1
|
Fehlberg Z, Stark Z, Best S. Reanalysis of genomic data, how do we do it now and what if we automate it? A qualitative study. Eur J Hum Genet 2024; 32:521-528. [PMID: 38212661 PMCID: PMC11061153 DOI: 10.1038/s41431-023-01532-4] [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: 10/23/2023] [Revised: 12/14/2023] [Accepted: 12/21/2023] [Indexed: 01/13/2024] Open
Abstract
Automating reanalysis of genomic data for undiagnosed rare disease patients presents a paradigm shift in how clinical genomics is delivered. We aimed to map the current manual and proposed automated approach to reanalysis and identify possible implementation strategies to address clinical and laboratory staff's perceived challenges to automation. Fourteen semi-structured interviews guided by a simplified process map were conducted with clinical and laboratory staff across Australia. Individual process maps were integrated into an overview of the current process, noting variation in service delivery. Participants then mapped an automated approach and were invited to discuss perceived challenges and possible supports to automation. Responses were analysed using the Consolidated Framework for Implementation Research, linking to the Expert Recommendations for Implementing Change framework to identify theory-informed implementation strategies. Process mapping demonstrates how automation streamlines processes with eleven steps reduced to seven. Although participants welcomed automation, challenges were raised at six of the steps. Strategies to overcome challenges include embedding project champions, developing education materials, facilitating clinical innovation and quality monitoring tools, and altering reimbursement structures. Future work can build on these findings to develop context specific implementation strategies to guide translation of an automated approach to reanalysis to improve clinical care and patient outcomes.
Collapse
Affiliation(s)
- Zoe Fehlberg
- Australian Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
| | - Zornitza Stark
- Australian Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- University of Melbourne, Melbourne, VIC, Australia
- Victorian Clinical Genetics Services, Murdoch Children's Research Institute, Melbourne, VIC, Australia
| | - Stephanie Best
- Australian Genomics, Murdoch Children's Research Institute, Melbourne, VIC, Australia.
- University of Melbourne, Melbourne, VIC, Australia.
- Department of Health Services Research, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia.
- Victorian Comprehensive Cancer Centre, Melbourne, VIC, Australia.
- Department of Oncology, Sir Peter MacCallum Cancer Centre, University of Melbourne, Melbourne, VIC, Australia.
| |
Collapse
|
2
|
Hines-Dowell S, McNamara E, Mostafavi R, Taylor L, Harrison L, McGee RB, Blake AK, Lewis S, Perrino M, Mandrell B, Nichols KE. Genomes for Nurses: Understanding and Overcoming Barriers to Nurses Utilizing Genomics. JOURNAL OF PEDIATRIC HEMATOLOGY/ONCOLOGY NURSING 2024; 41:140-147. [PMID: 38347731 DOI: 10.1177/27527530231214540] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/16/2024]
Abstract
Background: Genomic testing is an increasingly important technology within pediatric oncology that aids in cancer diagnosis, provides prognostic information, identifies therapeutic targets, and reveals underlying cancer predisposition. However, nurses lack basic knowledge of genomics and have limited self-assurance in using genomic information in their daily practice. This single-institution project was carried out at an academic pediatric cancer hospital in the United States with the aim to explore the barriers to achieving genomics literacy for pediatric oncology nurses. Method: This project assessed barriers to genomic education and preferences for receiving genomics education among pediatric oncology nurses, nurse practitioners, and physician assistants. An electronic survey with demographic questions and 15 genetics-focused questions was developed. The final survey instrument consisted of nine sections and was pilot-tested prior to administration. Data were analyzed using a ranking strategy, and five focus groups were conducted to capture more-nuanced information. The focus group sessions lasted 40 min to 1 hour and were recorded and transcribed. Results: Over 50% of respondents were uncomfortable with or felt unprepared to answer questions from patients and/or family members about genomics. This unease ranked as the top barrier to using genomic information in clinical practice. Discussion: These results reveal that most nurses require additional education to facilitate an understanding of genomics. This project lays the foundation to guide the development of a pediatric cancer genomics curriculum, which will enable the incorporation of genomics into nursing practice.
