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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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2
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Lau-Min KS, Bleznuck J, Wollack C, McKenna DB, Long JM, Hubert AP, Johnson M, Rochester SE, Constantino G, Dudzik C, Doucette A, Wangensteen K, Domchek SM, Landgraf J, Chen J, Nathanson KL, Katona BW. Development of an Electronic Health Record-Based Clinical Decision Support Tool for Patients With Lynch Syndrome. JCO Clin Cancer Inform 2023; 7:e2300024. [PMID: 37639653 PMCID: PMC10857752 DOI: 10.1200/cci.23.00024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 04/22/2023] [Accepted: 07/12/2023] [Indexed: 08/31/2023] Open
Abstract
PURPOSE To develop an electronic health record (EHR)-based clinical decision support (CDS) tool to promote guideline-recommended cancer risk management among patients with Lynch syndrome (LS), an inherited cancer syndrome that confers an increased risk of colorectal and other cancer types. MATERIALS AND METHODS We conducted a cross-sectional study to determine the baseline prevalence and predictors of guideline-recommended colonic surveillance and annual genetics program visits among patients with LS. Multivariable log-binomial regressions estimated prevalence ratios (PRs) of cancer risk management adherence by baseline sociodemographic and clinical characteristics. These analyses provided rationale for the development of an EHR-based CDS tool to support patients and clinicians with LS-related endoscopic surveillance and annual genetics program visits. The CDS leverages an EHR platform linking discrete genetic data to LS Genomic Indicators, in turn driving downstream clinician- and patient-facing CDS. RESULTS Among 323 patients with LS, cross-sectional adherence to colonic surveillance and annual genetics program visits was 69.3% and 55.4%, respectively. Patients with recent electronic patient portal use were more likely to be adherent to colonic surveillance (PR, 1.67; 95% CI, 1.11 to 2.52). Patients more recently diagnosed with LS were more likely to be adherent to annual genetics program visits (PR, 0.58; 95% CI, 0.44 to 0.76 for 2-4 years; PR, 0.62; 95% CI, 0.51 to 0.75 for ≥4 compared with <2 years). Our EHR-based CDS tool is now active for 421 patients with LS throughout our health system. CONCLUSION We have successfully developed an EHR-based CDS tool to promote guideline-recommended cancer risk management among patients with LS.
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Affiliation(s)
- Kelsey S. Lau-Min
- Division of Hematology/Oncology, Department of Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA
| | - Joseph Bleznuck
- Information Services Applications, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Colin Wollack
- Information Services Applications, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Danielle B. McKenna
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica M. Long
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna P. Hubert
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Mariah Johnson
- Department of Medical Ethics and Health Policy, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Shavon E. Rochester
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Gillain Constantino
- Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Christina Dudzik
- Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Abigail Doucette
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Kirk Wangensteen
- Division of Gastroenterology and Hepatology, Department of Medicine, Mayo Clinic, Rochester, MN
| | - Susan M. Domchek
- Division of Hematology/Oncology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Landgraf
- Information Services Applications, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica Chen
- Information Services Applications, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Katherine L. Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
- Abramson Cancer Center, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
| | - Bryson W. Katona
- Division of Gastroenterology, Department of Medicine, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
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3
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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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4
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Handra J, Elbert A, Gazzaz N, Moller-Hansen A, Hyunh S, Lee HK, Boerkoel P, Alderman E, Anderson E, Clarke L, Hamilton S, Hamman R, Hughes S, Ip S, Langlois S, Lee M, Li L, Mackenzie F, Patel MS, Prentice LM, Sangha K, Sato L, Seath K, Seppelt M, Swenerton A, Warnock L, Zambonin JL, Boerkoel CF, Chin HL, Armstrong L. The practice of genomic medicine: A delineation of the process and its governing principles. Front Med (Lausanne) 2023; 9:1071348. [PMID: 36714130 PMCID: PMC9877428 DOI: 10.3389/fmed.2022.1071348] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2022] [Accepted: 12/23/2022] [Indexed: 01/13/2023] Open
Abstract
Genomic medicine, an emerging medical discipline, applies the principles of evolution, developmental biology, functional genomics, and structural genomics within clinical care. Enabling widespread adoption and integration of genomic medicine into clinical practice is key to achieving precision medicine. We delineate a biological framework defining diagnostic utility of genomic testing and map the process of genomic medicine to inform integration into clinical practice. This process leverages collaboration and collective cognition of patients, principal care providers, clinical genomic specialists, laboratory geneticists, and payers. We detail considerations for referral, triage, patient intake, phenotyping, testing eligibility, variant analysis and interpretation, counseling, and management within the utilitarian limitations of health care systems. To reduce barriers for clinician engagement in genomic medicine, we provide several decision-making frameworks and tools and describe the implementation of the proposed workflow in a prototyped electronic platform that facilitates genomic care. Finally, we discuss a vision for the future of genomic medicine and comment on areas for continued efforts.
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Affiliation(s)
- Julia Handra
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Adrienne Elbert
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Nour Gazzaz
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada,Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada,Department of Pediatrics, Faculty of Medicine, King Abdulaziz University, Jeddah, Saudi Arabia
| | - Ashley Moller-Hansen
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Stephanie Hyunh
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Hyun Kyung Lee
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Pierre Boerkoel
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada
| | - Emily Alderman
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Erin Anderson
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Lorne Clarke
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Sara Hamilton
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Ronnalea Hamman
- Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Shevaun Hughes
- Clinical Research Informatics, Provincial Health Services Authority, Vancouver, BC, Canada
| | - Simon Ip
- Process & Systems Improvement, Provincial Health Services Authority, Vancouver, BC, Canada
| | - Sylvie Langlois
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Mary Lee
- Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Laura Li
- Breakthrough Genomics, Irvine, CA, United States
| | - Frannie Mackenzie
- Women’s Health Research Institute, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Millan S. Patel
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Leah M. Prentice
- Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Karan Sangha
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Laura Sato
- Process & Systems Improvement, Provincial Health Services Authority, Vancouver, BC, Canada
| | - Kimberly Seath
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Margaret Seppelt
- Process & Systems Improvement, Provincial Health Services Authority, Vancouver, BC, Canada
| | - Anne Swenerton
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Lynn Warnock
- Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Jessica L. Zambonin
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Cornelius F. Boerkoel
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
| | - Hui-Lin Chin
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada,Khoo Teck Puat-National University Children’s Medical Institute, National University Hospital, Singapore, Singapore,*Correspondence: Hui-Lin Chin,
| | - Linlea Armstrong
- Department of Medical Genetics, University of British Columbia, Vancouver, BC, Canada,Provincial Medical Genetics Program, British Columbia Women’s Hospital and Health Centre, Vancouver, BC, Canada
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5
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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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6
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Lau-Min KS, McKenna D, Asher SB, Bardakjian T, Wollack C, Bleznuck J, Biros D, Anantharajah A, Clark DF, Condit C, Ebrahimzadeh JE, Long JM, Powers J, Raper A, Schoenbaum A, Feldman M, Steinfeld L, Tuteja S, VanZandbergen C, Domchek SM, Ritchie MD, Landgraf J, Chen J, Nathanson KL. Impact of integrating genomic data into the electronic health record on genetics care delivery. Genet Med 2022; 24:2338-2350. [PMID: 36107166 PMCID: PMC10176082 DOI: 10.1016/j.gim.2022.08.009] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2022] [Revised: 08/15/2022] [Accepted: 08/15/2022] [Indexed: 11/30/2022] Open
Abstract
PURPOSE Integrating genomic data into the electronic health record (EHR) is key for optimally delivering genomic medicine. METHODS The PennChart Genomics Initiative (PGI) at the University of Pennsylvania is a multidisciplinary collaborative that has successfully linked orders and results from genetic testing laboratories with discrete genetic data in the EHR. We quantified the use of the genomic data within the EHR, performed a time study with genetic counselors, and conducted key informant interviews with PGI members to evaluate the effect of the PGI's efforts on genetics care delivery. RESULTS The PGI has interfaced with 4 genetic testing laboratories, resulting in the creation of 420 unique computerized genetic testing orders that have been used 4073 times to date. In a time study of 96 genetic testing activities, EHR use was associated with significant reductions in time spent ordering (2 vs 8 minutes, P < .001) and managing (1 vs 5 minutes, P < .001) genetic results compared with the use of online laboratory-specific portals. In key informant interviews, multidisciplinary collaboration and institutional buy-in were identified as key ingredients for the PGI's success. CONCLUSION The PGI's efforts to integrate genomic medicine into the EHR have substantially streamlined the delivery of genomic medicine.
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Affiliation(s)
- Kelsey S Lau-Min
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Danielle McKenna
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Stephanie Byers Asher
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Tanya Bardakjian
- Department of Neurology, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Colin Wollack
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Joseph Bleznuck
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Daniel Biros
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Arravinth Anantharajah
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Dana F Clark
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Courtney Condit
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica E Ebrahimzadeh
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica M Long
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jacquelyn Powers
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna Raper
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Anna Schoenbaum
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Michael Feldman
- Department of Pathology and Laboratory Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Sony Tuteja
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | | | - Susan M Domchek
- Division of Hematology and Oncology, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jeffrey Landgraf
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Jessica Chen
- Information Services, Penn Medicine, University of Pennsylvania, Philadelphia, PA
| | - Katherine L Nathanson
- Division of Translational Medicine and Human Genetics, Department of Medicine, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA; Abramson Cancer Center, Perelman School of Medicine, Penn Medicine, University of Pennsylvania, Philadelphia, PA.
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7
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Wiley K, Findley L, Goldrich M, Rakhra-Burris TK, Stevens A, Williams P, Bult CJ, Chisholm R, Deverka P, Ginsburg GS, Green ED, Jarvik G, Mensah GA, Ramos E, Relling MV, Roden DM, Rowley R, Alterovitz G, Aronson S, Bastarache L, Cimino JJ, Crowgey EL, Del Fiol G, Freimuth RR, Hoffman MA, Jeff J, Johnson K, Kawamoto K, Madhavan S, Mendonca EA, Ohno-Machado L, Pratap S, Taylor CO, Ritchie MD, Walton N, Weng C, Zayas-Cabán T, Manolio TA, Williams MS. A research agenda to support the development and implementation of genomics-based clinical informatics tools and resources. J Am Med Inform Assoc 2022; 29:1342-1349. [PMID: 35485600 PMCID: PMC9277642 DOI: 10.1093/jamia/ocac057] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Revised: 02/22/2022] [Accepted: 04/08/2022] [Indexed: 11/12/2022] Open
Abstract
OBJECTIVE The Genomic Medicine Working Group of the National Advisory Council for Human Genome Research virtually hosted its 13th genomic medicine meeting titled "Developing a Clinical Genomic Informatics Research Agenda". The meeting's goal was to articulate a research strategy to develop Genomics-based Clinical Informatics Tools and Resources (GCIT) to improve the detection, treatment, and reporting of genetic disorders in clinical settings. MATERIALS AND METHODS Experts from government agencies, the private sector, and academia in genomic medicine and clinical informatics were invited to address the meeting's goals. Invitees were also asked to complete a survey to assess important considerations needed to develop a genomic-based clinical informatics research strategy. RESULTS Outcomes from the meeting included identifying short-term research needs, such as designing and implementing standards-based interfaces between laboratory information systems and electronic health records, as well as long-term projects, such as identifying and addressing barriers related to the establishment and implementation of genomic data exchange systems that, in turn, the research community could help address. DISCUSSION Discussions centered on identifying gaps and barriers that impede the use of GCIT in genomic medicine. Emergent themes from the meeting included developing an implementation science framework, defining a value proposition for all stakeholders, fostering engagement with patients and partners to develop applications under patient control, promoting the use of relevant clinical workflows in research, and lowering related barriers to regulatory processes. Another key theme was recognizing pervasive biases in data and information systems, algorithms, access, value, and knowledge repositories and identifying ways to resolve them.
