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Brlek P, Bulić L, Bračić M, Projić P, Škaro V, Shah N, Shah P, Primorac D. Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives. Cells 2024; 13:504. [PMID: 38534348 DOI: 10.3390/cells13060504] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2024] [Revised: 03/04/2024] [Accepted: 03/11/2024] [Indexed: 03/28/2024] Open
Abstract
The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.
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Affiliation(s)
- Petar Brlek
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
| | - Luka Bulić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Matea Bračić
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
| | - Petar Projić
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
| | | | - Nidhi Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Parth Shah
- Dartmouth Hitchcock Medical Center, Lebannon, NH 03766, USA
| | - Dragan Primorac
- St. Catherine Specialty Hospital, 10000 Zagreb, Croatia
- International Center for Applied Biological Research, 10000 Zagreb, Croatia
- School of Medicine, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Split, 21000 Split, Croatia
- Eberly College of Science, The Pennsylvania State University, State College, PA 16802, USA
- The Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT 06516, USA
- REGIOMED Kliniken, 96450 Coburg, Germany
- Medical School, University of Rijeka, 51000 Rijeka, Croatia
- Faculty of Dental Medicine and Health, Josip Juraj Strossmayer University of Osijek, 31000 Osijek, Croatia
- Medical School, University of Mostar, 88000 Mostar, Bosnia and Herzegovina
- National Forensic Sciences University, Gujarat 382007, India
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Dolin RH, Shenvi E, Alvarez C, Barrows RC, Boxwala A, Lee B, Nathanson BH, Kleyner Y, Hagemann R, Hongsermeier T, Kapusnik-Uner J, Lakdawala A, Shalaby J. PillHarmonics: An Orchestrated Pharmacogenetics Medication Clinical Decision Support Service. Appl Clin Inform 2024; 15:378-387. [PMID: 38388174 PMCID: PMC11098593 DOI: 10.1055/a-2274-6763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/07/2024] [Indexed: 02/24/2024] Open
Abstract
OBJECTIVES Pharmacogenetics (PGx) is increasingly important in individualizing therapeutic management plans, but is often implemented apart from other types of medication clinical decision support (CDS). The lack of integration of PGx into existing CDS may result in incomplete interaction information, which may pose patient safety concerns. We sought to develop a cloud-based orchestrated medication CDS service that integrates PGx with a broad set of drug screening alerts and evaluate it through a clinician utility study. METHODS We developed the PillHarmonics service for implementation per the CDS Hooks protocol, algorithmically integrating a wide range of drug interaction knowledge using cloud-based screening services from First Databank (drug-drug/allergy/condition), PharmGKB (drug-gene), and locally curated content (drug-renal/hepatic/race). We performed a user study, presenting 13 clinicians and pharmacists with a prototype of the system's usage in synthetic patient scenarios. We collected feedback via a standard questionnaire and structured interview. RESULTS Clinician assessment of PillHarmonics via the Technology Acceptance Model questionnaire shows significant evidence of perceived utility. Thematic analysis of structured interviews revealed that aggregated knowledge, concise actionable summaries, and information accessibility were highly valued, and that clinicians would use the service in their practice. CONCLUSION Medication safety and optimizing efficacy of therapy regimens remain significant issues. A comprehensive medication CDS system that leverages patient clinical and genomic data to perform a wide range of interaction checking and presents a concise and holistic view of medication knowledge back to the clinician is feasible and perceived as highly valuable for more informed decision-making. Such a system can potentially address many of the challenges identified with current medication-related CDS.
