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Carter AB, Abruzzo LV, Hirschhorn JW, Jones D, Jordan DC, Nassiri M, Ogino S, Patel NR, Suciu CG, Temple-Smolkin RL, Zehir A, Roy S. Electronic Health Records and Genomics: Perspectives from the Association for Molecular Pathology Electronic Health Record (EHR) Interoperability for Clinical Genomics Data Working Group. J Mol Diagn 2021; 24:1-17. [PMID: 34656760 DOI: 10.1016/j.jmoldx.2021.09.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 09/14/2021] [Accepted: 09/28/2021] [Indexed: 02/09/2023] Open
Abstract
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.
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Affiliation(s)
- Alexis B Carter
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Children's Healthcare of Atlanta, Atlanta, Georgia.
| | - Lynne V Abruzzo
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Wexner Medical Center, The Ohio State University, Columbus, Ohio
| | - Julie W Hirschhorn
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Medical University of South Carolina, Charleston, South Carolina
| | - Dan Jones
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; The Ohio State University Comprehensive Cancer Center, James Cancer Hospital and Solove Research Institute, Columbus, Ohio
| | | | - Mehdi Nassiri
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Laboratory Medicine, Indiana University School of Medicine, Indianapolis, Indiana
| | - Shuji Ogino
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Brigham & Women's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Harvard T.H. Chan School of Public Health, Boston, Massachusetts; Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, Massachusetts
| | - Nimesh R Patel
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Rhode Island Hospital and Alpert Medical School of Brown University, Providence, Rhode Island
| | - Christopher G Suciu
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, Missouri; Institute for Informatics, Washington University School of Medicine, St. Louis, Missouri
| | | | - Ahmet Zehir
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Memorial Sloan Kettering Cancer Center, New York, New York
| | - Somak Roy
- The Electronic Health Record Interoperability for Clinical Genomics Data Working Group of the Informatics Subdivision, Association for Molecular Pathology, Rockville, Maryland; Department of Pathology, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio
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Rasmussen LV, Connolly JJ, Del Fiol G, Freimuth RR, Pet DB, Peterson JF, Shirts BH, Starren JB, Williams MS, Walton N, Taylor CO. Infobuttons for Genomic Medicine: Requirements and Barriers. Appl Clin Inform 2021; 12:383-390. [PMID: 33979874 DOI: 10.1055/s-0041-1729164] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022] Open
Abstract
OBJECTIVES The study aimed to understand potential barriers to the adoption of health information technology projects that are released as free and open source software (FOSS). METHODS We conducted a survey of research consortia participants engaged in genomic medicine implementation to assess perceived institutional barriers to the adoption of three systems: ClinGen electronic health record (EHR) Toolkit, DocUBuild, and MyResults.org. The survey included eight barriers from the Consolidated Framework for Implementation Research (CFIR), with additional barriers identified from a qualitative analysis of open-ended responses. RESULTS We analyzed responses from 24 research consortia participants from 18 institutions. In total, 14 categories of perceived barriers were evaluated, which were consistent with other observed barriers to FOSS adoption. The most frequent perceived barriers included lack of adaptability of the system, lack of institutional priority to implement, lack of trialability, lack of advantage of alternative systems, and complexity. CONCLUSION In addition to understanding potential barriers, we recommend some strategies to address them (where possible), including considerations for genomic medicine. Overall, FOSS developers need to ensure systems are easy to trial and implement and need to clearly articulate benefits of their systems, especially when alternatives exist. Institutional champions will remain a critical component to prioritizing genomic medicine projects.
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Affiliation(s)
- Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United Sates
| | - John J Connolly
- The Center for Applied Genomics, The Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, United Sates
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah, United Sates
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, United Sates
| | - Douglas B Pet
- Department of Neurology, University of California San Francisco, San Francisco, California, United Sates
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University School of Medicine, Nashville, Tennessee, United Sates
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, Washington, United Sates
| | - Justin B Starren
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United Sates
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United Sates
| | - Nephi Walton
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United Sates.,Intermountain Precision Genomics, Intermountain Healthcare, St George, Utah, United Sates
| | - Casey Overby Taylor
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, United Sates.,Department of Medicine and Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, Maryland, United Sates
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Kim HJ, Kim HJ, Park Y, Lee WS, Lim Y, Kim JH. Clinical Genome Data Model (cGDM) provides Interactive Clinical Decision Support for Precision Medicine. Sci Rep 2020; 10:1414. [PMID: 31996707 PMCID: PMC6989462 DOI: 10.1038/s41598-020-58088-2] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2019] [Accepted: 01/09/2020] [Indexed: 02/02/2023] Open
Abstract
In light of recent developments in genomic technology and the rapid accumulation of genomic information, a major transition toward precision medicine is anticipated. However, the clinical applications of genomic information remain limited. This lag can be attributed to several complex factors, including the knowledge gap between medical experts and bioinformaticians, the distance between bioinformatics workflows and clinical practice, and the unique characteristics of genomic data, which can make interpretation difficult. Here we present a novel genomic data model that allows for more interactive support in clinical decision-making. Informational modelling was used as a basis to design a communication scheme between sophisticated bioinformatics predictions and the representative data relevant to a clinical decision. This study was conducted by a multidisciplinary working group who carried out clinico-genomic workflow analysis and attribute extraction, through Failure Mode and Effects Analysis (FMEA). Based on those results, a clinical genome data model (cGDM) was developed with 8 entities and 46 attributes. The cGDM integrates reliability-related factors that enable clinicians to access the reliability problem of each individual genetic test result as clinical evidence. The proposed cGDM provides a data-layer infrastructure supporting the intellectual interplay between medical experts and informed decision-making.