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Affiliation(s)
- Ken Wiley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Laura Findley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Madison Goldrich
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Tejinder K Rakhra-Burris
- Department of Medicine, Center for Applied Genomics & Precision Medicine, Duke University, Durham, North Carolina, USA
| | - Ana Stevens
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Pamela Williams
- Department of Medicine, Center for Applied Genomics & Precision Medicine, Duke University, Durham, North Carolina, USA
| | | | - Rex Chisholm
- Feinberg School of Medicine, Northwestern University, Chicago, Illinois, USA
| | - Patricia Deverka
- Center for Translational and Policy Research in Precision Medicine, University of California at San Francisco, San Francisco, California, USA
| | - Geoffrey S Ginsburg
- All of Us Research Program, National Institutes of Health, Bethesda, Maryland, USA
| | - Eric D Green
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gail Jarvik
- Division of Medical Genetics, University of Washington, Seattle, Washington, USA
| | - George A Mensah
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Erin Ramos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Mary V Relling
- Pharmacy and Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - Dan M Roden
- Departments of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Robb Rowley
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gil Alterovitz
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Samuel Aronson
- Mass General Brigham, Research Information Sciences and Computing, Somerville, Massachusetts, USA
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - James J Cimino
- Heersink School of Medicine, University of Alabama at Birmingham, Alabama, USA
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Robert R Freimuth
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Mark A Hoffman
- School of Medicine, Children's Mercy Hospital Kansas City, University of Missouri Kansas City, Lees Summit, Missouri, USA
| | | | - Kevin Johnson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Kensaku Kawamoto
- Department of Biomedical Informatics, University of Utah School of Medicine, Salt Lake City, Utah, USA
| | - Subha Madhavan
- Innovation Center for Biomedical Informatics, Georgetown University, Washington, District of Columbia, USA
| | - Eneida A Mendonca
- Regenstrief Institute, Inc., Indianapolis, Indiana, USA.,Department of Pediatrics, Department of Biostatistics and Health Data Sciences, Indiana University School of Medicine, Indianapolis, Indiana, USA
| | - Lucila Ohno-Machado
- Department of Biomedical Informatics, University of California San Diego, La Jolla, California, USA
| | - Siddharth Pratap
- Bioinformatics Core, Meharry Medical College, Nashville, Tennessee, USA
| | | | - Marylyn D Ritchie
- Department of Genetics, Perelman School of Medicine, Institute for Biomedical Informatics, Penn Center for Precision Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Nephi Walton
- Intermountain Precision Genomics, Intermountain Healthcare, St George, Utah, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Teresa Zayas-Cabán
- National Library of Medicine, National Institutes of Health, Bethesda, Maryland, USA
| | - Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Marc S Williams
- Geisinger, Genomic Medicine Institute, Danville, Pennsylvania, USA
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8
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Austin CA, Szeto A, Gupta A, Wiltshire T, Crona DJ, Kistler C. The Pharmacogenetics of Opiates and Its Impact on Delirium in Mechanically Ventilated Adults: A Pilot Study. J Pharm Technol 2022; 38:195-201. [PMID: 35832565 PMCID: PMC9272493 DOI: 10.1177/87551225221085116] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Background: Pharmacogenetics may explain a substantial proportion of the variation seen in the efficacy and risk profile of analgesosedative drugs and the incidence of delirium in critically ill adults. Objectives: Conduct a feasibility study to demonstrate the reliability of collecting and analyzing pharmacogenetic information from critically ill patients and to assess the impact of pharmacogenetics on intensive care unit (ICU) outcomes. Methods: We prospectively enrolled subjects from the Medical ICU at the University of North Carolina (UNC). DNA was obtained via a buccal swab and evaluated using the DNA2Rx assay. We collected data on demographics, daily cumulative psychoactive medication exposure, and severity of illness. We performed daily delirium assessments via the CAM-ICU. We analyzed associations between select single nucleotide polymorphisms (SNPs) and delirium. Results: From June, 2018 through January, 2019, we screened 244 patients and enrolled 50. The median age was 62.0 years old (range: 28-82 years old), and 27 (54%) of the subjects were female. In all, 49 (98%) samples were both high quality and sufficient quantity. In secondary analyses, we found that 80% (12/15) of patients with two 2 copies of a G allele at rs4680 on COMT experienced delirium, whereas 44% (4/9) of patients with 2 copies of an A allele at this location had delirium. In all, 44% (4/9) of patients with 2 T allele copies at rs7439366 on UGT2B7 experienced delirium compared to 73% (11/15) of patients with 2 C allele copies at this location. Conclusions: We can feasibly collect genetic information from critically ill adults. We were able to efficiently collect high quality DNA of sufficient quantity to conduct pharmacogenetic analysis in this critically ill population. Although the sample size of our current study is too small to conduct robust inferential analyses, it suggests potential SNP targets for a future larger study.
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Affiliation(s)
- C. Adrian Austin
- Division of Pulmonary and Critical Care Medicine, Division of Geriatric Medicine, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Division of Geriatric Medicine, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andy Szeto
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Apoorva Gupta
- School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Timothy Wiltshire
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
| | - Daniel J. Crona
- Division of Pharmacotherapy and Experimental Therapeutics, UNC Eshelman School of Pharmacy, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- UNC Lineberger Comprehensive Cancer Center, Chapel Hill, NC, USA
- Department of Pharmacy, UNC Hospitals and Clinics, Chapel Hill, NC, USA
| | - Christine Kistler
- Division of Geriatric Medicine, Department of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- Department of Family Medicine, School of Medicine, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
- The Cecil G. Sheps Center for Health Services Research, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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9
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A qualitative study of prevalent laboratory information systems and data communication patterns for genetic test reporting. Genet Med 2021; 23:2171-2177. [PMID: 34230635 DOI: 10.1038/s41436-021-01251-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Revised: 06/08/2021] [Accepted: 06/09/2021] [Indexed: 11/09/2022] Open
Abstract
PURPOSE The availability of genetic test data within the electronic health record (EHR) is a pillar of the US vision for an interoperable health IT infrastructure and a learning health system. Although EHRs have been highly investigated, evaluation of the information systems used by the genetic labs has received less attention-but is necessary for achieving optimal interoperability. This study aimed to characterize how US genetic testing labs handle their information processing tasks. METHODS We followed a qualitative research method that included interviewing lab representatives and a panel discussion to characterize the information flow models. RESULTS Ten labs participated in the study. We identified three generic lab system models and their relevant characteristics: a backbone system with additional specialized systems for interpreting genetic results, a brokering system that handles housekeeping and communication, and a single primary system for results interpretation and report generation. CONCLUSION Labs have heterogeneous workflows and generally have a low adoption of standards when sending genetic test reports back to EHRs. Core interpretations are often delivered as free text, limiting their computational availability for clinical decision support tools. Increased provision of genetic test data in discrete and standard-based formats by labs will benefit individual and public health.
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10
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Taylor CO, Rasmussen LV, Rasmussen-Torvik LJ, Prows CA, Dorr DA, Samal L, Aronson S. Facilitating Genetics Aware Clinical Decision Support: Putting the eMERGE Infrastructure into Practice. ACI OPEN 2021; 5:e54-e58. [PMID: 37920232 PMCID: PMC10621326 DOI: 10.1055/s-0041-1729981] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/04/2023]
Abstract
This editorial provides context for a series of published case reports in ACI Open by summarizing activities and outputs of joint electronic health record integration and pharmacogenomics workgroups in the NIH-funded electronic Medical Records and Genomics (eMERGE) Network. A case report is a useful tool to describe the range of capabilities that an IT infrastructure or a particular technology must support. The activities we describe have informed infrastructure requirements used during eMERGE phase III, provided a venue to share experiences and ask questions among other eMERGE sites, summarized potential hazards that might be encountered for specific clinical decision support (CDS) implementation scenarios, and provided a simple framework that captured progress toward implementing CDS at eMERGE sites in a consistent format.
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Affiliation(s)
- Casey Overby Taylor
- Departments of Medicine and Biomedical Engineering, Johns Hopkins University, Baltimore, Maryland, United States
- Geisinger Health System, Genomic Medicine Institute, Danville, Pennsylvania, United States
| | - Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University, Chicago, Illinois, United States
| | | | - Cynthia A Prows
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, United States
| | - David A Dorr
- Departments of Medical Informatics and Clinical Epidemiology and Medicine, Oregon Health & Science University, Portland, Oregon, United States
| | - Lipika Samal
- Department of Medicine, Brigham and Women's Hospital, Boston, Massachusetts, United States
| | - Samuel Aronson
- Partners Personalized Medicine, Partners HealthCare, Cambridge, Massachusetts, United States
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11
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Dolin RH, Gothi SR, Boxwala A, Heale BSE, Husami A, Jones J, Khangar H, Londhe S, Naeymi-Rad F, Rao S, Rapchak B, Shalaby J, Suraj V, Xie N, Chamala S, Alterovitz G. vcf2fhir: a utility to convert VCF files into HL7 FHIR format for genomics-EHR integration. BMC Bioinformatics 2021; 22:104. [PMID: 33653260 PMCID: PMC7923512 DOI: 10.1186/s12859-021-04039-1] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2020] [Accepted: 02/21/2021] [Indexed: 11/13/2022] Open
Abstract
Background VCF formatted files are the lingua franca of next-generation sequencing, whereas HL7 FHIR is emerging as a standard language for electronic health record interoperability. A growing number of FHIR-based clinical genomics applications are emerging. Here, we describe an open source utility for converting variants from VCF format into HL7 FHIR format. Results vcf2fhir converts VCF variants into a FHIR Genomics Diagnostic Report. Conversion translates each VCF row into a corresponding FHIR-formatted variant in the generated report. In scope are simple variants (SNVs, MNVs, Indels), along with zygosity and phase relationships, for autosomes, sex chromosomes, and mitochondrial DNA. Input parameters include VCF file and genome build (‘GRCh37’ or ‘GRCh38’); and optionally a conversion region that indicates the region(s) to convert, a studied region that lists genomic regions studied by the lab, and a non-callable region that lists studied regions deemed uncallable by the lab. Conversion can be limited to a subset of VCF by supplying genomic coordinates of the conversion region(s). If studied and non-callable regions are also supplied, the output FHIR report will include ‘region-studied’ observations that detail which portions of the conversion region were studied, and of those studied regions, which portions were deemed uncallable. We illustrate the vcf2fhir utility via two case studies. The first, 'SMART Cancer Navigator', is a web application that offers clinical decision support by linking patient EHR information to cancerous gene variants. The second, 'Precision Genomics Integration Platform', intersects a patient's FHIR-formatted clinical and genomic data with knowledge bases in order to provide on-demand delivery of contextually relevant genomic findings and recommendations to the EHR. Conclusions Experience to date shows that the vcf2fhir utility can be effectively woven into clinically useful genomic-EHR integration pipelines. Additional testing will be a critical step towards the clinical validation of this utility, enabling it to be integrated in a variety of real world data flow scenarios. For now, we propose the use of this utility primarily to accelerate FHIR Genomics understanding and to facilitate experimentation with further integration of genomics data into the EHR.
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Affiliation(s)
- Robert H Dolin
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA.
| | - Shaileshbhai R Gothi
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Aziz Boxwala
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | | | - Ammar Husami
- Division of Human Genetics, Cincinnati Children's Hospital Medical Center and Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - James Jones
- Computational Health Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | - Himanshu Khangar
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | - Shubham Londhe
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | | | - Soujanya Rao
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | - Barbara Rapchak
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | - James Shalaby
- Elimu Informatics, 1160 Brickyard Cove Rd Ste 200, Richmond, CA, 94801-4173, USA
| | | | - Ning Xie
- Biomedical Cybernetics Laboratory, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Srikar Chamala
- Department of Pathology, Immunology and Laboratory Medicine, University of Florida, Gainesville, FL, USA
| | - Gil Alterovitz
- Brigham and Women's Hospital, Boston, MA, USA.,Harvard/MIT Division of Health Sciences and Technology, Harvard Medical School, Boston, MA, USA
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12
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Alrahbi DA, Khan M, Gupta S, Modgil S, Chiappetta Jabbour CJ. Challenges for developing health-care knowledge in the digital age. JOURNAL OF KNOWLEDGE MANAGEMENT 2020. [DOI: 10.1108/jkm-03-2020-0224] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Purpose
Health-care knowledge is dispersed among different departments in a health care organization, which makes it difficult at times to provide quality care services to patients. Therefore, this study aims to identify the main challenges in adopting health information technology (HIT).
Design/methodology/approach
This study surveyed 148 stakeholders in 4 key categories [patients, health-care providers, United Arab Emirates (UAE) citizens and foresight experts] to identify the challenges they face in adopting health care technologies. Responses were analyzed using exploratory (EFA) and confirmatory factor analysis (CFA).
Findings
EFA revealed four key latent factors predicting resistance to HIT adoption, namely, organizational strategy (ORGS); technical barriers; readiness for big data and the internet of things (IoT); and orientation (ORI). ORGS accounted for the greatest amount of variance. CFA indicated that readiness for big data and the IoT was only moderately correlated with HIT adoption, but the other three factors were strongly correlated. Specific items relating to cost, the effectiveness and usability of the technology and the organization were strongly correlated with HIT adoption. These results indicate that, in addition to financial considerations, effective HIT adoption requires ensuring that technologies will be easy to implement to ensure their long-term use.
Research limitations/implications
The results indicate that readiness for big data and the IoT-related infrastructure poses a challenge to HIT adoption in the UAE context. Respondents believed that the infrastructure of big data can be helpful in more efficiently storing and sharing health-care information. On the technological side, respondents felt that they may experience a steep learning curve. Regarding ORI, stakeholders expected many more such initiatives from health-care providers to make it more knowledge-specific and proactive.
Practical implications
This study has implications for knowledge management in the health -care sector for information technologies. The HIT can help firms in creating a knowledge eco-system, which is not possible in a dispersed knowledge environment. The utilization of the knowledge base that emerged from the practices and data can help the health care sector to set new standards of information flow and other clinical services such as monitoring the self-health condition. The HIT can further influence the actions of the pharmaceutical and medical device industry.
Originality/value
This paper highlights the challenges in HIT adoption and the most prominent factors. The conceptual model was empirically tested after the collection of primary data from the UAE using stakeholder theory.
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13
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Karimi S, Jiang X, Dolin RH, Kim M, Boxwala A. A secure system for genomics clinical decision support. J Biomed Inform 2020; 112:103602. [PMID: 33080397 PMCID: PMC8577277 DOI: 10.1016/j.jbi.2020.103602] [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: 05/16/2020] [Revised: 09/07/2020] [Accepted: 10/12/2020] [Indexed: 11/26/2022]
Abstract
We developed a prototype genomic archiving and communications system to securely store genome data and provide clinical decision support (CDS). This system operates on a client-server model. The client encrypts the data, and the server stores data and performs the computations necessary for CDS. Computations are directly performed on encrypted data, and the client decrypts results. The server cannot decrypt inputs or outputs, which provides strong guarantees of security. We have validated our system with three genomics-based CDS applications. The results demonstrate that it is possible to resolve a long-standing dilemma in genomic data privacy and accessibility, by using a principled cryptographical framework and a mathematical representation of genome data and CDS questions.