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Affiliation(s)
| | - Edna Shenvi
- Elimu Informatics, El Cerrito, California, United States
| | - Carla Alvarez
- Elimu Informatics, El Cerrito, California, United States
| | | | - Aziz Boxwala
- Elimu Informatics, El Cerrito, California, United States
| | - Benson Lee
- College of Pharmacy, Touro University California, Vallejo, California, United States
| | | | - Yelena Kleyner
- Elimu Informatics, El Cerrito, California, United States
| | - Rachel Hagemann
- Independent Contractor, San Francisco, California, United States
| | | | | | | | - James Shalaby
- Elimu Informatics, El Cerrito, California, United States
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Rasmussen LV, Agrawal AH, Botsford P, Powers A, Schnoebelen J, Xinos S, Harper G, Thanner J, McCabe S, Moore S, Wicklund CA, Duquette D, Gordon EJ. Challenges of Integrating APOL1 Genetic Test Results into the Electronic Health Record. Appl Clin Inform 2023; 14:321-325. [PMID: 37186083 PMCID: PMC10132929 DOI: 10.1055/s-0043-1767680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/12/2023] [Indexed: 05/17/2023] Open
Abstract
OBJECTIVES Integrating genetic test results into the electronic health record (EHR) is essential for integrating genetic testing into clinical practice. This article describes the organizational challenges of integrating discrete apolipoprotein L1 (APOL1) genetic test results into the EHR for a research study on culturally sensitive genetic counseling for living kidney donors. METHODS We convened a multidisciplinary team across three institutions (Northwestern University, Northwestern Memorial HealthCare [NMHC], and OHSU Knight Diagnostic Laboratories [KDL]), including researchers, physicians, clinical information technology, and project management. Through a series of meetings over a year between the team and the genetic testing laboratory, we explored and adjusted our EHR integration plan based on regulatory and budgetary constraints. RESULTS Our original proposal was to transmit results from KDL to NMHC as structured data sent via Health Level Seven (HL7) v2 message. This was ultimately deemed infeasible given the time and resources required to establish the interface, and the low number of samples to be processed for the study (n = 316). We next explored the use of Epic's Care Everywhere interoperability platform, but learned it was not possible as a laboratory test ordered for a research study; even though our intent was to study the APOL1 genetic test result's clinical use and impact, test results were still considered "research results." Faced with two remaining options-downloading a PDF from the KDL laboratory portal or scanning a faxed result from KDL-only a PDF of the APOL1 test result could be integrated into the EHR, reinforcing the status quo. CONCLUSION Even with early and ongoing stakeholder engagement, dedicated project management, and funding, unanticipated implementation challenges-especially for research projects-can result in drastic design tradeoffs.
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Affiliation(s)
- Luke V. Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Akansha H. Agrawal
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Paul Botsford
- Information Services, Digital Solutions, Northwestern Medicine, Chicago, Illinois, United States
| | - Andrew Powers
- Information Services, Clinical Applications, Northwestern Medicine, Chicago, Illinois, United States
| | - Jeffrey Schnoebelen
- Information Services, Business Relationship Management, Northwestern Medicine, Chicago, Illinois, United States
| | - Stavroula Xinos
- Information Services, Digital Administration, Northwestern Medicine, Chicago, Illinois, United States
| | - Gail Harper
- Business Development and Strategic Outreach, Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, Oregon, United States
| | - Jane Thanner
- Information Technology Group, Oregon Health & Science University, Portland, Oregon, United States
| | - Sarah McCabe
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, Oregon, United States
| | - Stephen Moore
- Knight Diagnostic Laboratories, Oregon Health & Science University, Portland, Oregon, United States
| | - Catherine A. Wicklund
- Department of Obstetrics and Gynecology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Debra Duquette
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, Illinois, United States
| | - Elisa J. Gordon
- Department of Surgery, Section of Surgical Sciences, and Center for Biomedical Ethics and Society, Vanderbilt University Medical Center, Nashville, Tennessee, United States
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Katz ML, Senter L, Reiter PL, Emerson B, Ennis AC, Shane-Carson KP, Aeilts A, Cassingham HR, Schnell PM, Agnese DM, Toland AE, Sweet K. Development of a web-based, theory-guided narrative intervention for women at elevated risk for breast cancer. PATIENT EDUCATION AND COUNSELING 2023; 106:163-169. [PMID: 36333195 PMCID: PMC10395484 DOI: 10.1016/j.pec.2022.10.348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 10/24/2022] [Accepted: 10/27/2022] [Indexed: 06/16/2023]
Abstract
OBJECTIVE To describe the development of a web-based, theory-guided narrative intervention that replaces conventional pre-test genetic counseling for women at elevated breast cancer risk. METHODS We used an iterative process that was guided by health behavior theory and feedback from multiple stakeholder groups including: 1) content input from genetic experts; 2) study team input; 3) review of video storyboards, video example, study logo, recruitment materials, post-test patient preference counseling survey, and additional study surveys; 4) video series development; and 5) intervention review and finalization of study-related materials. RESULTS The intervention is patient-centered providing convenience and an opportunity for an individual's preferences for post-test counseling delivery. The intervention's efficacy is being determined in a randomized controlled trial compared to conventional genetic counseling for adherence to recommended guidelines and changes in knowledge, perception of breast cancer risk, breast cancer-specific worry, and satisfaction with counseling. CONCLUSION If efficacious, the intervention may improve the delivery of the genetic testing and counseling process, inform best practices, and reduce the genetic counseling workforce burden. PRACTICE IMPLICATIONS The developed intervention has the potential to improve the genetic testing and counseling experience for women at elevated risk for breast cancer, inform best practices, and reduce genetic counseling workforce burden.