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Affiliation(s)
- Hyo Jung Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Hyeong Joon Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Yoomi Park
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Woo Seung Lee
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Younggyun Lim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Ju Han Kim
- Seoul National University Biomedical Informatics (SNUBI), Division of Biomedical Informatics, Seoul National University College of Medicine, Seoul, Republic of Korea.
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Williams MS, Taylor CO, Walton NA, Goehringer SR, Aronson S, Freimuth RR, Rasmussen LV, Hall ES, Prows CA, Chung WK, Fedotov A, Nestor J, Weng C, Rowley RK, Wiesner GL, Jarvik GP, Del Fiol G. Genomic Information for Clinicians in the Electronic Health Record: Lessons Learned From the Clinical Genome Resource Project and the Electronic Medical Records and Genomics Network. Front Genet 2019; 10:1059. [PMID: 31737042 PMCID: PMC6830110 DOI: 10.3389/fgene.2019.01059] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2019] [Accepted: 10/03/2019] [Indexed: 01/05/2023] Open
Abstract
Genomic knowledge is being translated into clinical care. To fully realize the value, it is critical to place credible information in the hands of clinicians in time to support clinical decision making. The electronic health record is an essential component of clinician workflow. Utilizing the electronic health record to present information to support the use of genomic medicine in clinical care to improve outcomes represents a tremendous opportunity. However, there are numerous barriers that prevent the effective use of the electronic health record for this purpose. The electronic health record working groups of the Electronic Medical Records and Genomics (eMERGE) Network and the Clinical Genome Resource (ClinGen) project, along with other groups, have been defining these barriers, to allow the development of solutions that can be tested using implementation pilots. In this paper, we present “lessons learned” from these efforts to inform future efforts leading to the development of effective and sustainable solutions that will support the realization of genomic medicine.
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Affiliation(s)
- Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
| | - Casey Overby Taylor
- Genomic Medicine Institute, Geisinger, Danville, PA, United States.,Department of Medicine, Johns Hopkins University, Baltimore, MD, United States
| | - Nephi A Walton
- Genomic Medicine Institute, Geisinger, Danville, PA, United States
| | | | | | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, United States
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University, Chicago, IL, United States
| | - Eric S Hall
- Department of Pediatrics, University of Cincinnati College of Medicine, and Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Cynthia A Prows
- Divisions of Human Genetics and Patient Services, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, United States
| | - Wendy K Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, United States
| | - Alexander Fedotov
- Irving Institute for Clinical and Translational Research, Columbia University, New York, NY, United States
| | - Jordan Nestor
- Department of Medicine, Division of Nephrology, Columbia University, New York, NY, United States
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, NY, United States
| | - Robb K Rowley
- National Human Genome Research Institute, Bethesda, MD, United States
| | - Georgia L Wiesner
- Division of Genetic Medicine, Department of Internal Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Gail P Jarvik
- Departments of Medicine (Medical Genetics) and Genome Sciences, University of Washington, Seattle, WA, United States
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, United States
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OpenEHR modeling for genomics in clinical practice. Int J Med Inform 2018; 120:147-156. [PMID: 30409340 DOI: 10.1016/j.ijmedinf.2018.10.007] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2017] [Revised: 04/18/2018] [Accepted: 10/15/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE The increasing usage of high throughput sequencing in personalized medicine brings new challenges to the realm of healthcare informatics. Patient records need to accommodate data of unprecedented size and complexity as well as keep track of their production process. In this work we present a solution for integrating genomic data into electronic health records via openEHR archetypes. METHODS We use the popular Variant Call Format as the base format to represent genetic test results within openEHR. We evaluate existing openEHR archetypes to determine what can be extended or specialized and what needs to be developed ex novo. RESULTS Eleven new archetypes have been developed, while an existing one has been specialized to represent genomic data. We show their applicability to rare genetic diseases and compare our approach to HL7 FHIR. CONCLUSION The proposed model allows to represent genetic test results in health records in a structured format. It supports different levels of abstraction, allowing both automated processing and clinical decision support. It is extensible via external references, allowing to keep track of data provenance and adapt to future domain changes.