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Affiliation(s)
| | - Xiaoqian Jiang
- UT Health School of Biomedical Informatics, Houston, TX, United States
| | | | - Miran Kim
- UT Health School of Biomedical Informatics, Houston, TX, United States
| | - Aziz Boxwala
- Elimu Informatics Inc., Richmond, CA, United States
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14
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Thomas M, Sakoda LC, Hoffmeister M, Rosenthal EA, Lee JK, van Duijnhoven FJB, Platz EA, Wu AH, Dampier CH, de la Chapelle A, Wolk A, Joshi AD, Burnett-Hartman A, Gsur A, Lindblom A, Castells A, Win AK, Namjou B, Van Guelpen B, Tangen CM, He Q, Li CI, Schafmayer C, Joshu CE, Ulrich CM, Bishop DT, Buchanan DD, Schaid D, Drew DA, Muller DC, Duggan D, Crosslin DR, Albanes D, Giovannucci EL, Larson E, Qu F, Mentch F, Giles GG, Hakonarson H, Hampel H, Stanaway IB, Figueiredo JC, Huyghe JR, Minnier J, Chang-Claude J, Hampe J, Harley JB, Visvanathan K, Curtis KR, Offit K, Li L, Le Marchand L, Vodickova L, Gunter MJ, Jenkins MA, Slattery ML, Lemire M, Woods MO, Song M, Murphy N, Lindor NM, Dikilitas O, Pharoah PDP, Campbell PT, Newcomb PA, Milne RL, MacInnis RJ, Castellví-Bel S, Ogino S, Berndt SI, Bézieau S, Thibodeau SN, Gallinger SJ, Zaidi SH, Harrison TA, Keku TO, Hudson TJ, Vymetalkova V, Moreno V, Martín V, Arndt V, Wei WQ, Chung W, Su YR, Hayes RB, White E, Vodicka P, Casey G, Gruber SB, Schoen RE, Chan AT, Potter JD, Brenner H, Jarvik GP, Corley DA, Peters U, Hsu L. Genome-wide Modeling of Polygenic Risk Score in Colorectal Cancer Risk. Am J Hum Genet 2020; 107:432-444. [PMID: 32758450 PMCID: PMC7477007 DOI: 10.1016/j.ajhg.2020.07.006] [Citation(s) in RCA: 101] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2019] [Accepted: 07/13/2020] [Indexed: 02/08/2023] Open
Abstract
Accurate colorectal cancer (CRC) risk prediction models are critical for identifying individuals at low and high risk of developing CRC, as they can then be offered targeted screening and interventions to address their risks of developing disease (if they are in a high-risk group) and avoid unnecessary screening and interventions (if they are in a low-risk group). As it is likely that thousands of genetic variants contribute to CRC risk, it is clinically important to investigate whether these genetic variants can be used jointly for CRC risk prediction. In this paper, we derived and compared different approaches to generating predictive polygenic risk scores (PRS) from genome-wide association studies (GWASs) including 55,105 CRC-affected case subjects and 65,079 control subjects of European ancestry. We built the PRS in three ways, using (1) 140 previously identified and validated CRC loci; (2) SNP selection based on linkage disequilibrium (LD) clumping followed by machine-learning approaches; and (3) LDpred, a Bayesian approach for genome-wide risk prediction. We tested the PRS in an independent cohort of 101,987 individuals with 1,699 CRC-affected case subjects. The discriminatory accuracy, calculated by the age- and sex-adjusted area under the receiver operating characteristics curve (AUC), was highest for the LDpred-derived PRS (AUC = 0.654) including nearly 1.2 M genetic variants (the proportion of causal genetic variants for CRC assumed to be 0.003), whereas the PRS of the 140 known variants identified from GWASs had the lowest AUC (AUC = 0.629). Based on the LDpred-derived PRS, we are able to identify 30% of individuals without a family history as having risk for CRC similar to those with a family history of CRC, whereas the PRS based on known GWAS variants identified only top 10% as having a similar relative risk. About 90% of these individuals have no family history and would have been considered average risk under current screening guidelines, but might benefit from earlier screening. The developed PRS offers a way for risk-stratified CRC screening and other targeted interventions.
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Affiliation(s)
- Minta Thomas
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Lori C Sakoda
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Michael Hoffmeister
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Elisabeth A Rosenthal
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA
| | - Jeffrey K Lee
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Franzel J B van Duijnhoven
- Division of Human Nutrition and Health, Wageningen University & Research, Wageningen 176700, the Netherlands
| | - Elizabeth A Platz
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Anna H Wu
- University of Southern California, Preventative Medicine, Los Angeles, CA 90089, USA
| | - Christopher H Dampier
- Department of Surgery, University of Virginia Health System, Charlottesville, VA 22903, USA
| | - Albert de la Chapelle
- Department of Cancer Biology and Genetics and the Comprehensive Cancer Center, The Ohio State University, Columbus, OH 43210, USA
| | - Alicja Wolk
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm 17177, Sweden
| | - Amit D Joshi
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | | | - Andrea Gsur
- Institute of Cancer Research, Department of Medicine I, Medical University Vienna, Vienna 1090, Austria
| | - Annika Lindblom
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm 17177, Sweden; Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm 17177, Sweden
| | - Antoni Castells
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona 08007, Spain
| | - Aung Ko Win
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Cincinnati VA Medical Center, Cincinnati, OH 45229, USA
| | - Bethany Van Guelpen
- Department of Radiation Sciences, Oncology Unit, Umeå University, Umeå 90187, Sweden; Wallenberg Centre for Molecular Medicine, Umeå University, Umeå 90187, Sweden
| | - Catherine M Tangen
- SWOG Statistical Center, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Qianchuan He
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Christopher I Li
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Clemens Schafmayer
- Department of General Surgery, University Hospital Rostock, Rostock 18051, Germany
| | - Corinne E Joshu
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Cornelia M Ulrich
- Huntsman Cancer Institute and Department of Population Health Sciences, University of Utah, Salt Lake City, UT 84112, USA
| | - D Timothy Bishop
- Leeds Institute of Cancer and Pathology, University of Leeds, Leeds LS2 9JT, UK
| | - Daniel D Buchanan
- University of Melbourne Centre for Cancer Research, Victorian Comprehensive Cancer Centre, Parkville, VIC 3010, Australia; Colorectal Oncogenomics Group, Department of Clinical Pathology, The University of Melbourne, Parkville, VIC 3010, Australia; Genomic Medicine and Family Cancer Clinic, Royal Melbourne Hospital, Parkville, VIC 3010, Australia
| | - Daniel Schaid
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55905, USA
| | - David A Drew
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - David C Muller
- School of Public Health, Imperial College London, London SW7 2AZ, UK
| | - David Duggan
- Translational Genomics Research Institute - An Affiliate of City of Hope, Phoenix, AZ 85003, USA
| | - David R Crosslin
- Department of Bioinformatics and Medical Education, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Demetrius Albanes
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Edward L Giovannucci
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02108, USA
| | - Eric Larson
- Kaiser Permanente Washington Research Institute, Seattle, WA 98101, USA
| | - Flora Qu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Frank Mentch
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Graham G Giles
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA 19104, USA
| | - Heather Hampel
- Division of Human Genetics, Department of Internal Medicine, The Ohio State University Comprehensive Cancer Center, Columbus, OH 43210, USA
| | - Ian B Stanaway
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA
| | - Jane C Figueiredo
- Department of Medicine, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA; Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90033, USA
| | - Jeroen R Huyghe
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Jessica Minnier
- School of Public Health, Oregon Health & Science University, Portland, OR 97239, USA
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, 69120 Germany; University Medical Centre Hamburg-Eppendorf, University Cancer Centre Hamburg (UCCH), Hamburg 20246, Germany
| | - Jochen Hampe
- Department of Medicine I, University Hospital Dresden, Technische Universität Dresden (TU Dresden), Dresden 01062, Germany
| | - John B Harley
- Center for Autoimmune Genomics and Etiology (CAGE), Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45229, USA; University of Cincinnati College of Medicine, Cincinnati, OH 45229, USA; Cincinnati VA Medical Center, Cincinnati, OH 45229, USA
| | - Kala Visvanathan
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, and the Sidney Kimmel Comprehensive Cancer Center at Johns Hopkins, Baltimore, MD 21287, USA
| | - Keith R Curtis
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Kenneth Offit
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY 10021, USA; Department of Medicine, Weill Cornell Medical College, NY 10065, USA
| | - Li Li
- Department of Family Medicine, University of Virginia, Charlottesville, VA 22903, USA
| | | | - Ludmila Vodickova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Marc J Gunter
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Mark A Jenkins
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia
| | - Martha L Slattery
- Department of Internal Medicine, University of Utah, Salt Lake City, UT 84132, USA
| | - Mathieu Lemire
- PanCuRx Translational Research Initiative, Ontario, Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Michael O Woods
- Memorial University of Newfoundland, Discipline of Genetics, St. John's, NL A1B 3R7, Canada
| | - Mingyang Song
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Department of Nutrition, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - Neil Murphy
- Nutrition and Metabolism Section, International Agency for Research on Cancer, World Health Organization, Lyon 69372, France
| | - Noralane M Lindor
- Department of Health Science Research, Mayo Clinic, Scottsdale, AZ 85260, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN 55905, USA
| | - Paul D P Pharoah
- Department of Public Health and Primary Care, University of Cambridge, Cambridge CB2 0SR, UK
| | - Peter T Campbell
- Behavioral and Epidemiology Research Group, American Cancer Society, Atlanta, GA 30303, USA
| | - Polly A Newcomb
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; School of Public Health, University of Washington, Seattle, WA 98195, USA
| | - Roger L Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia; Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia
| | - Robert J MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3000, Australia; Cancer Epidemiology Division, Cancer Council Victoria, 615 St Kilda Road, Melbourne, VIC 3004, Australia
| | - Sergi Castellví-Bel
- Gastroenterology Department, Hospital Clínic, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas (CIBEREHD), University of Barcelona, Barcelona 08007, Spain
| | - Shuji Ogino
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Program in MPE Molecular Pathological Epidemiology, Department of Pathology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115, USA; Department of Oncologic Pathology, Dana-Farber Cancer Institute, Boston, MA 02215, USA
| | - Sonja I Berndt
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Stéphane Bézieau
- Service de Génétique Médicale, Centre Hospitalier Universitaire (CHU) Nantes, Nantes 44093, France
| | - Stephen N Thibodeau
- Division of Laboratory Genetics, Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN 85054, USA
| | - Steven J Gallinger
- Lunenfeld Tanenbaum Research Institute, Mount Sinai Hospital, University of Toronto, Toronto, ON M5G1X5, Canada
| | - Syed H Zaidi
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Tabitha A Harrison
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Temitope O Keku
- Center for Gastrointestinal Biology and Disease, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Thomas J Hudson
- Ontario Institute for Cancer Research, Toronto, ON M5G0A3, Canada
| | - Veronika Vymetalkova
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Victor Moreno
- Oncology Data Analytics Program, Catalan Institute of Oncology, L'Hospitalet de Llobregat, Barcelona 08908, Spain; CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain; Department of Clinical Sciences, Faculty of Medicine, University of Barcelona, Barcelona 08907, Spain; ONCOBEL Program, Bellvitge Biomedical Research Institute (IDIBELL), L'Hospitalet de Llobregat, Barcelona 08908, Spain
| | - Vicente Martín
- CIBER Epidemiología y Salud Pública (CIBERESP), Madrid 28029, Spain; Biomedicine Institute (IBIOMED), University of León, León 24071, Spain
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA
| | - Wendy Chung
- Office of Research & Development, Department of Veterans Affairs, Washington, DC 20420, USA; Departments of Pediatrics and Medicine, Columbia University Medical Center, New York, NY 10032, USA
| | - Yu-Ru Su
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA
| | - Richard B Hayes
- Division of Epidemiology, Department of Population Health, New York University School of Medicine, New York, NY 10016, USA
| | - Emily White
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Pavel Vodicka
- Department of Molecular Biology of Cancer, Institute of Experimental Medicine of the Czech Academy of Sciences, 142 20 Prague 4, Czech Republic; Institute of Biology and Medical Genetics, First Faculty of Medicine, Charles University, 128 00 Prague, Czech Republic; Faculty of Medicine and Biomedical Center in Pilsen, Charles University, 323 00 Pilsen, Czech Republic
| | - Graham Casey
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22903, USA
| | - Stephen B Gruber
- Department of Preventive Medicine, USC Norris Comprehensive Cancer Center, Keck School of Medicine, University of Southern California, Los Angeles, CA 90089, USA
| | - Robert E Schoen
- Department of Medicine and Epidemiology, University of Pittsburgh Medical Center, Pittsburgh, PA 15219, USA
| | - Andrew T Chan
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA; Division of Gastroenterology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA; Broad Institute of Harvard and MIT, Cambridge, MA 02141, USA; Department of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health, Harvard University, Boston, MA 02115, USA
| | - John D Potter
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Centre for Public Health Research, Massey University, Wellington 6140, New Zealand
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg 69120, Germany; German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg 69120, Germany
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington Medical Center, Seattle, WA 98195, USA; Genome Sciences, University of Washington Medical Center, Seattle, WA 98195, USA
| | - Douglas A Corley
- Division of Research, Kaiser Permanente Northern California, Oakland, CA 94612, USA
| | - Ulrike Peters
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Epidemiology, University of Washington, Seattle, WA 98195, USA.
| | - Li Hsu
- Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA; Department of Biostatistics, University of Washington, Seattle, WA 98195, USA.