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Affiliation(s)
- Mira L Katz
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA; Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA.
| | - Leigha Senter
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Paul L Reiter
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA; Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA
| | - Brent Emerson
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Alysha C Ennis
- Division of Health Behavior and Health Promotion, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Kate P Shane-Carson
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Amber Aeilts
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Hayley R Cassingham
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Patrick M Schnell
- Division of Biostatistics, College of Public Health, The Ohio State University, Columbus, OH, USA
| | - Doreen M Agnese
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA; Department of Surgery, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Amanda E Toland
- Comprehensive Cancer Center, The Ohio State University, Columbus, OH, USA; Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA; Department of Cancer Biology and Genetics, College of Medicine, The Ohio State University, Columbus, OH, USA
| | - Kevin Sweet
- Division of Human Genetics, Department of Internal Medicine, College of Medicine, The Ohio State University, Columbus, OH, USA
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Dolin RH, Heale BSE, Alterovitz G, Gupta R, Aronson J, Boxwala A, Gothi SR, Haines D, Hermann A, Hongsermeier T, Husami A, Jones J, Naeymi-Rad F, Rapchak B, Ravishankar C, Shalaby J, Terry M, Xie N, Zhang P, Chamala S. Introducing HL7 FHIR Genomics Operations: a developer-friendly approach to genomics-EHR integration. J Am Med Inform Assoc 2022; 30:485-493. [PMID: 36548217 PMCID: PMC9933060 DOI: 10.1093/jamia/ocac246] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 11/16/2022] [Accepted: 12/02/2022] [Indexed: 12/24/2022] Open
Abstract
OBJECTIVE Enabling clinicians to formulate individualized clinical management strategies from the sea of molecular data remains a fundamentally important but daunting task. Here, we describe efforts towards a new paradigm in genomics-electronic health record (HER) integration, using a standardized suite of FHIR Genomics Operations that encapsulates the complexity of molecular data so that precision medicine solution developers can focus on building applications. MATERIALS AND METHODS FHIR Genomics Operations essentially "wrap" a genomics data repository, presenting a uniform interface to applications. More importantly, operations encapsulate the complexity of data within a repository and normalize redundant data representations-particularly relevant in genomics, where a tremendous amount of raw data exists in often-complex non-FHIR formats. RESULTS Fifteen FHIR Genomics Operations have been developed, designed to support a wide range of clinical scenarios, such as variant discovery; clinical trial matching; hereditary condition and pharmacogenomic screening; and variant reanalysis. Operations are being matured through the HL7 balloting process, connectathons, pilots, and the HL7 FHIR Accelerator program. DISCUSSION Next-generation sequencing can identify thousands to millions of variants, whose clinical significance can change over time as our knowledge evolves. To manage such a large volume of dynamic and complex data, new models of genomics-EHR integration are needed. Qualitative observations to date suggest that freeing application developers from the need to understand the nuances of genomic data, and instead base applications on standardized APIs can not only accelerate integration but also dramatically expand the applications of Omic data in driving precision care at scale for all.
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Affiliation(s)
- Robert H Dolin
- Corresponding Author: Robert H. Dolin, MD, Elimu Informatics, 1709 Julian Ct, El Cerrito, CA 94530, USA;
| | | | - Gil Alterovitz
- Brigham and Women’s Hospital, Boston, Massachusetts, USA,Harvard/MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, Massachusetts, USA
| | - Rohan Gupta
- Shri Mata Vaishno Devi University, Katra, Jammu and Kashmir, India
| | | | | | - Shaileshbhai R Gothi
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, Florida, USA
| | - David Haines
- Leap of Faith Technologies, Libertyville, Illinois, USA
| | - Arthur Hermann
- Department of Health IT Strategy & Policy, Kaiser Permanente, Pasadena, California, USA
| | | | - Ammar Husami
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, USA,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - James Jones
- Computational Health Informatics Program, Boston Children’s Hospital, Boston, Massachusetts, USA
| | | | | | | | | | - May Terry
- MITRE Corporation, McLean, Virginia, USA
| | - Ning Xie
- Biomedical Cybernetics Laboratory, Department of Medicine, Brigham and Women’s Hospital, Boston, Massachusetts, USA
| | - Powell Zhang
- Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
| | - Srikar Chamala
- Keck School of Medicine, Department of Pathology, University of Southern California, Los Angeles, California, USA,Department of Pathology and Laboratory Medicine, Children's Hospital Los Angeles, Los Angeles, California, USA
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Haas CB, Ralston J, Fullerton SM, Scrol A, Henrikson NB. Environmental scan of family chart linking for genetic cascade screening in a U.S. integrated health system. Front Genet 2022; 13:886650. [PMID: 36035175 PMCID: PMC9403414 DOI: 10.3389/fgene.2022.886650] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 07/20/2022] [Indexed: 11/13/2022] Open
Abstract
Background: An alternative to population-based genetic testing, automated cascade genetic testing facilitated by sharing of family health history, has been conceptualized as a more efficient and cost-effective approach to identify hereditary genetic conditions. However, existing software and applications programming interfaces (API) for the practical implementation of this approach in health care settings have not been described.Methods: We reviewed API available for facilitating cascade genetic testing in electronic health records (EHRs). We emphasize any information regarding informed consent as provided for each tool. Using semi-structured key informant interviews, we investigated uptake of and barriers to integrating automated family cascade genetic testing into the EHR.Results: We summarized the functionalities of six tools related to utilizing family health history to facilitate cascade genetic testing. No tools were explicitly capable of facilitating family cascade genetic testing, but few enterprise EHRs supported family health history linkage. We conducted five key informant interviews with four main considerations that emerged including: 1) incentives for interoperability, 2) HIPAA and regulations, 3) mobile-app and alternatives to EHR deployment, 4) fundamental changes to conceptualizing EHRs.Discussion: Despite the capabilities of existing technology, limited bioinformatic support has been developed to automate processes needed for family cascade genetic testing and the main barriers for implementation are nontechnical, including an understanding of regulations, consent, and workflow. As the trade-off between cost and efficiency for population-based and family cascade genetic testing shifts, the additional tools necessary for their implementation should be considered.