Collapse
Affiliation(s)
| | | | | | - Leslie Taylor
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Lynn Harrison
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Rose B McGee
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Alise K Blake
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | - Sara Lewis
- St. Jude Children's Research Hospital, Memphis, TN, USA
| | | | | | - Kim E Nichols
- St. Jude Children's Research Hospital, Memphis, TN, USA
| |
Collapse
|
3
|
Friedrich B, Vindrola-Padros C, Lucassen AM, Patch C, Clarke A, Lakhanpaul M, Lewis C. "A very big challenge": a qualitative study to explore the early barriers and enablers to implementing a national genomic medicine service in England. Front Genet 2024; 14:1282034. [PMID: 38239852 PMCID: PMC10794539 DOI: 10.3389/fgene.2023.1282034] [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: 08/23/2023] [Accepted: 11/27/2023] [Indexed: 01/22/2024] Open
Abstract
Background: The Genomic Medicine Service (GMS) was launched in 2018 in England to create a step-change in the use of genomics in the NHS, including offering whole genome sequencing (WGS) as part of routine care. In this qualitative study on pediatric rare disease diagnosis, we used an implementation science framework to identify enablers and barriers which have influenced rollout. Methods: Semi-structured interviews were conducted with seven participants tasked with designing the GMS and 14 tasked with leading the implementation across the seven Genomic Medicine Service Alliances (GMSAs) and/or Genomic Laboratory Hubs (GLHs) between October 2021 and February 2022. Results: Overall, those involved in delivering the service strongly support its aims and ambitions. Challenges include: 1) concerns around the lack of trained and available workforce (clinicians and scientists) to seek consent from patients, interpret findings and communicate results; 2) the lack of a digital, coordinated infrastructure in place to support and standardize delivery with knock-on effects including onerous administrative aspects required to consent patients and order WGS tests; 3) that the "mainstreaming agenda", whilst considered important, encountered reluctance to become engaged from those who did not see it as a priority or viewed it as being politically rather than clinically driven; 4) the timelines and targets set for the GMS were perceived by some as too ambitious. Interviewees discussed local adaptations and strategies employed to address the various challenges they had encountered, including 1) capacity-building, 2) employing genomic associates and other support staff to support the consent and test ordering process, 3) having "genomic champions" embedded in mainstream services to impart knowledge and best practice, 4) enhancing collaboration between genetic and mainstream specialties, 5) building evaluation into the service and 6) co-creating services with patients and the public. Conclusion: Our findings highlight the challenges of implementing system-wide change within a complex healthcare system. Local as well as national solutions can undoubtedly address many of these barriers over time.