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15
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Hoffman JM, Flynn AJ, Juskewitch JE, Freimuth RR. Biomedical Data Science and Informatics Challenges to Implementing Pharmacogenomics with Electronic Health Records. Annu Rev Biomed Data Sci 2020. [DOI: 10.1146/annurev-biodatasci-020320-093614] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Pharmacogenomic information must be incorporated into electronic health records (EHRs) with clinical decision support in order to fully realize its potential to improve drug therapy. Supported by various clinical knowledge resources, pharmacogenomic workflows have been implemented in several healthcare systems. Little standardization exists across these efforts, however, which limits scalability both within and across clinical sites. Limitations in information standards, knowledge management, and the capabilities of modern EHRs remain challenges for the widespread use of pharmacogenomics in the clinic, but ongoing efforts are addressing these challenges. Although much work remains to use pharmacogenomic information more effectively within clinical systems, the experiences of pioneering sites and lessons learned from those programs may be instructive for other clinical areas beyond genomics. We present a vision of what can be achieved as informatics and data science converge to enable further adoption of pharmacogenomics in the clinic.
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Affiliation(s)
- James M. Hoffman
- Department of Pharmaceutical Sciences and the Office of Quality and Patient Care, St. Jude Children's Research Hospital, Memphis, Tennessee 38105, USA
| | - Allen J. Flynn
- Department of Learning Health Sciences, Medical School, University of Michigan, Ann Arbor, Michigan 48109, USA
| | - Justin E. Juskewitch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, Minnesota 55905, USA
| | - Robert R. Freimuth
- Division of Digital Health Sciences, Department of Health Sciences Research, Center for Individualized Medicine, and Information and Knowledge Management, Mayo Clinic, Rochester, Minnesota 55905, USA
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16
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Prows CA, Marsolo K, Myers MF, Nix J, Hall ES. Adapting Clinical Systems to Enable Adolescents' Genomic Choices. ACI OPEN 2020; 4:e126-e131. [PMID: 36177089 PMCID: PMC9518747 DOI: 10.1055/s-0040-1718747] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
BACKGROUND We offered adolescents personalized choices about the type of genetic results they wanted to learn during a research study and created a workflow to filter and transfer the results to the electronic health record (EHR). METHODS We describe adaptations needed to ensure that adolescents' results documented in the EHR and returned to adolescent/parent dyads matched their choices. A web application enabled manual modification of the underlying laboratory report data based on adolescents' choices. The final PDF format of the laboratory reports was not viewable through the EHR patient portal, so an EHR form was created to support the manual entry of discrete results that could be viewed in the portal. RESULTS Enabling adolescents' choices about genetic results was a labor-intensive process. More than 350 hours was required for development of the application and EHR form, as well as over 50 hours of a study professional's time to enter choices into the application and EHR. Adolescents and their parents who learned genetic results through the patient portal indicated that they were satisfied with the method of return and would make their choices again if given the option. CONCLUSION Although future EHR upgrades are expected to enable patient portal access to PDFs, additional improvements are needed to allow the results to be partitioned and filtered based on patient preferences. Furthermore, separating these results into more discrete components will allow them to be stored separately in the EHR, supporting the use of these data in clinical decision support or artificial intelligence applications.
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Affiliation(s)
- Cynthia A. Prows
- Divisions of Human Genetics and Patient Services, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States
| | - Keith Marsolo
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina, United States
| | - Melanie F. Myers
- Division of Human Genetics, Cincinnati Children’s Hospital Medical Center; College of Medicine, University of Cincinnati, Cincinnati, Ohio, United States
| | - Jeremy Nix
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States
| | - Eric S. Hall
- Division of Biomedical Informatics, Cincinnati Children’s Hospital Medical Center, Cincinnati, Ohio, United States
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17
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Watkins M, Eilbeck K. FHIR Lab Reports: using SMART on FHIR and CDS Hooks to increase the clinical utility of pharmacogenomic laboratory test results. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2020; 2020:683-692. [PMID: 32477691 PMCID: PMC7233102] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Laboratory tests are a common aspect of clinical care and are the primary source of clinical genomic data. However, most laboratories use PDF documents to store and exchange the results of these tests. This locks the data into a static format and leaves the results only human-readable. The ordering clinician uses the results, but after that the information is unlikely to be used again. Future use would require a clinician to know that the test was performed, know where to find the PDF report, and take the time to open it and determine relevance to that future scenario. New computational standards such as SMART on FHIR and CDS Hooks present opportunities to better utilize these results, both physically upon receipt and asynchronously in future clinical encounters for that patient. Full app available at https://github.com/mwatkin8/FHIR-Lab-Reports-App. Demo available at http://hematite.genetics.utah.edu/FHIR-Lab-Reports/.
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Affiliation(s)
- Michael Watkins
- The Department of Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Karen Eilbeck
- The Department of Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
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18
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Watkins M, Rynearson S, Henrie A, Eilbeck K. Implementing the VMC Specification to Reduce Ambiguity in Genomic Variant Representation. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:1226-1235. [PMID: 32308920 PMCID: PMC7153148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Current methods used for representing biological sequence variants allow flexibility, which has created redundancy within variant archives and discordance among variant representation tools. While research methodologies have been able to adapt to this ambiguity, strict clinical standards make it difficult to use this data in what would otherwise be useful clinical interventions. We implemented a specification developed by the GA4GH Variant Modeling Collaboration (VMC), which details a new approach to unambiguous representation of variants at the allelic level, as a haplotype, or as a genotype. Our implementation, called the VMC Test Suite (http://vcfclin.org), offers web tools to generate and insert VMC identifiers into a VCF file and to generate a VMC bundle JSON representation of a VCF file or HGVS expression. A command line tool with similar functionality is also introduced. These tools facilitate use of this standard-an important step toward reliable querying of variants and their associated annotations.
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Affiliation(s)
- Michael Watkins
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Shawn Rynearson
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Alex Henrie
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
| | - Karen Eilbeck
- Biomedical Informatics, 421 Wakara Way, University of Utah, Salt Lake City, Utah 84108
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19
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Dexter P, Ong H, Elsey A, Bell G, Walton N, Chung W, Rasmussen L, Hicks K, Owusu-Obeng A, Scott S, Ellis S, Peterson J. Development of a Genomic Data Flow Framework: Results of a Survey Administered to NIH-NHGRI IGNITE and eMERGE Consortia Participants. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:363-370. [PMID: 32308829 PMCID: PMC7153090] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Precision health's more individualized molecular approach will enrich our understanding of disease etiology and patient outcomes. Universal implementation of precision health will not be feasible, however, until there is much greater automation of processes related to genomic data transmission, transformation, and interpretation. In this paper, we describe a framework for genomic data flow developed by the Clinical Informatics Work Group of the NIH National Human Genome Research Institute (NHGRI) IGNITE Network consortium. We subsequently report the results of a genomic data flow survey administered to sites funded by NIH-NHGRI for large scale genomic medicine implementations. Finally, we discuss insights and challenges identified through these survey results as they relate to both the current and a desirable future state of genomic data flow.
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Affiliation(s)
- Paul Dexter
- Indiana University School of Medicine, Indianapolis, IN
- Regenstrief Institute, Inc., Indianapolis, IN
| | - Henry Ong
- Vanderbilt University Medical Center, Nashville, TN
| | | | | | | | | | | | | | | | - Stuart Scott
- Icahn School of Medicine at Mount Sinai, New York, NY
| | - Steve Ellis
- Icahn School of Medicine at Mount Sinai, New York, NY
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20
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Johnson KB, Clayton EW, Starren J, Peterson J. The Implementation Chasm Hindering Genome-informed Health Care. THE JOURNAL OF LAW, MEDICINE & ETHICS : A JOURNAL OF THE AMERICAN SOCIETY OF LAW, MEDICINE & ETHICS 2020; 48:119-125. [PMID: 32342791 PMCID: PMC7395963 DOI: 10.1177/1073110520916999] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
The promises of precision medicine are often heralded in the medical and lay literature, but routine integration of genomics in clinical practice is still limited. While the "last mile' infrastructure to bring genomics to the bedside has been demonstrated in some healthcare settings, a number of challenges remain - both in the receptivity of today's health system and in its technical and educational readiness to respond to this evolution in care. To improve the impact of genomics on health and disease management, we will need to integrate both new knowledge and new care processes into existing workflows. This change will be onerous and time-consuming, but hopefully valuable to the provision of high quality, economically feasible care worldwide.
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Affiliation(s)
- Kevin B Johnson
- Kevin B. Johnson, M.D., M.S., is Cornelius Vanderbilt Professor and Chair of Biomedical Informatics, with a joint appointment in the Department of Pediatrics at Vanderbilt University Medical Center. He received his M.D. from Johns Hopkins Hospital in Baltimore and his M.S. in Medical Informatics from Stanford University in 1992. Ellen Wright Clayton, M.D., J.D., is the Craig-Weaver Professor of Pediatrics, Professor of Health Policy in the Center for Biomedical Ethics and Society at Vanderbilt University Medical Center, and Professor of Law at Vanderbilt University. She has been studying the ethical, legal, and social implications of genetics research and its translation to the clinic for many years. She is currently a PI of LawSeq as well as GetPreCiSe, a Center of Excellence in ELSI Research focused on genetic privacy and identity, and has been an investigator in the eMERGE Network since its inception. Justin Starren, M.D., M.S., Ph.D., is Professor of Preventive Medicine and Medical Social Sciences and Chief of the Division of Health and Biomedical Informatics at the Northwestern University Feinberg School of Medicine. He received his M.D. and M.S. in Immunogenetics from Washington University in St. Louis in 1987, and his Ph.D. in Biomedical Informatics from Columbia University in 1997. Josh Peterson, M.D., M.P.H., is an Associate Professor of Biomedical Informatics and Medicine at Vanderbilt University Medical Center. He received his M.D. from Vanderbilt University in 1997 and his M.P.H. from Harvard University School of Public Health in 2002
| | - Ellen Wright Clayton
- Kevin B. Johnson, M.D., M.S., is Cornelius Vanderbilt Professor and Chair of Biomedical Informatics, with a joint appointment in the Department of Pediatrics at Vanderbilt University Medical Center. He received his M.D. from Johns Hopkins Hospital in Baltimore and his M.S. in Medical Informatics from Stanford University in 1992. Ellen Wright Clayton, M.D., J.D., is the Craig-Weaver Professor of Pediatrics, Professor of Health Policy in the Center for Biomedical Ethics and Society at Vanderbilt University Medical Center, and Professor of Law at Vanderbilt University. She has been studying the ethical, legal, and social implications of genetics research and its translation to the clinic for many years. She is currently a PI of LawSeq as well as GetPreCiSe, a Center of Excellence in ELSI Research focused on genetic privacy and identity, and has been an investigator in the eMERGE Network since its inception. Justin Starren, M.D., M.S., Ph.D., is Professor of Preventive Medicine and Medical Social Sciences and Chief of the Division of Health and Biomedical Informatics at the Northwestern University Feinberg School of Medicine. He received his M.D. and M.S. in Immunogenetics from Washington University in St. Louis in 1987, and his Ph.D. in Biomedical Informatics from Columbia University in 1997. Josh Peterson, M.D., M.P.H., is an Associate Professor of Biomedical Informatics and Medicine at Vanderbilt University Medical Center. He received his M.D. from Vanderbilt University in 1997 and his M.P.H. from Harvard University School of Public Health in 2002
| | - Justin Starren
- Kevin B. Johnson, M.D., M.S., is Cornelius Vanderbilt Professor and Chair of Biomedical Informatics, with a joint appointment in the Department of Pediatrics at Vanderbilt University Medical Center. He received his M.D. from Johns Hopkins Hospital in Baltimore and his M.S. in Medical Informatics from Stanford University in 1992. Ellen Wright Clayton, M.D., J.D., is the Craig-Weaver Professor of Pediatrics, Professor of Health Policy in the Center for Biomedical Ethics and Society at Vanderbilt University Medical Center, and Professor of Law at Vanderbilt University. She has been studying the ethical, legal, and social implications of genetics research and its translation to the clinic for many years. She is currently a PI of LawSeq as well as GetPreCiSe, a Center of Excellence in ELSI Research focused on genetic privacy and identity, and has been an investigator in the eMERGE Network since its inception. Justin Starren, M.D., M.S., Ph.D., is Professor of Preventive Medicine and Medical Social Sciences and Chief of the Division of Health and Biomedical Informatics at the Northwestern University Feinberg School of Medicine. He received his M.D. and M.S. in Immunogenetics from Washington University in St. Louis in 1987, and his Ph.D. in Biomedical Informatics from Columbia University in 1997. Josh Peterson, M.D., M.P.H., is an Associate Professor of Biomedical Informatics and Medicine at Vanderbilt University Medical Center. He received his M.D. from Vanderbilt University in 1997 and his M.P.H. from Harvard University School of Public Health in 2002
| | - Josh Peterson
- Kevin B. Johnson, M.D., M.S., is Cornelius Vanderbilt Professor and Chair of Biomedical Informatics, with a joint appointment in the Department of Pediatrics at Vanderbilt University Medical Center. He received his M.D. from Johns Hopkins Hospital in Baltimore and his M.S. in Medical Informatics from Stanford University in 1992. Ellen Wright Clayton, M.D., J.D., is the Craig-Weaver Professor of Pediatrics, Professor of Health Policy in the Center for Biomedical Ethics and Society at Vanderbilt University Medical Center, and Professor of Law at Vanderbilt University. She has been studying the ethical, legal, and social implications of genetics research and its translation to the clinic for many years. She is currently a PI of LawSeq as well as GetPreCiSe, a Center of Excellence in ELSI Research focused on genetic privacy and identity, and has been an investigator in the eMERGE Network since its inception. Justin Starren, M.D., M.S., Ph.D., is Professor of Preventive Medicine and Medical Social Sciences and Chief of the Division of Health and Biomedical Informatics at the Northwestern University Feinberg School of Medicine. He received his M.D. and M.S. in Immunogenetics from Washington University in St. Louis in 1987, and his Ph.D. in Biomedical Informatics from Columbia University in 1997. Josh Peterson, M.D., M.P.H., is an Associate Professor of Biomedical Informatics and Medicine at Vanderbilt University Medical Center. He received his M.D. from Vanderbilt University in 1997 and his M.P.H. from Harvard University School of Public Health in 2002
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21
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Rasmussen LV, Smith ME, Almaraz F, Persell SD, Rasmussen-Torvik LJ, Pacheco JA, Chisholm RL, Christensen C, Herr TM, Wehbe FH, Starren JB. An ancillary genomics system to support the return of pharmacogenomic results. J Am Med Inform Assoc 2020; 26:306-310. [PMID: 30778576 DOI: 10.1093/jamia/ocy187] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/13/2017] [Revised: 10/15/2018] [Accepted: 12/20/2018] [Indexed: 01/26/2023] Open
Abstract
Existing approaches to managing genetic and genomic test results from external laboratories typically include filing of text reports within the electronic health record, making them unavailable in many cases for clinical decision support. Even when structured computable results are available, the lack of adopted standards requires considerations for processing the results into actionable knowledge, in addition to storage and management of the data. Here, we describe the design and implementation of an ancillary genomics system used to receive and process heterogeneous results from external laboratories, which returns a descriptive phenotype to the electronic health record in support of pharmacogenetic clinical decision support.