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Affiliation(s)
- Cameron B. Haas
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
- *Correspondence: Cameron B. Haas,
| | - James Ralston
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Stephanie M. Fullerton
- Department of Bioethics and Humanities, University of Washington School of Medicine, Seattle, WA, United States
| | - Aaron Scrol
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
| | - Nora B. Henrikson
- Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States
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Hicks JK, Howard R, Reisman P, Adashek JJ, Fields KK, Gray JE, McIver B, McKee K, O'Leary MF, Perkins RM, Robinson E, Tandon A, Teer JK, Markowitz J, Rollison DE. Integrating Somatic and Germline Next-Generation Sequencing Into Routine Clinical Oncology Practice. JCO Precis Oncol 2021; 5:PO.20.00513. [PMID: 34095711 PMCID: PMC8169076 DOI: 10.1200/po.20.00513] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/14/2021] [Accepted: 04/20/2021] [Indexed: 12/27/2022] Open
Abstract
Next-generation sequencing (NGS) is rapidly expanding into routine oncology practice. Genetic variations in both the cancer and inherited genomes are informative for hereditary cancer risk, prognosis, and treatment strategies. Herein, we focus on the clinical perspective of integrating NGS results into patient care to assist with therapeutic decision making. Five key considerations are addressed for operationalization of NGS testing and application of results to patient care as follows: (1) NGS test ordering and workflow design; (2) result reporting, curation, and storage; (3) clinical consultation services that provide test interpretations and identify opportunities for molecularly guided therapy; (4) presentation of genetic information within the electronic health record; and (5) education of providers and patients. Several of these key considerations center on informatics tools that support NGS test ordering and referencing back to the results for therapeutic purposes. Clinical decision support tools embedded within the electronic health record can assist with NGS test utilization and identifying opportunities for targeted therapy including clinical trial eligibility. Challenges for project and change management in operationalizing NGS-supported, evidence-based patient care in the context of current information technology systems with appropriate clinical data standards are discussed, and solutions for overcoming barriers are provided.
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Affiliation(s)
- J. Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
| | - Rachel Howard
- Department of Health Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Phillip Reisman
- Department of Health Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jacob J. Adashek
- Department of Internal Medicine, University of South Florida, Tampa, FL
| | - Karen K. Fields
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Clinical Pathways, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jhanelle E. Gray
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Bryan McIver
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Kelly McKee
- Department of Clinical Pathways, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Mandy F. O'Leary
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Randa M. Perkins
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Clinical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Edmondo Robinson
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Internal Medicine, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Ankita Tandon
- Department of Internal Medicine, University of South Florida, Tampa, FL
| | - Jamie K. Teer
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Joseph Markowitz
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Dana E. Rollison
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
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Stancil SL, Berrios C, Abdel-Rahman S. Adolescent perceptions of pharmacogenetic testing. Pharmacogenomics 2021; 22:335-343. [PMID: 33849282 PMCID: PMC8173518 DOI: 10.2217/pgs-2020-0177] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Accepted: 03/05/2021] [Indexed: 11/21/2022] Open
Abstract
Background: Despite the expansion of pharmacogenetics (PGx), the views of pediatric patients remain unknown. This study explores adolescents' understanding and perceptions of PGx testing. Methods: Adolescents who had PGx testing were interviewed and their electronic health records were reviewed. Results: Adolescents accurately described reason for testing and most felt the results impacted their current and future care. None perceived risks to securing future employment or insurance. All felt PGx would benefit their peers. Conclusion: Adolescents understand the reasons for PGx and perceive testing to be useful, low risk and applicable to peers. Findings from this study advocate for the inclusion of adolescents in shared decision-making regarding testing and for active engagement in the discussion of results.
