abstract Full Text Full Text PDF PubMed Scopus (4) Google Scholar and address some of the challenges addressed in this document. The Clinical Genome Resource (ClinGen) has an EHR Working Group that aims to ensure that the ClinGen resource is designed to be accessible to providers and patients through EHRs and related systems7Heale B.S.E. Overby C.L. Del Fiol G. Rubinstein W.S. Maglott D.R. Nelson T.H. Milosavljevic A. Martin C.L. Goehringer S.R. Freimuth R.R. Williams M.S. Integrating genomic resources with electronic health records using the HL7 infobutton standard.Appl Clin Inform. 2016; 7: 817-831Crossref PubMed Scopus (11) Google Scholar,8Overby C.L. Heale B. Aronson S. Cherry J.M. Dwight S. Milosavljevic A. Nelson T. Niehaus A. Weaver M.A. Ramos E.M. Williams M.S. Providing access to genomic variant knowledge in a healthcare setting: a vision for the ClinGen Electronic Health Records Workgroup.Clin Pharmacol Ther. 2016; 99: 157-160Crossref PubMed Scopus (9) Google Scholar (ClinGen-EHR Working Group, https://www.clinicalgenome.org/working-groups/ehr, last accessed November 9, 2020). In 2018, ClinGen was the first entity recognized by the US Food and Drug Administration (FDA) as part of its public human genetic variant database program (ClinGen-FDA Recognized Human Variant Database, https://www.clinicalgenome.org/about/fda-recognition; Recognition of ClinGen Expert Curated Human Variant Data Decision Letter, https://www.clinicalgenome.org/site/assets/files/3978/approval_q181150_letter_jpnd_final.pdf, both last accessed December 28, 2020) for variant curation of germline variants for hereditary disease, where there is a high likelihood that the disease or condition will materialize given a deleterious variant (ie, high penetrance). Although such recognition does not constitute marketing clearance or approval, data from FDA-recognized databases would generally constitute valid scientific evidence that can be used to support the clinical validity of genotype-phenotype relationships that may be used as assertions in a premarket submission. Variants curated by expert panels according to the FDA recognition letter are marked as reviewed by expert panel and FDA-recognized database. There are numerous other projects for improving the overall usability of genomic data, which are too numerous to describe and which have not been specifically referenced herein because they are not specific to the usability of genomic data in EHRs. In comparison to those efforts described above that have specifically addressed challenges and opportunities of genomics in EHRs, this article presents a more in-depth view of the technical challenges for each portion of the workflow, and high-level summaries are presented at the end of each section for reference. Laboratories often receive electronic laboratory orders from EHRs, particularly when the laboratory is internal to the health care organization that houses the EHR or when they have an HL7 interface with the EHR. However, orders for genetic testing are often devoid of important clinical history, specimen type, and diagnosis information relevant to the requested test. Unlike other laboratory tests, the amount and breadth of information that may be possible to include on even one genetic test can be staggering. Data elements, such as the patient's observed phenotype (affected versus not affected), status of the specimen submitted (germline unaffected tissue versus tumor versus somatic overgrowth region), and tumor status (primary tumor versus metastasis), are easily added to orders now because the build is relatively small and easy to execute. However, suspected disease condition, clinical history, clinical features, family history, tumor type, and specimen source location would each have to have large numbers of options available. This makes build in most EHRs difficult if not impossible, and it generates unacceptable burden on physicians to manually fill out these forms. Automated entry of the required data elements is a future and lofty goal, but currently this is unattainable in most EHRs because these data elements are not captured in a coded and structured manner in the patient's clinical notes in most if not all EHRs. Problem lists are generally unhelpful because they typically only contain confirmed high-level diagnoses and may also be out-of-date. Some reference laboratories have extensive electronic forms for data entry of clinical history available on their websites for exomes and other tests. However, significant time is required to fill out these electronic forms, and some also lack comprehensive coverage of the concepts needed for the patient. There may be inadequate search functionality, and there may be no place to free text in necessary clinical history when it cannot be found in the form. Compared with simply sending a copy of the latest clinical note and/or pedigree on the patient with the specimen and requisition or filling out a simple free-text field on clinical history in the electronic order, it is not surprising that genomic orders in EHRs have not generally been built in this manner. From a data analysis and aggregation perspective, it would certainly be preferable for laboratories to receive this information in a structured and coded manner, but this will not be possible until EHRs are better able to capture accurate and granular data on symptoms and course and until coding systems are able to comprehensively include this information in an unambiguous and accurate manner. Coding systems, such as Logical Observation Identifiers Names and Codes (LOINC, https://loinc.org, last accessed January 21, 2021) and Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT, https://www.snomed.org, last accessed January 21, 2021), are challenging to use, especially in the United States. For example, it is not certain whether LOINC or SNOMED should be used for clinical history information from a regulatory perspective. LOINC has foundational deficits from a data aggregation perspective because of its lack of structural hierarchy of concepts. It also lacks sufficient granularity to reliably distinguish between different tests that are not semantically interoperable. Summary: EHRs are not yet ready to send accurate, coded, and appropriately granular clinical history, signs, symptoms, family history, and other broad sets of data elements to laboratories without generating significant burden on providers. Ordering genomic tests in EHRs poses challenges that, if not properly managed, can lead to unnecessary or redundant testing as well as delays in performing the correct test with subsequent delays in diagnosis and therapeutic intervention. Concerns over genomic test utilization have been driven, in part, by the high cost of these assays, leading many health care organizations to devote considerable time and effort on appropriate test use and strategies for order review as cost-saving measures.9Riley J.D. Procop G.W. Kottke-Marchant K. Wyllie R. Lacbawan F.L. Improving molecular genetic test utilization through order restriction, test review, and guidance.J Mol Diagn. 2015; 17: 225-229Abstract Full Text Full Text PDF PubMed Scopus (32) Google Scholar, 10Desai K. Hooker G. Adeboyeje G. Kachroo S. Sen S.S. HSR20-083: real-world utilization and coding variability in medical claims for next-generation sequencing (NGS)-based diagnostic tests among cancer patients in the U.S..JNCCN. 2020; 18: HSR20-083Google Scholar, 11Hsiao S.J. Sireci A. Pendrick D. Freeman C. Yang J. Schwartz G.K. Mansukhani M.M. Carvajal R.D. Oberg J.A. Clinical utility and reimbursement
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