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Affiliation(s)
- Luke V Rasmussen
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Maureen E Smith
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL.,Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Federico Almaraz
- Department of Information Technology, Northwestern Memorial HealthCare, Chicago, IL
| | - Stephen D Persell
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Carl Christensen
- Department of Information Technology, Northwestern Memorial HealthCare, Chicago, IL.,Department of Information Technology, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Timothy M Herr
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Firas H Wehbe
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Justin B Starren
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
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22
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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] [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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Morgan S, Hanna J, Yousef GM. Knowledge Translation in Oncology: The Bumpy Ride From Bench to Bedside. Am J Clin Pathol 2020; 153:5-13. [PMID: 31836881 DOI: 10.1093/ajcp/aqz099] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
OBJECTIVES Knowledge translation (KT) is the dynamic process of mobilizing best-practice evidence to guide health care decisions. METHODS Using a PubMed search, challenges were identified and milestones defined. RESULTS Substantial challenges exist in integrating discoveries into patient care, including technical limitations related to genomic testing like turnaround time, standardization, reproducibility, and results interpretation. Other challenges include lack of proper training in genetic counseling for health care providers, clarity of scientific evidence, and ethical, legal and social considerations. In addition, most health care systems lack accessibility to genetic testing services. Moving forward, KT should be addressed at three main frontiers. The first is patients centered for proper understanding and decision making; the second is directed toward health care professionals, including clinical decision support and clarity of roles; and the third addresses resources of health care systems. CONCLUSIONS Implementing KT requires developing strategies to enhance awareness and promote behavioral changes congruent with research evidence, designing a systematic approach by health care providers and stakeholders to achieve patient-centered care.
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Affiliation(s)
- Sarah Morgan
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Canada
| | - Jessica Hanna
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Canada
| | - George M Yousef
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
- Department of Paediatric Laboratory Medicine, Hospital for Sick Children, Toronto, Canada
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Aronson S, Babb L, Ames D, Gibbs RA, Venner E, Connelly JJ, Marsolo K, Weng C, Williams MS, Hartzler AL, Liang WH, Ralston JD, Devine EB, Murphy S, Chute CG, Caraballo PJ, Kullo IJ, Freimuth RR, Rasmussen LV, Wehbe FH, Peterson JF, Robinson JR, Wiley K, Overby Taylor C. Empowering genomic medicine by establishing critical sequencing result data flows: the eMERGE example. J Am Med Inform Assoc 2019; 25:1375-1381. [PMID: 29860405 DOI: 10.1093/jamia/ocy051] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2017] [Accepted: 04/18/2018] [Indexed: 11/14/2022] Open
Abstract
The eMERGE Network is establishing methods for electronic transmittal of patient genetic test results from laboratories to healthcare providers across organizational boundaries. We surveyed the capabilities and needs of different network participants, established a common transfer format, and implemented transfer mechanisms based on this format. The interfaces we created are examples of the connectivity that must be instantiated before electronic genetic and genomic clinical decision support can be effectively built at the point of care. This work serves as a case example for both standards bodies and other organizations working to build the infrastructure required to provide better electronic clinical decision support for clinicians.
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Affiliation(s)
- Samuel Aronson
- Research Information Science and Computing, Partners HealthCare, Boston, Massachusetts, USA.,Partners Personalized Medicine, Partners HealthCare, Boston Massachusetts, USA
| | - Lawrence Babb
- Mitogen-GeneInsight, Sunquest Information Systems, Boston, Massachusetts, USA
| | - Darren Ames
- DNAnexus, Inc., Mountain View, California, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - Eric Venner
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, Texas, USA
| | - John J Connelly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania, USA
| | - Keith Marsolo
- Division of Biomedical Informatics, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.,Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | - Chunhua Weng
- Department of Biomedical Informatics, Columbia University, New York, New York, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA
| | - Andrea L Hartzler
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Wayne H Liang
- Informatics Institute, University of Alabama at Birmingham, Birmingham, Alabama, USA.,Department of Pediatrics, University of Alabama at Birmingham, Birmingham, Alabama, USA
| | - James D Ralston
- Kaiser Permanente Washington Health Research Institute, Seattle, Washington, USA.,Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Emily Beth Devine
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, Washington, USA
| | - Shawn Murphy
- Research Information Science and Computing, Partners HealthCare, Boston, Massachusetts, USA
| | | | | | - Iftikhar J Kullo
- Department of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University, Chicago, Illinois, USA
| | - Firas H Wehbe
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University, Chicago, Illinois, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Jamie R Robinson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Ken Wiley
- National Human Genome Research Institute, Bethesda, Maryland, USA
| | - Casey Overby Taylor
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania, USA.,Department of Medicine, Johns Hopkins University, Baltimore, Maryland, USA
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25
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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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26
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Nass SJ, Cohen MB, Nayar R, Zutter MM, Balogh EP, Schilsky RL, Hricak H, Elenitoba-Johnson KSJ. Improving Cancer Diagnosis and Care: Patient Access to High-Quality Oncologic Pathology. Oncologist 2019; 24:1287-1290. [PMID: 31366725 PMCID: PMC6795152 DOI: 10.1634/theoncologist.2019-0261] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/31/2019] [Indexed: 11/18/2022] Open
Abstract
Drawing on discussions at a workshop hosted by the National Cancer Policy Forum, current challenges in pathology are reviewed and practical steps to facilitate high‐quality cancer diagnosis and care through improved patient access to expertise in oncologic pathology are highlighted
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Affiliation(s)
- Sharyl J Nass
- Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine, Washington, District of Columbia, USA
| | - Michael B Cohen
- Department of Pathology, Wake Forest School of Medicine, Winston-Salem, North Carolina, USA
| | - Ritu Nayar
- Department of Pathology, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Mary M Zutter
- Department of Pathology, Microbiology, and Immunology, Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Erin P Balogh
- Health and Medicine Division, National Academies of Sciences, Engineering, and Medicine, Washington, District of Columbia, USA
| | | | - Hedvig Hricak
- Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, New York, USA
| | - Kojo S J Elenitoba-Johnson
- Department of Pathology and Laboratory Medicine, University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
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27
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Van den Wijngaert S, Bossuyt N, Ferns B, Busson L, Serrano G, Wautier M, Thomas I, Byott M, Dupont Y, Nastouli E, Hallin M, Kozlakidis Z, Vandenberg O. Bigger and Better? Representativeness of the Influenza A Surveillance Using One Consolidated Clinical Microbiology Laboratory Data Set as Compared to the Belgian Sentinel Network of Laboratories. Front Public Health 2019; 7:150. [PMID: 31275914 PMCID: PMC6591264 DOI: 10.3389/fpubh.2019.00150] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2018] [Accepted: 05/23/2019] [Indexed: 12/29/2022] Open
Abstract
Infectious diseases remain a serious public health concern globally, while the need for reliable and representative surveillance systems remains as acute as ever. The public health surveillance of infectious diseases uses reported positive results from sentinel clinical laboratories or laboratory networks, to survey the presence of specific microbial agents known to constitute a threat to public health in a given population. This monitoring activity is commonly based on a representative fraction of the microbiology laboratories nationally reporting to a single central reference point. However, in recent years a number of clinical microbiology laboratories (CML) have undergone a process of consolidation involving a shift toward laboratory amalgamation and closer real-time informational linkage. This report aims to investigate whether such merging activities might have a potential impact on infectious diseases surveillance. Influenza data was used from Belgian public health surveillance 2014–2017, to evaluate whether national infection trends could be estimated equally as effectively from only just one centralized CML serving the wider Brussels area (LHUB-ULB). The overall comparison reveals that there is a close correlation and representativeness of the LHUB-ULB data to the national and international data for the same time periods, both on epidemiological and molecular grounds. Notably, the effectiveness of the LHUB-ULB surveillance remains partially subject to local regional variations. A subset of the Influenza samples had their whole genome sequenced so that the observed epidemiological trends could be correlated to molecular observations from the same period, as an added-value proposition. These results illustrate that the real-time integration of high-throughput whole genome sequencing platforms available in consolidated CMLs into the public health surveillance system is not only credible but also advantageous to use for future surveillance and prediction purposes. This can be most effective when implemented for automatic detection systems that might include multiple layers of information and timely implementation of control strategies.
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Affiliation(s)
- Sigi Van den Wijngaert
- Department of Microbiology, LHUB-ULB, Pole Hospitalier Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Nathalie Bossuyt
- Sciensano, SD Epidemiology and Surveillance, Service 'Epidemiology of Infectious Diseases', Brussels, Belgium
| | - Bridget Ferns
- Department of Clinical Virology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.,UCLH/UCL Biomedical Research Centre, NIHR, London, United Kingdom
| | - Laurent Busson
- Department of Microbiology, LHUB-ULB, Pole Hospitalier Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Gabriela Serrano
- Research Centre on Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium
| | - Magali Wautier
- Department of Microbiology, LHUB-ULB, Pole Hospitalier Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | | | - Matthew Byott
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom
| | - Yves Dupont
- Sciensano, SD Epidemiology and Surveillance, Service 'Epidemiology of Infectious Diseases', Brussels, Belgium
| | - Eleni Nastouli
- Department of Clinical Virology, University College London Hospitals NHS Foundation Trust, London, United Kingdom.,Department of Population, Policy and Practice, Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Marie Hallin
- Department of Microbiology, LHUB-ULB, Pole Hospitalier Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
| | - Zisis Kozlakidis
- Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom.,International Agency for Research on Cancer, World Health Organization, Lyon, France
| | - Olivier Vandenberg
- Research Centre on Environmental and Occupational Health, School of Public Health, Université Libre de Bruxelles, Brussels, Belgium.,Division of Infection and Immunity, Faculty of Medical Sciences, University College London, London, United Kingdom.,Innovation and Business Development Unit, LHUB-ULB, Pole Hospitalier Universitaire de Bruxelles, Université Libre de Bruxelles, Brussels, Belgium
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28
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Krumm N, Shirts BH. Technical, Biological, and Systems Barriers for Molecular Clinical Decision Support. Clin Lab Med 2019; 39:281-294. [PMID: 31036281 DOI: 10.1016/j.cll.2019.01.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
Genome-enabled or molecular clinical decision support (CDS) systems provide unique advantages for the clinical use of genomic data; however, their implementation is complicated by technical, biological, and systemic barriers. This article reviews the substantial technical progress that has been made in the past decade and finds that the underlying biological limitations of genomics as well as systemic barriers to adoption of molecular CDS have been comparatively underestimated. A hybrid consultative CDS system, which integrates a genomics consultant into an active CDS system, may provide an interim path forward.
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Affiliation(s)
- Niklas Krumm
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA.
| | - Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Box 357110, 1959 Northeast Pacific Street, NW120, Seattle, WA 98195-7110, USA
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29
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Campbell WS, Carter AB, Cushman-Vokoun AM, Greiner TC, Dash RC, Routbort M, de Baca ME, Campbell JR. A Model Information Management Plan for Molecular Pathology Sequence Data Using Standards: From Sequencer to Electronic Health Record. J Mol Diagn 2019; 21:408-417. [PMID: 30797065 DOI: 10.1016/j.jmoldx.2018.12.002] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2018] [Revised: 11/10/2018] [Accepted: 12/04/2018] [Indexed: 10/27/2022] Open
Abstract
Incorporating genetic variant data into the electronic health record (EHR) in discrete computable fashion has vexed the informatics community for years. Genetic sequence test results are typically communicated by the molecular laboratory and stored in the EHR as textual documents. Although text documents are useful for human readability and initial use, they are not conducive for data retrieval and reuse. As a result, clinicians often struggle to find historical gene sequence results on a series of oncology patients within the EHR that might influence the care of the current patient. Second, identification of patients with specific mutation results in the EHR who are now eligible for new and/or changing therapy is not easily accomplished. Third, the molecular laboratory is challenged to monitor its sequencing processes for nonrandom process variation and other quality metrics. A novel approach to address each of these issues is presented and demonstrated. The authors use standard Health Level 7 laboratory result message formats in conjunction with international standards, Systematized Nomenclature of Medicine Clinical Terms and Human Genome Variant Society nomenclature, to represent, communicate, and store discrete gene sequence data within the EHR in a scalable fashion. This information management plan enables the support of the clinician at the point of care, enhances population management, and facilitates audits for maintaining laboratory quality.