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Affiliation(s)
- Stephani L Stancil
- Division of Adolescent Medicine, Children’s Mercy Kansas City, MO 64108, USA
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Kansas City, MO 64108, USA
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, MO 64108, USA
| | - Courtney Berrios
- Genomic Medicine Center, Children’s Mercy Kansas City, MO 64108, USA
| | - Susan Abdel-Rahman
- Division of Clinical Pharmacology, Toxicology & Therapeutic Innovation, Children’s Mercy Kansas City, MO 64108, USA
- Department of Pediatrics, University of Missouri-Kansas City School of Medicine, MO 64108, USA
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Hossain ME, Khan A, Moni MA, Uddin S. Use of Electronic Health Data for Disease Prediction: A Comprehensive Literature Review. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2021; 18:745-758. [PMID: 31478869 DOI: 10.1109/tcbb.2019.2937862] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/20/2023]
Abstract
Disease prediction has the potential to benefit stakeholders such as the government and health insurance companies. It can identify patients at risk of disease or health conditions. Clinicians can then take appropriate measures to avoid or minimize the risk and in turn, improve quality of care and avoid potential hospital admissions. Due to the recent advancement of tools and techniques for data analytics, disease risk prediction can leverage large amounts of semantic information, such as demographics, clinical diagnosis and measurements, health behaviours, laboratory results, prescriptions and care utilisation. In this regard, electronic health data can be a potential choice for developing disease prediction models. A significant number of such disease prediction models have been proposed in the literature over time utilizing large-scale electronic health databases, different methods, and healthcare variables. The goal of this comprehensive literature review was to discuss different risk prediction models that have been proposed based on electronic health data. Search terms were designed to find relevant research articles that utilized electronic health data to predict disease risks. Online scholarly databases were searched to retrieve results, which were then reviewed and compared in terms of the method used, disease type, and prediction accuracy. This paper provides a comprehensive review of the use of electronic health data for risk prediction models. A comparison of the results from different techniques for three frequently modelled diseases using electronic health data was also discussed in this study. In addition, the advantages and disadvantages of different risk prediction models, as well as their performance, were presented. Electronic health data have been widely used for disease prediction. A few modelling approaches show very high accuracy in predicting different diseases using such data. These modelling approaches have been used to inform the clinical decision process to achieve better outcomes.
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Cheng CM, So TW, Bubp JL. Characterization of Pharmacogenetic Information in Food and Drug Administration Drug Labeling and the Table of Pharmacogenetic Associations. Ann Pharmacother 2020; 55:1185-1194. [PMID: 33384014 DOI: 10.1177/1060028020983049] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022] Open
Abstract
BACKGROUND The US Food and Drug Administration (FDA) recommends using only FDA-reviewed pharmacogenetic information to make prescribing decisions based on genetic test results. Such information is available in drug labeling and in the Table of Pharmacogenetic Associations ("Associations table"). OBJECTIVE To compile a list of drug-gene pairs from drug labeling and the Associations table and categorize the pharmacogenetic information and clinical outcome associated with each drug-gene pair. METHODS This was a cross-sectional analysis of pharmacogenetic information in the Associations table and individual drug labeling in March 2020. We used the Table of Pharmacogenomic Biomarkers in Drug Labeling to identify drug labels to review. We categorized the pharmacogenetic information for each drug-gene pair according to whether the purpose was to describe (1) polymorphisms affecting drug disposition (metabolism or transport), (2) polymorphisms affecting a direct drug target, (3) variants associated with adverse drug reaction (ADR) susceptibility, (4) variants associated with therapeutic failure, (5) a biomarker-defined indication, or (6) a biomarker-defined ADR. We also categorized the clinical outcome-efficacy, safety, or unknown-associated with each drug-gene pair. We reported counts and proportions of drug-gene pairs in each pharmacogenetic information and clinical outcome category. RESULTS We identified 308 drug-gene pairs, of which 36% were associated with a biomarker-defined drug indication, 33% with polymorphic drug metabolism, and 28% with ADR susceptibility. Most drug-gene pairs (n = 267, 87%) were associated with an efficacy or safety-related outcome. CONCLUSION AND RELEVANCE FDA-reviewed pharmacogenetic information is available for more than 300 drug-gene pairs and can help guide prescribing decisions.