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Shankar P, Anderson N. Advances in Sharing Multi-sourced Health Data on Decision Support Science 2016-2017. Yearb Med Inform 2018; 27:16-24. [PMID: 30157504 PMCID: PMC6115214 DOI: 10.1055/s-0038-1641215] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
INTRODUCTION Clinical decision support science is expanding to include integration from broader and more varied data sources, diverse platforms and delivery modalities, and is responding to emerging regulatory guidelines and increased interest from industry. OBJECTIVE Evaluate key advances and challenges of accessing, sharing, and managing data from multiple sources for development and implementation of Clinical Decision Support (CDS) systems in 2016-2017. METHODS Assessment of literature and scientific conference proceedings, current and pending policy development, and review of commercial applications nationally and internationally. RESULTS CDS research is approaching multiple landmark points driven by commercialization interests, emerging regulatory policy, and increased public awareness. However, the availability of patient-related "Big Data" sources from genomics and mobile health, expanded privacy considerations, applications of service-based computational techniques and tools, the emergence of "app" ecosystems, and evolving patient-centric approaches reflect the distributed, complex, and uneven maturity of the CDS landscape. Nonetheless, the field of CDS is yet to mature. The lack of standards and CDS-specific policies from regulatory bodies that address the privacy and safety concerns of data and knowledge sharing to support CDS development may continue to slow down the broad CDS adoption within and across institutions. CONCLUSION Partnerships with Electronic Health Record and commercial CDS vendors, policy makers, standards development agencies, clinicians, and patients are needed to see CDS deployed in the evolving learning health system.
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Affiliation(s)
- Prabhu Shankar
- Division of Health Informatics, Department of Public Health Sciences, School of Medicine, University of California, Davis, CA, USA
| | - Nick Anderson
- Division of Health Informatics, Department of Public Health Sciences, School of Medicine, University of California, Davis, CA, USA
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Williams MS, Kern MS, Lerch VR, Billet J, Williams JL, Moore GJ. Implementation of a patient-facing genomic test report in the electronic health record using a web-application interface. BMC Med Inform Decis Mak 2018; 18:32. [PMID: 29843696 PMCID: PMC5975475 DOI: 10.1186/s12911-018-0614-x] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2017] [Accepted: 05/22/2018] [Indexed: 11/30/2022] Open
Abstract
Background Genomic medicine is emerging into clinical care. Communication of genetic laboratory results to patients and providers is hampered by the complex technical nature of the laboratory reports. This can lead to confusion and misinterpretation of the results resulting in inappropriate care. Patients usually do not receive a copy of the report leading to further opportunities for miscommunication. To address these problems, interpretive reports were created using input from the intended end users, patients and providers. This paper describes the technical development and deployment of the first patient-facing genomic test report (PGR) within an electronic health record (EHR) ecosystem using a locally developed standards-based web-application interface. Methods A patient-facing genomic test report with a companion provider report was configured for implementation within the EHR using a locally developed software platform, COMPASS™. COMPASS™ is designed to manage secure data exchange, as well as patient and provider access to patient reported data capture and clinical display tools. COMPASS™ is built using a Software as a Service (SaaS) approach which exposes an API that apps can interact with. Results An authoring tool was developed that allowed creation of patient-specific PGRs and the accompanying provider reports. These were converted to a format that allowed them to be presented in the patient portal and EHR respectively using the existing COMPASS™ interface thus allowing patients, caregivers and providers access to individual reports designed for the intended end user. Conclusions The PGR as developed was shown to enhance patient and provider communication around genomic results. It is built on current standards but is designed to support integration with other tools and be compatible with emerging opportunities such as SMART on FHIR. This approach could be used to support genomic return of results as the tool is scalable and generalizable.
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Affiliation(s)
- Marc S Williams
- Genomic Medicine Institute, Geisinger, 100 North Academy Avenue, Danville, PA, USA.