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Affiliation(s)
- Walter S Campbell
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska.
| | - Alexis B Carter
- Department of Pathology, Children's Healthcare of Atlanta, Atlanta, Georgia
| | - Allison M Cushman-Vokoun
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Timothy C Greiner
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, Nebraska
| | - Rajesh C Dash
- Department of Pathology, Duke University Health System, Durham, North Carolina
| | - Mark Routbort
- Department of Hematopathology, University of Texas MD Anderson Cancer Center, Houston, Texas
| | | | - James R Campbell
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, Nebraska
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30
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Pennington JW, Karavite DJ, Krause EM, Miller J, Bernhardt BA, Grundmeier RW. Genomic decision support needs in pediatric primary care. J Am Med Inform Assoc 2018; 24:851-856. [PMID: 28339689 DOI: 10.1093/jamia/ocw184] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2016] [Accepted: 12/23/2016] [Indexed: 11/12/2022] Open
Abstract
Clinical genome and exome sequencing can diagnose pediatric patients with complex conditions that often require follow-up care with multiple specialties. The American Academy of Pediatrics emphasizes the role of the medical home and the primary care pediatrician in coordinating care for patients who need multidisciplinary support. In addition, the electronic health record (EHR) with embedded clinical decision support is recognized as an important component in providing care in this setting. We interviewed 6 clinicians to assess their experience caring for patients with complex and rare genetic findings and hear their opinions about how the EHR currently supports this role. Using these results, we designed a candidate EHR clinical decision support application mock-up and conducted formative exploratory user testing with 26 pediatric primary care providers to capture opinions on its utility in practice with respect to a specific clinical scenario. Our results indicate agreement that the functionality represented by the mock-up would effectively assist with care and warrants further development.
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Affiliation(s)
- Jeffrey W Pennington
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Dean J Karavite
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Edward M Krause
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Jeffrey Miller
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Barbara A Bernhardt
- Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
| | - Robert W Grundmeier
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA.,Department of Medicine, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA
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31
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Hicks JK, Dunnenberger HM, Gumpper KF, Haidar CE, Hoffman JM. Integrating pharmacogenomics into electronic health records with clinical decision support. Am J Health Syst Pharm 2018; 73:1967-1976. [PMID: 27864204 DOI: 10.2146/ajhp160030] [Citation(s) in RCA: 91] [Impact Index Per Article: 15.2] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022] Open
Abstract
PURPOSE Existing pharmacogenomic informatics models, key implementation steps, and emerging resources to facilitate the development of pharmacogenomic clinical decision support (CDS) are described. SUMMARY Pharmacogenomics is an important component of precision medicine. Informatics, especially CDS in the electronic health record (EHR), is a critical tool for the integration of pharmacogenomics into routine patient care. Effective integration of pharmacogenomic CDS into the EHR can address implementation challenges, including the increasing volume of pharmacogenomic clinical knowledge, the enduring nature of pharmacogenomic test results, and the complexity of interpreting results. Both passive and active CDS provide point-of-care information to clinicians that can guide the systematic use of pharmacogenomics to proactively optimize pharmacotherapy. Key considerations for a successful implementation have been identified; these include clinical workflows, identification of alert triggers, and tools to guide interpretation of results. These considerations, along with emerging resources from the Clinical Pharmacogenetics Implementation Consortium and the National Academy of Medicine, are described. CONCLUSION The EHR with CDS is essential to curate pharmacogenomic data and disseminate patient-specific information at the point of care. As part of the successful implementation of pharmacogenomics into clinical settings, all relevant clinical recommendations pertaining to gene-drug pairs must be summarized and presented to clinicians in a manner that is seamlessly integrated into the clinical workflow of the EHR. In some situations, ancillary systems and applications outside the EHR may be integrated to augment the capabilities of the EHR.
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Affiliation(s)
- J Kevin Hicks
- DeBartolo Family Personalized Medicine Institute and Department of Population Sciences, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | | | - Karl F Gumpper
- Department of Pharmacy, Boston Children's Hospital, Boston, MA
| | - Cyrine E Haidar
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN
| | - James M Hoffman
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, TN.
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32
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Al Kawam A, Sen A, Datta A, Dickey N. Understanding the Bioinformatics Challenges of Integrating Genomics into Healthcare. IEEE J Biomed Health Inform 2017; 22:1672-1683. [PMID: 29990071 DOI: 10.1109/jbhi.2017.2778263] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Genomic data is paving the way towards personalized healthcare. By unveiling genetic disease-contributing factors, genomic data can aid in the detection, diagnosis, and treatment of a wide range of complex diseases. Integrating genomic data into healthcare is riddled with a wide range of challenges spanning social, ethical, legal, educational, economic, and technical aspects. Bioinformatics is a core integration aspect presenting an overwhelming number of unaddressed challenges. In this paper we tackle the fundamental bioinformatics integration concerns including: genomic data generation, storage, representation, and utilization in conjunction with clinical data. We divide the bioinformatics challenges into a series of seven intertwined integration aspects spanning the areas of informatics, knowledge management, and communication. For each aspect, we provide a detailed discussion of the current research directions, outstanding challenges, and possible resolutions. This paper seeks to help narrow the gap between the genomic applications, which are being predominantly utilized in research settings, and the clinical adoption of these applications.
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33
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Ronquillo JG, Weng C, Lester WT. Assessing the readiness of precision medicine interoperabilty: An exploratory study of the National Institutes of Health genetic testing registry. JOURNAL OF INNOVATION IN HEALTH INFORMATICS 2017; 24:918. [PMID: 29334348 PMCID: PMC5891224 DOI: 10.14236/jhi.v24i4.918] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/22/2016] [Accepted: 08/29/2017] [Indexed: 11/18/2022] Open
Abstract
BACKGROUND Precision medicine involves three major innovations currently taking place in healthcare: electronic health records, genomics, and big data. A major challenge for healthcare providers, however, is understanding the readiness for practical application of initiatives like precision medicine. OBJECTIVE To better understand the current state and challenges of precision medicine interoperability using a national genetic testing registry as a starting point, placed in the context of established interoperability formats. METHODS We performed an exploratory analysis of the National Institutes of Health Genetic Testing Registry. Relevant standards included Health Level Seven International Version 3 Implementation Guide for Family History, the Human Genome Organization Gene Nomenclature Committee (HGNC) database, and Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT). We analyzed the distribution of genetic testing laboratories, genetic test characteristics, and standardized genome/clinical code mappings, stratified by laboratory setting. RESULTS There were a total of 25472 genetic tests from 240 laboratories testing for approximately 3632 distinct genes. Most tests focused on diagnosis, mutation confirmation, and/or risk assessment of germline mutations that could be passed to offspring. Genes were successfully mapped to all HGNC identifiers, but less than half of tests mapped to SNOMED CT codes, highlighting significant gaps when linking genetic tests to standardized clinical codes that explain the medical motivations behind test ordering. Conclusion: While precision medicine could potentially transform healthcare, successful practical and clinical application will first require the comprehensive and responsible adoption of interoperable standards, terminologies, and formats across all aspects of the precision medicine pipeline.
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34
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Freimuth RR, Formea CM, Hoffman JM, Matey E, Peterson JF, Boyce RD. Implementing Genomic Clinical Decision Support for Drug-Based Precision Medicine. CPT-PHARMACOMETRICS & SYSTEMS PHARMACOLOGY 2017; 6:153-155. [PMID: 28109071 PMCID: PMC5351408 DOI: 10.1002/psp4.12173] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 12/08/2016] [Revised: 01/16/2017] [Accepted: 01/17/2017] [Indexed: 11/07/2022]
Abstract
The explosive growth of patient-specific genomic information relevant to drug therapy will continue to be a defining characteristic of biomedical research. To implement drug-based personalized medicine (PM) for patients, clinicians need actionable information incorporated into electronic health records (EHRs). New clinical decision support (CDS) methods and informatics infrastructure are required in order to comprehensively integrate, interpret, deliver, and apply the full range of genomic data for each patient.
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Affiliation(s)
- R R Freimuth
- Department of Health Sciences Research, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - C M Formea
- Department of Pharmacy, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - J M Hoffman
- Department of Pharmaceutical Sciences, St. Jude Children's Research Hospital, Memphis, Tennessee, USA
| | - E Matey
- Department of Pharmacy, Center for Individualized Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - J F Peterson
- Department of Biomedical Informatics and Medicine, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - R D Boyce
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Campbell WS, Karlsson D, Vreeman DJ, Lazenby AJ, Talmon GA, Campbell JR. A computable pathology report for precision medicine: extending an observables ontology unifying SNOMED CT and LOINC. J Am Med Inform Assoc 2017; 25:259-266. [PMID: 29024958 PMCID: PMC7378880 DOI: 10.1093/jamia/ocx097] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2017] [Revised: 07/21/2017] [Accepted: 08/28/2017] [Indexed: 11/29/2022] Open
Abstract
Background The College of American Pathologists (CAP) introduced the first cancer synoptic reporting protocols in 1998. However, the objective of a fully computable and machine-readable cancer synoptic report remains elusive due to insufficient definitional content in Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT) and Logical Observation Identifiers Names and Codes (LOINC). To address this terminology gap, investigators at the University of Nebraska Medical Center (UNMC) are developing, authoring, and testing a SNOMED CT observable ontology to represent the data elements identified by the synoptic worksheets of CAP. Methods Investigators along with collaborators from the US National Library of Medicine, CAP, the International Health Terminology Standards Development Organization, and the UK Health and Social Care Information Centre analyzed and assessed required data elements for colorectal cancer and invasive breast cancer synoptic reporting. SNOMED CT concept expressions were developed at UNMC in the Nebraska Lexicon© SNOMED CT namespace. LOINC codes for each SNOMED CT expression were issued by the Regenstrief Institute. SNOMED CT concepts represented observation answer value sets. Results UNMC investigators created a total of 194 SNOMED CT observable entity concept definitions to represent required data elements for CAP colorectal and breast cancer synoptic worksheets, including biomarkers. Concepts were bound to colorectal and invasive breast cancer reports in the UNMC pathology system and successfully used to populate a UNMC biobank. Discussion The absence of a robust observables ontology represents a barrier to data capture and reuse in clinical areas founded upon observational information. Terminology developed in this project establishes the model to characterize pathology data for information exchange, public health, and research analytics.
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Affiliation(s)
- Walter S Campbell
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Daniel Karlsson
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden
| | - Daniel J Vreeman
- Regenstrief Institute, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Audrey J Lazenby
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - Geoffrey A Talmon
- Department of Pathology and Microbiology, University of Nebraska Medical Center, Omaha, NE, USA
| | - James R Campbell
- Department of Internal Medicine, University of Nebraska Medical Center, Omaha, NE, USA
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Anjum A, Raschia G, Gelgon M, Khan A, Malik SUR, Ahmad N, Ahmed M, Suhail S, Alam MM. τ -safety: A privacy model for sequential publication with arbitrary updates. Comput Secur 2017. [DOI: 10.1016/j.cose.2016.12.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
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Achour B, Al Feteisi H, Lanucara F, Rostami-Hodjegan A, Barber J. Global Proteomic Analysis of Human Liver Microsomes: Rapid Characterization and Quantification of Hepatic Drug-Metabolizing Enzymes. Drug Metab Dispos 2017; 45:666-675. [PMID: 28373266 DOI: 10.1124/dmd.116.074732] [Citation(s) in RCA: 36] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Accepted: 03/30/2017] [Indexed: 12/17/2022] Open
Abstract
Many genetic and environmental factors lead to interindividual variations in the metabolism and transport of drugs, profoundly affecting efficacy and toxicity. Precision dosing, that is, targeting drug dose to a well characterized subpopulation, is dependent on quantitative models of the profiles of drug-metabolizing enzymes (DMEs) and transporters within that subpopulation, informed by quantitative proteomics. We report the first use of ion mobility-mass spectrometry for this purpose, allowing rapid, robust, label-free quantification of human liver microsomal (HLM) proteins from distinct individuals. Approximately 1000 proteins were identified and quantified in four samples, including an average of 70 DMEs. Technical and biological variabilities were distinguishable, with technical variability accounting for about 10% of total variability. The biological variation between patients was clearly identified, with samples showing a range of expression profiles for cytochrome P450 and uridine 5'-diphosphoglucuronosyltransferase enzymes. Our results showed excellent agreement with previous data from targeted methods. The label-free method, however, allowed a fuller characterization of the in vitro system, showing, for the first time, that HLMs are significantly heterogeneous. Further, the traditional units of measurement of DMEs (pmol mg-1 HLM protein) are shown to introduce error arising from variability in unrelated, highly abundant proteins. Simulations of this variability suggest that up to 1.7-fold variation in apparent CYP3A4 abundance is artifactual, as are background positive correlations of up to 0.2 (Spearman correlation coefficient) between the abundances of DMEs. We suggest that protein concentrations used in pharmacokinetic predictions and scaling to in vivo clinical situations (physiologically based pharmacokinetics and in vitro-in vivo extrapolation) should be referenced instead to tissue mass.