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Affiliation(s)
| | - Thomas W So
- First Databank, Inc, South San Francisco, CA, USA
| | - Jeff L Bubp
- First Databank, Inc, South San Francisco, CA, USA
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11
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Şık AS, Aydınoğlu AU, Aydın Son Y. Assessing the readiness of Turkish health information systems for integrating genetic/genomic patient data: System architecture and available terminologies, legislative, and protection of personal data. Health Policy 2020; 125:203-212. [PMID: 33342546 DOI: 10.1016/j.healthpol.2020.12.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2019] [Revised: 11/29/2020] [Accepted: 12/05/2020] [Indexed: 02/08/2023]
Abstract
Advances in genetic/genomic research and translational studies drive the progress on molecular diagnosis, personalised treatment, and monitoring. Healthcare professionals and governments are encouraged to set administrative regulations and implement structured and interoperable representation to utilise the genetic/genomic data, which will support precision medicine approaches through Health Information Systems (HIS). Clear regulations and careful legislation are also crucial for the security and privacy of genetic/genomic test data. In this article, we present a review of the National Health Information System of Turkey (NHIS-T) about interoperable health data representation for genetic tests. We discuss the content of rules and regulations related to genetic/genomic testing and structured data representation in Turkey. A brief comparison of the Turkish "Law on the Protection of Personal Data" (LPPD) in genetic/genomic data privacy with its counterparts is presented. The final discussion about the shortcomings of Turkey is transferable to health information systems worldwide. Constructing a national reference database and IT infrastructure to enable data integration and exchange between genomic data, metadata, and health records will improve genetics studies' utility and outcomes. The critical success factors behind integration are establishing broadly accepted terminologies and government guidance. The governments should set clear a transparent policy defining the legal and ethical framework, workforce training, clinical decision-support tools, public engagement, and education concurrently.
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Affiliation(s)
- Ayhan Serkan Şık
- Department of Medical Informatics, Middle East Technical University, METU Informatics Institute, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, 06800, Ankara, Turkey; Department of Management Information Systems, Ankara Medipol University, Faculty of Economics, Administrative and Social Sciences, Haci Bayram Mahallesi, Talatpasa Bulvari, No:2, Ankara, Turkey.
| | - Arsev Umur Aydınoğlu
- Department of Science and Technology Policy Studies, Middle East Technical University, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, MM Building 3rd Floor No: 320, 06800, Ankara, Turkey.
| | - Yeşim Aydın Son
- Department of Medical Informatics, Middle East Technical University, METU Informatics Institute, Universiteler Mahallesi, Dumlupinar Bulvari, No:1, 06800, Ankara, Turkey.
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12
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Chang WC, Tanoshima R, Ross CJD, Carleton BC. Challenges and Opportunities in Implementing Pharmacogenetic Testing in Clinical Settings. Annu Rev Pharmacol Toxicol 2020; 61:65-84. [PMID: 33006916 DOI: 10.1146/annurev-pharmtox-030920-025745] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
The clinical implementation of pharmacogenetic biomarkers continues to grow as new genetic variants associated with drug outcomes are discovered and validated. The number of drug labels that contain pharmacogenetic information also continues to expand. Published, peer-reviewed clinical practice guidelines have also been developed to support the implementation of pharmacogenetic tests. Incorporating pharmacogenetic information into health care benefits patients as well as clinicians by improving drug safety and reducing empiricism in drug selection. Barriers to the implementation of pharmacogenetic testing remain. This review explores current pharmacogenetic implementation initiatives with a focus on the challenges of pharmacogenetic implementation and potential opportunities to overcome these challenges.
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Affiliation(s)
- Wan-Chun Chang
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia V6H 3V4, Canada; .,BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z 4H4, Canada
| | - Reo Tanoshima
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia V6H 3V4, Canada; .,BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z 4H4, Canada
| | - Colin J D Ross
- BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z 4H4, Canada.,Faculty of Pharmaceutical Sciences, University of British Columbia, Vancouver, British Columbia V6T 1Z3, Canada
| | - Bruce C Carleton
- Division of Translational Therapeutics, Department of Pediatrics, Faculty of Medicine, University of British Columbia, Vancouver, British Columbia V6H 3V4, Canada; .,BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z 4H4, Canada.,Pharmaceutical Outcomes Programme, BC Children's Hospital Research Institute, Vancouver, British Columbia V5Z 4H4, Canada
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13
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Rajurkar S, Mambetsariev I, Pharaon R, Leach B, Tan T, Kulkarni P, Salgia R. Non-Small Cell Lung Cancer from Genomics to Therapeutics: A Framework for Community Practice Integration to Arrive at Personalized Therapy Strategies. J Clin Med 2020; 9:E1870. [PMID: 32549358 PMCID: PMC7356243 DOI: 10.3390/jcm9061870] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/26/2020] [Revised: 06/12/2020] [Accepted: 06/12/2020] [Indexed: 12/25/2022] Open
Abstract
Non-small cell lung cancer (NSCLC) is a heterogeneous disease, and therapeutic management has advanced with the identification of various key oncogenic mutations that promote lung cancer tumorigenesis. Subsequent studies have developed targeted therapies against these oncogenes in the hope of personalizing therapy based on the molecular genomics of the tumor. This review presents approved treatments against actionable mutations in NSCLC as well as promising targets and therapies. We also discuss the current status of molecular testing practices in community oncology sites that would help to direct oncologists in lung cancer decision-making. We propose a collaborative framework between community practice and academic sites that can help improve the utilization of personalized strategies in the community, through incorporation of increased testing rates, virtual molecular tumor boards, vendor-based oncology clinical pathways, and an academic-type singular electronic health record system.