Collapse
Affiliation(s)
- Bettina Friedrich
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Cecilia Vindrola-Padros
- Department of Targeted Intervention and Rapid Research Evaluation and Appraisal Lab (RREAL), University College London, London, United Kingdom
| | - Anneke M. Lucassen
- Clinical Ethics and Law, Faculty of Medicine, University of Southampton, Southampton, United Kingdom
- Centre for Personalised Medicine, The Wellcome Centre for Human Genetics, Oxford, United Kingdom
| | - Chris Patch
- Engagement and Society, Wellcome Connecting Science Wellcome Genome Campus, Hinxton, United Kingdom
| | - Angus Clarke
- Division of Cancer and Genetics, Cardiff University School of Medicine, Cardiff, United Kingdom
| | - Monica Lakhanpaul
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
| | - Celine Lewis
- Population, Policy and Practice, UCL Great Ormond Street Institute of Child Health, London, United Kingdom
- London North Genomic Laboratory Hub, London, United Kingdom
| |
Collapse
|
4
|
Bousman CA, Maruf AA, Marques DF, Brown LC, Müller DJ. The emergence, implementation, and future growth of pharmacogenomics in psychiatry: a narrative review. Psychol Med 2023; 53:7983-7993. [PMID: 37772416 PMCID: PMC10755240 DOI: 10.1017/s0033291723002817] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/24/2023] [Accepted: 08/30/2023] [Indexed: 09/30/2023]
Abstract
Psychotropic medication efficacy and tolerability are critical treatment issues faced by individuals with psychiatric disorders and their healthcare providers. For some people, it can take months to years of a trial-and-error process to identify a medication with the ideal efficacy and tolerability profile. Current strategies (e.g. clinical practice guidelines, treatment algorithms) for addressing this issue can be useful at the population level, but often fall short at the individual level. This is, in part, attributed to interindividual variation in genes that are involved in pharmacokinetic (i.e. absorption, distribution, metabolism, elimination) and pharmacodynamic (e.g. receptors, signaling pathways) processes that in large part, determine whether a medication will be efficacious or tolerable. A precision prescribing strategy know as pharmacogenomics (PGx) assesses these genomic variations, and uses it to inform selection and dosing of certain psychotropic medications. In this review, we describe the path that led to the emergence of PGx in psychiatry, the current evidence base and implementation status of PGx in the psychiatric clinic, and finally, the future growth potential of precision psychiatry via the convergence of the PGx-guided strategy with emerging technologies and approaches (i.e. pharmacoepigenomics, pharmacomicrobiomics, pharmacotranscriptomics, pharmacoproteomics, pharmacometabolomics) to personalize treatment of psychiatric disorders.
Collapse
Affiliation(s)
- Chad A. Bousman
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, AB, Canada
- Department of Medical Genetics, University of Calgary, Calgary, AB, Canada
- Departments of Physiology and Pharmacology, and Community Health Sciences, University of Calgary, Calgary, AB, Canada
- AB Children's Hospital Research Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Melbourne, Melbourne, VIC, Australia
| | - Abdullah Al Maruf
- The Mathison Centre for Mental Health Research & Education, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Psychiatry, University of Calgary, AB, Canada
- College of Pharmacy, Rady Faculty of Health Sciences, Winnipeg, MB, Canada
| | | | | | - Daniel J. Müller
- Pharmacogenetics Research Clinic, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada
- Department of Psychiatry, Psychosomatics and Psychotherapy, Center of Mental Health, University Hospital of Wurzburg, Wurzburg, Germany
| |
Collapse
|
5
|
Dickey L, Gronowski B, Jones K, Rinaldi JB, Emery K, Clemens J, Gordon O, Vartanian K. Participation in genetic screening: testing different outreach methods across a diverse hospital system based patient population. Front Genet 2023; 14:1272931. [PMID: 37900185 PMCID: PMC10602775 DOI: 10.3389/fgene.2023.1272931] [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: 08/04/2023] [Accepted: 09/29/2023] [Indexed: 10/31/2023] Open
Abstract
Introduction: Genomics has the potential to transform medicine by identifying genetic risk factors that predispose people to certain illnesses. Use of genetic screening is rapidly expanding and shifting towards screening all patients regardless of known risk factors, but research is limited on the success of broad population-level outreach for genetic testing and the effectiveness of different outreach methods across diverse populations. In this study, we tested the effectiveness of Digital Only (emailing and texting) and Brochure Plus Digital (mailed brochure, emailing, and texting) outreach to encourage a diverse patient population to participate in a large hospital system's whole genome sequencing program. Methods: Disproportionate stratified sampling was used to create a study population more demographically diverse than the eligible population and response rates were analyzed overall and by demographics to understand the effectiveness of different outreach strategies. Results: 7.5% of all eligible patients enrolled in the program. While approximately 70% of patients invited to complete genetic testing identified in their EHR as being Hispanic, Black or African America, Asian, or another non-White race, these patients generally enrolled at lower rates than the overall population. Other underrepresented groups had higher enrollment rates including people with Medicaid coverage (8.7%) and those residing in rural areas (10.6%). We found no significant difference in enrollment rates between our Digital-Only and our Brochure Plus Digital outreach approaches in the overall population, but enrollment rates were significantly higher for Asian patients and patients who resided in rural areas in the Brochure Plus Digital group. Across both outreach approaches, links provided in emails were most commonly used for enrollment. Discussion: Our study reveals expected enrollment rates for proactive outreach by a hospital system for genetic testing in a diverse population. As more hospital systems are adopting population-scale genetic testing, these findings can inform future outreach efforts to recruit patients for genetic testing including those patients traditionally underrepresented in genomics.