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Affiliation(s)
- Brahim Achour
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester (B.A., H.A.F., A.R.-H., J.B.), Waters Corporation, Wilmslow, Cheshire East (F.L.), and Simcyp Limited (a Certara Company), Blades Enterprise Centre, Sheffield (A.R.-H.), United Kingdom
| | - Hajar Al Feteisi
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester (B.A., H.A.F., A.R.-H., J.B.), Waters Corporation, Wilmslow, Cheshire East (F.L.), and Simcyp Limited (a Certara Company), Blades Enterprise Centre, Sheffield (A.R.-H.), United Kingdom
| | - Francesco Lanucara
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester (B.A., H.A.F., A.R.-H., J.B.), Waters Corporation, Wilmslow, Cheshire East (F.L.), and Simcyp Limited (a Certara Company), Blades Enterprise Centre, Sheffield (A.R.-H.), United Kingdom
| | - Amin Rostami-Hodjegan
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester (B.A., H.A.F., A.R.-H., J.B.), Waters Corporation, Wilmslow, Cheshire East (F.L.), and Simcyp Limited (a Certara Company), Blades Enterprise Centre, Sheffield (A.R.-H.), United Kingdom
| | - Jill Barber
- Centre for Applied Pharmacokinetic Research, Division of Pharmacy and Optometry, School of Health Sciences, University of Manchester, Manchester (B.A., H.A.F., A.R.-H., J.B.), Waters Corporation, Wilmslow, Cheshire East (F.L.), and Simcyp Limited (a Certara Company), Blades Enterprise Centre, Sheffield (A.R.-H.), United Kingdom
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Campbell JR, Talmon G, Cushman-Vokoun A, Karlsson D, Scott Campbell W. An Extended SNOMED CT Concept Model for Observations in Molecular Genetics. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2017; 2016:352-360. [PMID: 28269830 PMCID: PMC5333284] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/06/2023]
Abstract
Molecular genetics laboratory reports are multiplying and increasingly of clinical importance in diagnosis and treatment of cancer, infectious disease and managing of public health. Little of this data is structured or maintained in the EHR in format useful for decision support or research. Structured, computable reporting is limited by non-availability of a domain ontology for these data. The IHTSDO and Regenstrief Institute(RI) have been collaborating since 2008 to develop a unified concept model and ontology of observable entities - concepts which represent the results of laboratory and clinical observations. In this paper we report the progress we have made to apply that unified concept model to the structured recording of observations in clinical molecular genetic pathology including immunohistochemistry and sequence variant findings. The primary use case for deployment is the structured and coded reporting of Cancer checklist
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Affiliation(s)
| | | | | | - Daniel Karlsson
- Department of Biomedical Engineering, Linköping University; Linköping, Sweden
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Alanazi A. Incorporating Pharmacogenomics into Health Information Technology, Electronic Health Record and Decision Support System: An Overview. J Med Syst 2016; 41:19. [DOI: 10.1007/s10916-016-0673-4] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2016] [Accepted: 12/07/2016] [Indexed: 10/20/2022]
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Tenenbaum JD, Avillach P, Benham-Hutchins M, Breitenstein MK, Crowgey EL, Hoffman MA, Jiang X, Madhavan S, Mattison JE, Nagarajan R, Ray B, Shin D, Visweswaran S, Zhao Z, Freimuth RR. An informatics research agenda to support precision medicine: seven key areas. J Am Med Inform Assoc 2016; 23:791-5. [PMID: 27107452 PMCID: PMC4926738 DOI: 10.1093/jamia/ocv213] [Citation(s) in RCA: 52] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2015] [Accepted: 12/24/2015] [Indexed: 01/22/2023] Open
Abstract
The recent announcement of the Precision Medicine Initiative by President Obama has brought precision medicine (PM) to the forefront for healthcare providers, researchers, regulators, innovators, and funders alike. As technologies continue to evolve and datasets grow in magnitude, a strong computational infrastructure will be essential to realize PM's vision of improved healthcare derived from personal data. In addition, informatics research and innovation affords a tremendous opportunity to drive the science underlying PM. The informatics community must lead the development of technologies and methodologies that will increase the discovery and application of biomedical knowledge through close collaboration between researchers, clinicians, and patients. This perspective highlights seven key areas that are in need of further informatics research and innovation to support the realization of PM.
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Affiliation(s)
- Jessica D Tenenbaum
- Department of Biostatistics and Bioinformatics, Duke University, Durham, NC, USA
| | - Paul Avillach
- Department of Biomedical Informatics, Harvard Medical School & Children's Hospital Informatics Program, Boston Children's Hospital, Boston, MA, USA
| | | | | | - Erin L Crowgey
- Center for Bioinformatics & Computational Biology, University of Delaware, Newark, DE, USA
| | - Mark A Hoffman
- Department of Biomedical & Health Informatics, University of Missouri - Kansas City, Children's Mercy Hospital, Kansas City, MO, USA
| | - Xia Jiang
- Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA, USA
| | - Subha Madhavan
- Department of Oncology, Lombardi Comprehensive Cancer Center, Georgetown University Medical Center, Innovation Center for Biomedical Informatics, Washington, DC, USA
| | - John E Mattison
- Exponential Medicine, Singularity University; Internal Medicine, System Solutions at Kaiser Permanente, Pasadena, CA, USA
| | | | - Bisakha Ray
- Center for Health Informatics and Bioinformatics, New York University School of Medicine, New York, NY, USA
| | - Dmitriy Shin
- Department of Pathology, MU Informatics Institute, University of Missouri, Columbia, MO, USA
| | - Shyam Visweswaran
- Department of Biomedical Informatics and the Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, USA
| | - Zhongming Zhao
- Center for Precision Health, School of Biomedical Informatics, University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
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Evans JP, Wilhelmsen KC, Berg J, Schmitt CP, Krishnamurthy A, Fecho K, Ahalt SC. A New Framework and Prototype Solution for Clinical Decision Support and Research in Genomics and Other Data-intensive Fields of Medicine. EGEMS 2016; 4:1198. [PMID: 27195307 PMCID: PMC4862762 DOI: 10.13063/2327-9214.1198] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/02/2023]
Abstract
Introduction: In genomics and other fields, it is now possible to capture and store large amounts of data in electronic medical records (EMRs). However, it is not clear if the routine accumulation of massive amounts of (largely uninterpretable) data will yield any health benefits to patients. Nevertheless, the use of large-scale medical data is likely to grow. To meet emerging challenges and facilitate optimal use of genomic data, our institution initiated a comprehensive planning process that addresses the needs of all stakeholders (e.g., patients, families, healthcare providers, researchers, technical staff, administrators). Our experience with this process and a key genomics research project contributed to the proposed framework. Framework: We propose a two-pronged Genomic Clinical Decision Support System (CDSS) that encompasses the concept of the “Clinical Mendeliome” as a patient-centric list of genomic variants that are clinically actionable and introduces the concept of the “Archival Value Criterion” as a decision-making formalism that approximates the cost-effectiveness of capturing, storing, and curating genome-scale sequencing data. We describe a prototype Genomic CDSS that we developed as a first step toward implementation of the framework. Conclusion: The proposed framework and prototype solution are designed to address the perspectives of stakeholders, stimulate effective clinical use of genomic data, drive genomic research, and meet current and future needs. The framework also can be broadly applied to additional fields, including other ‘-omics’ fields. We advocate for the creation of a Task Force on the Clinical Mendeliome, charged with defining Clinical Mendeliomes and drafting clinical guidelines for their use.
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Affiliation(s)
- James P Evans
- Department of Genetics, University of North Carolina at Chapel Hill; Department of Medicine, University of North Carolina at Chapel Hill
| | - Kirk C Wilhelmsen
- Department of Genetics, University of North Carolina at Chapel Hill; Department of Neurology, University of North Carolina at Chapel Hill
| | - Jonathan Berg
- Department of Genetics, University of North Carolina at Chapel Hill
| | - Charles P Schmitt
- Renaissance Computing Institute, University of North Carolina at Chapel Hill
| | - Ashok Krishnamurthy
- Renaissance Computing Institute, University of North Carolina at Chapel Hill
| | - Karamarie Fecho
- Renaissance Computing Institute, University of North Carolina at Chapel Hill
| | - Stanley C Ahalt
- Department of Computer Science, University of North Carolina at Chapel Hill; Renaissance Computing Institute, University of North Carolina at Chapel Hill
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Payne PRO, Lussier Y, Foraker RE, Embi PJ. Rethinking the role and impact of health information technology: informatics as an interventional discipline. BMC Med Inform Decis Mak 2016; 16:40. [PMID: 27025583 PMCID: PMC4812636 DOI: 10.1186/s12911-016-0278-3] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/13/2015] [Accepted: 03/21/2016] [Indexed: 12/03/2022] Open
Abstract
Recent advances in the adoption and use of health information technology (HIT) have had a dramatic impact on the practice of medicine. In many environments, this has led to the ability to achieve new efficiencies and levels of safety. In others, the impact has been less positive, and is associated with both: 1) workflow and user experience dissatisfaction; and 2) perceptions of missed opportunities relative to the use of computational tools to enable data-driven and precise clinical decision making. Simultaneously, the “pipeline” through which new diagnostic tools and therapeutic agents are being developed and brought to the point-of-care or population health is challenged in terms of both cost and timeliness. Given the confluence of these trends, it can be argued that now is the time to consider new ways in which HIT can be used to deliver health and wellness interventions comparable to traditional approaches (e.g., drugs, devices, diagnostics, and behavioral modifications). Doing so could serve to fulfill the promise of what has been recently promoted as “precision medicine” in a rapid and cost-effective manner. However, it will also require the health and life sciences community to embrace new modes of using HIT, wherein the use of technology becomes a primary intervention as opposed to enabler of more conventional approaches, a model that we refer to in this commentary as “interventional informatics”. Such a paradigm requires attention to critical issues, including: 1) the nature of the relationships between HIT vendors and healthcare innovators; 2) the formation and function of multidisciplinary teams consisting of technologists, informaticians, and clinical or scientific subject matter experts; and 3) the optimal design and execution of clinical studies that focus on HIT as the intervention of interest. Ultimately, the goal of an “interventional informatics” approach can and should be to substantially improve human health and wellness through the use of data-driven interventions at the point of care of broader population levels. Achieving a vision of “interventional informatics” will requires us to re-think how we study HIT tools in order to generate the necessary evidence-base that can support and justify their use as a primary means of improving the human condition.
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Affiliation(s)
- Philip R O Payne
- College of Medicine Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.
| | - Yves Lussier
- College of Medicine, Center for Biostatistics and Biomedical Informatics, University of Arizona, Tucson, AZ, USA
| | - Randi E Foraker
- College of Medicine Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA.,College of Public Health, Division of Epidemiology, The Ohio State University, Columbus, OH, USA
| | - Peter J Embi
- College of Medicine Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
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Hoffman JM, Dunnenberger HM, Kevin Hicks J, Caudle KE, Whirl Carrillo M, Freimuth RR, Williams MS, Klein TE, Peterson JF. Developing knowledge resources to support precision medicine: principles from the Clinical Pharmacogenetics Implementation Consortium (CPIC). J Am Med Inform Assoc 2016; 23:796-801. [PMID: 27026620 DOI: 10.1093/jamia/ocw027] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2015] [Accepted: 01/13/2016] [Indexed: 11/13/2022] Open
Abstract
To move beyond a select few genes/drugs, the successful adoption of pharmacogenomics into routine clinical care requires a curated and machine-readable database of pharmacogenomic knowledge suitable for use in an electronic health record (EHR) with clinical decision support (CDS). Recognizing that EHR vendors do not yet provide a standard set of CDS functions for pharmacogenetics, the Clinical Pharmacogenetics Implementation Consortium (CPIC) Informatics Working Group is developing and systematically incorporating a set of EHR-agnostic implementation resources into all CPIC guidelines. These resources illustrate how to integrate pharmacogenomic test results in clinical information systems with CDS to facilitate the use of patient genomic data at the point of care. Based on our collective experience creating existing CPIC resources and implementing pharmacogenomics at our practice sites, we outline principles to define the key features of future knowledge bases and discuss the importance of these knowledge resources for pharmacogenomics and ultimately precision medicine.
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Affiliation(s)
- James M Hoffman
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Henry M Dunnenberger
- Center for Molecular Medicine, NorthShore University HealthSystem, Evanston, IL, USA
| | - J Kevin Hicks
- Pharmacy Department and Genomic Medicine Institute, Cleveland Clinic, Cleveland, OH, USA
| | - Kelly E Caudle
- Department of Pharmaceutical Sciences, St Jude Children's Research Hospital, Memphis, TN, USA
| | | | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA, USA
| | - Teri E Klein
- Department of Genetics, Stanford University, Stanford, CA, USA
| | - Josh F Peterson
- Departments of Medicine and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
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Hall JL, Ryan JJ, Bray BE, Brown C, Lanfear D, Newby LK, Relling MV, Risch NJ, Roden DM, Shaw SY, Tcheng JE, Tenenbaum J, Wang TN, Weintraub WS. Merging Electronic Health Record Data and Genomics for Cardiovascular Research: A Science Advisory From the American Heart Association. ACTA ACUST UNITED AC 2016; 9:193-202. [PMID: 26976545 DOI: 10.1161/hcg.0000000000000029] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
The process of scientific discovery is rapidly evolving. The funding climate has influenced a favorable shift in scientific discovery toward the use of existing resources such as the electronic health record. The electronic health record enables long-term outlooks on human health and disease, in conjunction with multidimensional phenotypes that include laboratory data, images, vital signs, and other clinical information. Initial work has confirmed the utility of the electronic health record for understanding mechanisms and patterns of variability in disease susceptibility, disease evolution, and drug responses. The addition of biobanks and genomic data to the information contained in the electronic health record has been demonstrated. The purpose of this statement is to discuss the current challenges in and the potential for merging electronic health record data and genomics for cardiovascular research.