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Affiliation(s)
| | | | | | | | | | | | - Ravi Salgia
- Department of Medical Oncology and Therapeutics Research, City of Hope, Duarte, CA 91010, USA; (S.R.); (I.M.); (R.P.); (B.L.); (T.T.); (P.K.)
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14
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A Systematic Review of Network Studies Based on Administrative Health Data. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:ijerph17072568. [PMID: 32283623 PMCID: PMC7177895 DOI: 10.3390/ijerph17072568] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/10/2020] [Revised: 04/05/2020] [Accepted: 04/06/2020] [Indexed: 11/17/2022]
Abstract
Effective and efficient delivery of healthcare services requires comprehensive collaboration and coordination between healthcare entities and their complex inter-reliant activities. This inter-relation and coordination lead to different networks among diverse healthcare stakeholders. It is important to understand the varied dynamics of these networks to measure the efficiency of healthcare delivery services. To date, however, a work that systematically reviews these networks outlined in different studies is missing. This article provides a comprehensive summary of studies that have focused on networks and administrative health data. By summarizing different aspects including research objectives, key research questions, adopted methods, strengths and weaknesses, this research provides insights into the inherently complex and interlinked networks present in healthcare services. The outcome of this research is important to healthcare management and may guide further research in this area.
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15
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Li R, Chen Y, Ritchie MD, Moore JH. Electronic health records and polygenic risk scores for predicting disease risk. Nat Rev Genet 2020; 21:493-502. [PMID: 32235907 DOI: 10.1038/s41576-020-0224-1] [Citation(s) in RCA: 53] [Impact Index Per Article: 13.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/02/2020] [Indexed: 01/03/2023]
Abstract
Accurate prediction of disease risk based on the genetic make-up of an individual is essential for effective prevention and personalized treatment. Nevertheless, to date, individual genetic variants from genome-wide association studies have achieved only moderate prediction of disease risk. The aggregation of genetic variants under a polygenic model shows promising improvements in prediction accuracies. Increasingly, electronic health records (EHRs) are being linked to patient genetic data in biobanks, which provides new opportunities for developing and applying polygenic risk scores in the clinic, to systematically examine and evaluate patient susceptibilities to disease. However, the heterogeneous nature of EHR data brings forth many practical challenges along every step of designing and implementing risk prediction strategies. In this Review, we present the unique considerations for using genotype and phenotype data from biobank-linked EHRs for polygenic risk prediction.
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Affiliation(s)
- Ruowang Li
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yong Chen
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Marylyn D Ritchie
- Department of Genetics, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Biostatistics, Epidemiology & Informatics, University of Pennsylvania, Philadelphia, PA, USA.
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16
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Beesley LJ, Salvatore M, Fritsche LG, Pandit A, Rao A, Brummett C, Willer CJ, Lisabeth LD, Mukherjee B. The emerging landscape of health research based on biobanks linked to electronic health records: Existing resources, statistical challenges, and potential opportunities. Stat Med 2020; 39:773-800. [PMID: 31859414 PMCID: PMC7983809 DOI: 10.1002/sim.8445] [Citation(s) in RCA: 43] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 09/10/2019] [Accepted: 11/16/2019] [Indexed: 01/03/2023]
Abstract
Biobanks linked to electronic health records provide rich resources for health-related research. With improvements in administrative and informatics infrastructure, the availability and utility of data from biobanks have dramatically increased. In this paper, we first aim to characterize the current landscape of available biobanks and to describe specific biobanks, including their place of origin, size, and data types. The development and accessibility of large-scale biorepositories provide the opportunity to accelerate agnostic searches, expedite discoveries, and conduct hypothesis-generating studies of disease-treatment, disease-exposure, and disease-gene associations. Rather than designing and implementing a single study focused on a few targeted hypotheses, researchers can potentially use biobanks' existing resources to answer an expanded selection of exploratory questions as quickly as they can analyze them. However, there are many obvious and subtle challenges with the design and analysis of biobank-based studies. Our second aim is to discuss statistical issues related to biobank research such as study design, sampling strategy, phenotype identification, and missing data. We focus our discussion on biobanks that are linked to electronic health records. Some of the analytic issues are illustrated using data from the Michigan Genomics Initiative and UK Biobank, two biobanks with two different recruitment mechanisms. We summarize the current body of literature for addressing these challenges and discuss some standing open problems. This work complements and extends recent reviews about biobank-based research and serves as a resource catalog with analytical and practical guidance for statisticians, epidemiologists, and other medical researchers pursuing research using biobanks.