Collapse
Affiliation(s)
- Lindsay Dickey
- Center for Outcomes Research and Education, Portland, OR, United States
| | - Ben Gronowski
- Center for Outcomes Research and Education, Portland, OR, United States
| | - Kyle Jones
- Center for Outcomes Research and Education, Portland, OR, United States
| | - J. B. Rinaldi
- Center for Outcomes Research and Education, Portland, OR, United States
| | - Kate Emery
- Center for Clinical Genetics and Genomics for Providence Southern California, Burbank, CA, United States
| | - Jon Clemens
- Center for Clinical Genetics and Genomics for Providence Southern California, Burbank, CA, United States
| | - Ora Gordon
- Center for Clinical Genetics and Genomics for Providence Southern California, Burbank, CA, United States
| | - Keri Vartanian
- Center for Outcomes Research and Education, Portland, OR, United States
| |
Collapse
|
6
|
Allen CG, Sterba K, Norman S, Jackson A, Hunt KJ, McMahon L, Judge DP. Use of a multi-phased approach to identify and address facilitators and barriers to the implementation of a population-wide genomic screening program. Implement Sci Commun 2023; 4:122. [PMID: 37821977 PMCID: PMC10566189 DOI: 10.1186/s43058-023-00500-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Accepted: 09/06/2023] [Indexed: 10/13/2023] Open
Abstract
INTRODUCTION Population-wide genomic screening for CDC Tier-1 conditions offers the ability to identify the 1-2% of the US population at increased risk for Hereditary Breast and Ovarian Cancer, Lynch Syndrome, and Familial Hypercholesterolemia. Implementation of population-wide screening programs is highly complex, requiring engagement of diverse collaborators and implementation teams. Implementation science offers tools to promote integration of these programs through the identification of determinants of success and strategies to address potential barriers. METHODS Prior to launching the program, we conducted a pre-implementation survey to assess anticipated barriers and facilitators to reach, effectiveness, adoption, implementation, and maintenance (RE-AIM), among 51 work group members (phase 1). During the first year of program implementation, we completed coding of 40 work group meetings guided by the Consolidated Framework for Implementation Research (CFIR) (phase 2). We matched the top barriers to implementation strategies identified during phase 2 using the CFIR-ERIC (Expert Recommendation for Implementing Change) matching tool. RESULTS Staffing and workload concerns were listed as the top barrier in the pre-implementation phase of the program. Top barriers during implementation included adaptability (n = 8, 20%), complexity (n = 14, 35%), patient needs and resources (n = 9, 22.5%), compatibility (n = 11, 27.5%), and self-efficacy (n = 9, 22.5%). We identified 16 potential implementation strategies across six ERIC clusters to address these barriers and operationalized these strategies for our specific setting and program needs. CONCLUSION Our findings provide an example of successful use of the CFIR-ERIC tool to guide implementation of a population-wide genomic screening program.