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Aziz A, Kawamoto K, Eilbeck K, Williams MS, Freimuth RR, Hoffman MA, Rasmussen LV, Overby CL, Shirts BH, Hoffman JM, Welch BM. The genomic CDS sandbox: An assessment among domain experts. J Biomed Inform 2016; 60:84-94. [PMID: 26778834 DOI: 10.1016/j.jbi.2015.12.019] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2015] [Revised: 12/11/2015] [Accepted: 12/29/2015] [Indexed: 01/17/2023]
Abstract
Genomics is a promising tool that is becoming more widely available to improve the care and treatment of individuals. While there is much assertion, genomics will most certainly require the use of clinical decision support (CDS) to be fully realized in the routine clinical setting. The National Human Genome Research Institute (NHGRI) of the National Institutes of Health recently convened an in-person, multi-day meeting on this topic. It was widely recognized that there is a need to promote the innovation and development of resources for genomic CDS such as a CDS sandbox. The purpose of this study was to evaluate a proposed approach for such a genomic CDS sandbox among domain experts and potential users. Survey results indicate a significant interest and desire for a genomic CDS sandbox environment among domain experts. These results will be used to guide the development of a genomic CDS sandbox.
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Affiliation(s)
- Ayesha Aziz
- Medical University of South Carolina, Charleston, SC, United States.
| | | | - Karen Eilbeck
- University of Utah, Salt Lake City, UT, United States.
| | | | | | | | | | | | | | - James M Hoffman
- St. Jude Children's Research Hospital, Memphis, TN, United States.
| | - Brandon M Welch
- Medical University of South Carolina, Charleston, SC, United States.
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Payne TH, Corley S, Cullen TA, Gandhi TK, Harrington L, Kuperman GJ, Mattison JE, McCallie DP, McDonald CJ, Tang PC, Tierney WM, Weaver C, Weir CR, Zaroukian MH. Report of the AMIA EHR-2020 Task Force on the status and future direction of EHRs. J Am Med Inform Assoc 2015; 22:1102-10. [PMID: 26024883 PMCID: PMC5009932 DOI: 10.1093/jamia/ocv066] [Citation(s) in RCA: 121] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2015] [Revised: 06/16/2015] [Accepted: 06/16/2015] [Indexed: 01/17/2023] Open
Affiliation(s)
- Thomas H Payne
- UW Medicine Information Technology Services, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | | | | | | | | | | | | | - Clement J McDonald
- National Institutes of Health, National Library of Medicine, Bethesda, MD, USA
| | - Paul C Tang
- Palo Alto Medical Foundation, Palo Alto, CA, USA
| | - William M Tierney
- Regenstrief Institute, Inc., Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Charlene R Weir
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Michael H Zaroukian
- Sparrow Health System, Lansing, MI and Department of Medicine, College of Human Medicine, Michigan State University, East Lansing, MI, USA
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Herr TM, Bielinski SJ, Bottinger E, Brautbar A, Brilliant M, Chute CG, Denny J, Freimuth RR, Hartzler A, Kannry J, Kohane IS, Kullo IJ, Lin S, Pathak J, Peissig P, Pulley J, Ralston J, Rasmussen L, Roden D, Tromp G, Williams MS, Starren J. A conceptual model for translating omic data into clinical action. J Pathol Inform 2015; 6:46. [PMID: 26430534 PMCID: PMC4584438 DOI: 10.4103/2153-3539.163985] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2015] [Accepted: 07/01/2015] [Indexed: 01/27/2023] Open
Abstract
Genomic, proteomic, epigenomic, and other “omic” data have the potential to enable precision medicine, also commonly referred to as personalized medicine. The volume and complexity of omic data are rapidly overwhelming human cognitive capacity, requiring innovative approaches to translate such data into patient care. Here, we outline a conceptual model for the application of omic data in the clinical context, called “the omic funnel.” This model parallels the classic “Data, Information, Knowledge, Wisdom pyramid” and adds context for how to move between each successive layer. Its goal is to allow informaticians, researchers, and clinicians to approach the problem of translating omic data from bench to bedside, by using discrete steps with clearly defined needs. Such an approach can facilitate the development of modular and interoperable software that can bring precision medicine into widespread practice.
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Affiliation(s)
- Timothy M Herr
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Suzette J Bielinski
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Erwin Bottinger
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine, Mount Sinai, New York, USA
| | - Ariel Brautbar
- Division of Genetics and Endocrinology, Cook Children's Medical Center, Fort Worth, Texas, USA
| | - Murray Brilliant
- Center for Human Genetics, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Christopher G Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, Maryland, USA
| | - Joshua Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, Tennessee, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | | | - Joseph Kannry
- Icahn School of Medicine, Mount Sinai, New York, USA
| | - Isaac S Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA
| | - Iftikhar J Kullo
- Division of Cardiovascular Diseases, Mayo Clinic, Rochester, Minnesota, USA
| | - Simon Lin
- Nationwide Children's Hospital, Columbus, Ohio, USA
| | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, Minnesota, USA
| | - Peggy Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, Wisconsin, USA
| | - Jill Pulley
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - James Ralston
- Group Health Research Institute, Seattle, Washington, USA
| | - Luke Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
| | - Dan Roden
- Vanderbilt University School of Medicine, Nashville, Tennessee, USA
| | - Gerard Tromp
- Weis Center for Research, Danville, Pennsylvania, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, Pennsylvania, USA
| | - Justin Starren
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA
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Shirts BH, Salama JS, Aronson SJ, Chung WK, Gray SW, Hindorff LA, Jarvik GP, Plon SE, Stoffel EM, Tarczy-Hornoch PZ, Van Allen EM, Weck KE, Chute CG, Freimuth RR, Grundmeier RW, Hartzler AL, Li R, Peissig PL, Peterson JF, Rasmussen LV, Starren JB, Williams MS, Overby CL. CSER and eMERGE: current and potential state of the display of genetic information in the electronic health record. J Am Med Inform Assoc 2015; 22:1231-42. [PMID: 26142422 DOI: 10.1093/jamia/ocv065] [Citation(s) in RCA: 62] [Impact Index Per Article: 6.9] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2015] [Accepted: 05/12/2015] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Clinicians' ability to use and interpret genetic information depends upon how those data are displayed in electronic health records (EHRs). There is a critical need to develop systems to effectively display genetic information in EHRs and augment clinical decision support (CDS). MATERIALS AND METHODS The National Institutes of Health (NIH)-sponsored Clinical Sequencing Exploratory Research and Electronic Medical Records & Genomics EHR Working Groups conducted a multiphase, iterative process involving working group discussions and 2 surveys in order to determine how genetic and genomic information are currently displayed in EHRs, envision optimal uses for different types of genetic or genomic information, and prioritize areas for EHR improvement. RESULTS There is substantial heterogeneity in how genetic information enters and is documented in EHR systems. Most institutions indicated that genetic information was displayed in multiple locations in their EHRs. Among surveyed institutions, genetic information enters the EHR through multiple laboratory sources and through clinician notes. For laboratory-based data, the source laboratory was the main determinant of the location of genetic information in the EHR. The highest priority recommendation was to address the need to implement CDS mechanisms and content for decision support for medically actionable genetic information. CONCLUSION Heterogeneity of genetic information flow and importance of source laboratory, rather than clinical content, as a determinant of information representation are major barriers to using genetic information optimally in patient care. Greater effort to develop interoperable systems to receive and consistently display genetic and/or genomic information and alert clinicians to genomic-dependent improvements to clinical care is recommended.
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Affiliation(s)
- Brian H Shirts
- Department of Laboratory Medicine, University of Washington, Seattle, WA, 98195, USA
| | - Joseph S Salama
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA
| | | | - Wendy K Chung
- Department of Pediatrics, Columbia University Medical Center, New York, NY, USA
| | - Stacy W Gray
- Department of Medicine, Harvard Medical School, Boston, MA, USA Dana-Farber Cancer Institute, Boston, MA, USA
| | - Lucia A Hindorff
- National Human Genome Research Institute, NIH, Rockville, MD, USA
| | - Gail P Jarvik
- Department of Medicine, Division of Medical Genetics, University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Sharon E Plon
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
| | - Elena M Stoffel
- Department of Internal Medicine, University of Michigan, Ann Arbor, MI, USA
| | - Peter Z Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, USA
| | - Eliezer M Van Allen
- Dana-Farber Cancer Institute, Boston, MA, USA The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Karen E Weck
- Department of Pathology and Laboratory Medicine, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Christopher G Chute
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Robert R Freimuth
- Department of Health Sciences Research, Division of Biomedical Statistics and Informatics, Mayo Clinic, Rochester, MN, USA
| | - Robert W Grundmeier
- Department of Biomedical and Health Informatics, The Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Andrea L Hartzler
- Group Health Research Institute, Group Health Cooperative, Seattle, WA, USA
| | - Rongling Li
- National Human Genome Research Institute, NIH, Rockville, MD, USA
| | - Peggy L Peissig
- Biomedical Informatics Research Center, Marshfield Clinic Research Foundation, Marshfield, WI, USA
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt, Nashville, TN, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Justin B Starren
- Department of Preventive Medicine, Division of Health and Biomedical Informatics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Marc S Williams
- Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA
| | - Casey L Overby
- Genome Medicine Institute, Geisinger Medical Center, Danville, PA, USA Department of Medicine, Program for Personalized and Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, MD, USA
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Affiliation(s)
- Isaac S Kohane
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA 02115, USA.
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50
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Castro VM, Minnier J, Murphy SN, Kohane I, Churchill SE, Gainer V, Cai T, Hoffnagle AG, Dai Y, Block S, Weill SR, Nadal-Vicens M, Pollastri AR, Rosenquist JN, Goryachev S, Ongur D, Sklar P, Perlis RH, Smoller JW. Validation of electronic health record phenotyping of bipolar disorder cases and controls. Am J Psychiatry 2015; 172:363-72. [PMID: 25827034 PMCID: PMC4441333 DOI: 10.1176/appi.ajp.2014.14030423] [Citation(s) in RCA: 86] [Impact Index Per Article: 9.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
OBJECTIVE The study was designed to validate use of electronic health records (EHRs) for diagnosing bipolar disorder and classifying control subjects. METHOD EHR data were obtained from a health care system of more than 4.6 million patients spanning more than 20 years. Experienced clinicians reviewed charts to identify text features and coded data consistent or inconsistent with a diagnosis of bipolar disorder. Natural language processing was used to train a diagnostic algorithm with 95% specificity for classifying bipolar disorder. Filtered coded data were used to derive three additional classification rules for case subjects and one for control subjects. The positive predictive value (PPV) of EHR-based bipolar disorder and subphenotype diagnoses was calculated against diagnoses from direct semistructured interviews of 190 patients by trained clinicians blind to EHR diagnosis. RESULTS The PPV of bipolar disorder defined by natural language processing was 0.85. Coded classification based on strict filtering achieved a value of 0.79, but classifications based on less stringent criteria performed less well. No EHR-classified control subject received a diagnosis of bipolar disorder on the basis of direct interview (PPV=1.0). For most subphenotypes, values exceeded 0.80. The EHR-based classifications were used to accrue 4,500 bipolar disorder cases and 5,000 controls for genetic analyses. CONCLUSIONS Semiautomated mining of EHRs can be used to ascertain bipolar disorder patients and control subjects with high specificity and predictive value compared with diagnostic interviews. EHRs provide a powerful resource for high-throughput phenotyping for genetic and clinical research.
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Affiliation(s)
- Victor M. Castro
- Partners Research Information Systems and Computing, Oregon Health & Science University, Portland, OR
| | - Jessica Minnier
- Department of Public Health & Preventive Medicine, Oregon Health & Science University, Portland, OR
| | - Shawn N. Murphy
- Partners Research Information Systems and Computing, Oregon Health & Science University, Portland, OR
- Laboratory of Computer Science and Department of Neurology, Massachusetts General Hospital, Boston, MA
| | - Isaac Kohane
- Center for Biomedical Informatics, Harvard Medical School, Boston, MA
| | | | - Vivian Gainer
- Partners Research Information Systems and Computing, Oregon Health & Science University, Portland, OR
| | - Tianxi Cai
- Department of Biostatistics, Harvard School of Public Health, Boston, MA
| | - Alison G. Hoffnagle
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
| | - Yael Dai
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
| | - Stefanie Block
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
| | - Sydney R. Weill
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
| | - Mireya Nadal-Vicens
- Center for Anxiety and Traumatic Stress Disorders, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
| | - Alisha R. Pollastri
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
| | - J. Niels Rosenquist
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
| | - Sergey Goryachev
- Partners Research Information Systems and Computing, Oregon Health & Science University, Portland, OR
| | | | - Pamela Sklar
- Division of Psychiatric Genomics, Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Roy H. Perlis
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Center for Experimental Drugs and Diagnostics, Massachusetts General Hospital, Boston, MA
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetic Research, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
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