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Affiliation(s)
| | | | | | - Anita Pandit
- University of Michigan, Department of Biostatistics
| | - Arvind Rao
- University of Michigan, Department of Computational Medicine and Bioinformatics
| | - Chad Brummett
- University of Michigan, Department of Anesthesiology
| | - Cristen J. Willer
- University of Michigan, Department of Computational Medicine and Bioinformatics
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17
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Gazzarata R, Monteverde ME, Ruggiero C, Maggi N, Palmieri D, Parruti G, Giacomini M. Healthcare Associated Infections: An Interoperable Infrastructure for Multidrug Resistant Organism Surveillance. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2020; 17:E465. [PMID: 31936787 PMCID: PMC7013448 DOI: 10.3390/ijerph17020465] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 12/25/2019] [Accepted: 12/30/2019] [Indexed: 01/26/2023]
Abstract
Prevention and surveillance of healthcare associated infections caused by multidrug resistant organisms (MDROs) has been given increasing attention in recent years and is nowadays a major priority for health care systems. The creation of automated regional, national and international surveillance networks plays a key role in this respect. A surveillance system has been designed for the Abruzzo region in Italy, focusing on the monitoring of the MDROs prevalence in patients, on the appropriateness of antibiotic prescription in hospitalized patients and on foreseeable interactions with other networks at national and international level. The system has been designed according to the Service Oriented Architecture (SOA) principles, and Healthcare Service Specification (HSSP) standards and Clinical Document Architecture Release 2 (CDAR2) have been adopted. A description is given with special reference to implementation state, specific design and implementation choices and next foreseeable steps. The first release will be delivered at the Complex Operating Unit of Infectious Diseases of the Local Health Authority of Pescara (Italy).
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Affiliation(s)
| | | | - Carmelina Ruggiero
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
| | - Norbert Maggi
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
| | - Dalia Palmieri
- Epidemiology Office, Azienda Unità Sanitaria Locale (AUSL) di Pescara, 65124 Pescara, Italy;
| | - Giustino Parruti
- Department of Infectious Disease, Azienda Sanitaria Locale (AUSL) di Pescara, 65124 Pescara, Italy;
| | - Mauro Giacomini
- Healthropy S.r.l., 17100 Savona, Italy (C.R.); (N.M.); (M.G.)
- Department of Informatics, Bioengineering, Robotics and Systems Engineering (DIBRIS), University of Genova, 16145 Genoa, Italy
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19
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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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20
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Campion DP, Dowell FJ. Translating Pharmacogenetics and Pharmacogenomics to the Clinic: Progress in Human and Veterinary Medicine. Front Vet Sci 2019; 6:22. [PMID: 30854372 PMCID: PMC6396708 DOI: 10.3389/fvets.2019.00022] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Accepted: 01/18/2019] [Indexed: 12/29/2022] Open
Abstract
As targeted personalized therapy becomes more widely used in human medicine, clients will expect the veterinary clinician to be able to implement an evidence-based strategy regarding both the prescribing of medicines and also recognition of the potential for adverse drug reactions (ADR) for their pet, at breed and individual level. This review aims to provide an overview of current developments and challenges in pharmacogenetics in medicine for a veterinary audience and to map these to developments in veterinary pharmacogenetics. Pharmacogenetics has been in development over the past 100 years but has been revolutionized following the publication of the human, and then veterinary species genomes. Genetic biomarkers called pharmacogenes have been identified as specific genetic loci on chromosomes which are associated with either positive or adverse drug responses. Pharmacogene variation may be classified according to the associated drug response, such as a change in (1) the pharmacokinetics; (2) the pharmacodynamics; (3) genes in the downstream pathway of the drug or (4) the effect of “off-target” genes resulting in a response that is unrelated to the intended target. There are many barriers to translation of pharmacogenetic information to the clinic, however, in human medicine, international initiatives are promising real change in the delivery of personalized medicine by 2025. We argue that for effective translation into the veterinary clinic, clinicians, international experts, and stakeholders must collaborate to ensure quality assurance and genetic test validation so that animals may also benefit from this genomics revolution.
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Affiliation(s)
- Deirdre P Campion
- UCD School of Veterinary Medicine, University College Dublin, Dublin, Ireland
| | - Fiona J Dowell
- Division of Veterinary Science and Education, School of Veterinary Medicine, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, United Kingdom
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