| | - Melissa S Kern
- Center for Pharmacy Innovation and Outcomes, Geisinger, Danville, PA, USA
| | - Virginia R Lerch
- Institute for Advanced Application, Geisinger, Danville, PA, USA
| | - Jonathan Billet
- Institute for Advanced Application, Geisinger, Danville, PA, USA
| | - Janet L Williams
- Genomic Medicine Institute, Geisinger, 100 North Academy Avenue, Danville, PA, USA
| | - Gregory J Moore
- Institute for Advanced Application, Geisinger, Danville, PA, USA
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Crump JK, Del Fiol G, Williams MS, Freimuth RR. Prototype of a Standards-Based EHR and Genetic Test Reporting Tool Coupled with HL7-Compliant Infobuttons. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2018; 2017:330-339. [PMID: 29888091 PMCID: PMC5961781] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
Integration of genetic information is becoming increasingly important in clinical practice. However, genetic information is often ambiguous and difficult to understand, and clinicians have reported low-self-efficacy in integrating genetics into their care routine. The Health Level Seven (HL7) Infobutton standard helps to integrate online knowledge resources within Electronic Health Records (EHRs) and is required for EHR certification in the US. We implemented a prototype of a standards-based genetic reporting application coupled with infobuttons leveraging the Infobutton and Fast Healthcare Interoperability Resources (FHIR) Standards. Infobutton capabilities were provided by Open Infobutton, an open source package compliant with the HL7 Infobutton Standard. The resulting prototype demonstrates how standards-based reporting of genetic results, coupled with curated knowledge resources, can provide dynamic access to clinical knowledge on demand at the point of care. The proposed functionality can be enabled within any EHR system that has been certified through the US Meaningful Use program.
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Affiliation(s)
- Jacob K Crump
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, Utah
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota
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Jenders RA. Advances in Clinical Decision Support: Highlights of Practice and the Literature 2015-2016. Yearb Med Inform 2017; 26:125-132. [PMID: 29063552 PMCID: PMC6239223 DOI: 10.15265/iy-2017-012] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/30/2022] Open
Abstract
Introduction: Advances in clinical decision support (CDS) continue to evolve to support the goals of clinicians, policymakers, patients and professional organizations to improve clinical practice, patient safety, and the quality of care. Objectives: Identify key thematic areas or foci in research and practice involving clinical decision support during the 2015-2016 time period. Methods: Thematic analysis consistent with a grounded theory approach was applied in a targeted review of journal publications, the proceedings of key scientific conferences as well as activities in standards development organizations in order to identify the key themes underlying work related to CDS. Results: Ten key thematic areas were identified, including: 1) an emphasis on knowledge representation, with a focus on clinical practice guidelines; 2) various aspects of precision medicine, including the use of sensor and genomic data as well as big data; 3) efforts in quality improvement; 4) innovative uses of computer-based provider order entry (CPOE) systems, including relevant data displays; 5) expansion of CDS in various clinical settings; 6) patient-directed CDS; 7) understanding the potential negative impact of CDS; 8) obtaining structured data to drive CDS interventions; 9) the use of diagnostic decision support; and 10) the development and use of standards for CDS. Conclusions: Active research and practice in 2015-2016 continue to underscore the importance and broad utility of CDS for effecting change and improving the quality and outcome of clinical care.
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Affiliation(s)
- R. A. Jenders
- Center for Biomedical Informatics and Department of Medicine, Charles Drew University, Los Angeles, California, USA
- Clinical and Translational Science Institute and Department of Medicine, University of California, Los Angeles, California, USA
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Rasmussen LV, Overby CL, Connolly J, Chute CG, Denny JC, Freimuth R, Hartzler AL, Holm IA, Manzi S, Pathak J, Peissig PL, Smith M, Williams MS, Shirts BH, Stoffel EM, Tarczy-Hornoch P, Rohrer Vitek CR, Wolf WA, Starren J. Practical considerations for implementing genomic information resources. Experiences from eMERGE and CSER. Appl Clin Inform 2016; 7:870-82. [PMID: 27652374 DOI: 10.4338/aci-2016-04-ra-0060] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2016] [Accepted: 08/12/2016] [Indexed: 12/11/2022] Open
Abstract
OBJECTIVES To understand opinions and perceptions on the state of information resources specifically targeted to genomics, and approaches to delivery in clinical practice. METHODS We conducted a survey of genomic content use and its clinical delivery from representatives across eight institutions in the electronic Medical Records and Genomics (eMERGE) network and two institutions in the Clinical Sequencing Exploratory Research (CSER) consortium in 2014. RESULTS Eleven responses representing distinct projects across ten sites showed heterogeneity in how content is being delivered, with provider-facing content primarily delivered via the electronic health record (EHR) (n=10), and paper/pamphlets as the leading mode for patient-facing content (n=9). There was general agreement (91%) that new content is needed for patients and providers specific to genomics, and that while aspects of this content could be shared across institutions there remain site-specific needs (73% in agreement). CONCLUSION This work identifies a need for the improved access to and expansion of information resources to support genomic medicine, and opportunities for content developers and EHR vendors to partner with institutions to develop needed resources, and streamline their use - such as a central content site in multiple modalities while implementing approaches to allow for site-specific customization.
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Affiliation(s)
- Luke V Rasmussen
- Luke Rasmussen, Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, 750 North Lake Shore Drive, 11th Floor, Rubloff Building, Chicago, IL 60611, Phone: 312-503-2823
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