Collapse
Affiliation(s)
- Caitlin G Allen
- Department of Public Health Science, College of Medicine, Medical University of South Carolina, Charleston, SC, USA.
| | - Katherine Sterba
- Department of Public Health Science, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Samantha Norman
- In Our DNA SC, Medical University of South Carolina, Charleston, SC, USA
| | - Amy Jackson
- In Our DNA SC, Medical University of South Carolina, Charleston, SC, USA
| | - Kelly J Hunt
- Department of Public Health Science, College of Medicine, Medical University of South Carolina, Charleston, SC, USA
| | - Lori McMahon
- Research Office, Medical University of South Carolina, Charleston, SC, USA
| | - Daniel P Judge
- In Our DNA SC, Medical University of South Carolina, Charleston, SC, USA
| |
Collapse
|
7
|
Middleton A, Adams A, Aidid H, Atutornu J, Boraschi D, Borra J, Bircan T, Burch C, Costa A, Dickinson A, Enticknap A, Galloway C, Gale F, Garlick E, Haydon E, Henriques S, Mitchell M, Milne R, Monaghan J, Morley KI, Muella Santos M, Olivares Boldu L, Olumogba F, Orviss K, Parry V, Patch C, Robarts L, Shingles S, Smidt C, Tomlin B, Parkinson S. Public engagement with genomics. Wellcome Open Res 2023; 8:310. [PMID: 37928209 PMCID: PMC10624956 DOI: 10.12688/wellcomeopenres.19473.2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/13/2023] [Indexed: 11/07/2023] Open
Abstract
As detailed in its flagship report, Genome UK, the UK government recognises the vital role that broad public engagement across whole populations plays in the field of genomics. However, there is limited evidence about how to do this at scale. Most public audiences do not feel actively connected to science, are oftenunsure of the relevance to their lives and rarely talk to their family and friends about; we term this dis-connection a 'disengaged public audience'. We use a narrative review to explore: (i) UK attitudes towards genetics and genomics and what may influence reluctance to engage with these topics; (ii) innovative public engagement approaches that have been used to bring diverse public audiences into conversations about the technology. Whilst we have found some novel engagement methods that have used participatory arts, film, social media and deliberative methods, there is no clear agreement on best practice. We did not find a consistently used, evidence-based strategy for delivering public engagement about genomics across diverse and broad populations, nor a specific method that is known to encourage engagement from groups that have historically felt (in terms of perception) and been (in reality) excluded from genomic research. We argue there is a need for well-defined, tailor-made engagement strategies that clearly articulate the audience, the purpose and the proposed impact of the engagement intervention. This needs to be coupled with robust evaluation frameworks to build the evidence-base for population-level engagement strategies.
Collapse
Affiliation(s)
- Anna Middleton
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | | | - Hugbaad Aidid
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | - Jerome Atutornu
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
- School of Health and Sport Sciences, University of Suffolk, Ipswich, England, UK
| | - Daniela Boraschi
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | | | - Tuba Bircan
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | - Claudette Burch
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | | | | | | | - Catherine Galloway
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | | | - Emma Garlick
- Wellcome Connecting Science, Hinxton, England, UK
| | - Em Haydon
- Wellcome Connecting Science, Hinxton, England, UK
| | - Sasha Henriques
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
- Clinical Genetics Department, Guy's and St Thomas' Hospital, London, England, UK
| | - Marion Mitchell
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | - Richard Milne
- Wellcome Connecting Science, Hinxton, England, UK
- Kavli Centre for Ethics, Science and the Public, University of Cambridge, Cambridge, England, UK
| | | | - Katherine I Morley
- RAND Europe, Cambridge, England, UK
- Melbourne School of Population Health, The University of Melbourne, Melbourne, Victoria, Australia
| | | | | | | | | | - Vivienne Parry
- Genomics England, Queen Mary University of London, London, England, UK
| | | | | | - Sam Shingles
- Wellcome Connecting Science, Hinxton, England, UK
| | - Cindy Smidt
- Wellcome Connecting Science, Hinxton, England, UK
| | - Ben Tomlin
- Wellcome Connecting Science, Hinxton, England, UK
| | | |
Collapse
|
8
|
Ellis SD, Brooks JV, Birken SA, Morrow E, Hilbig ZS, Wulff-Burchfield E, Kinney AY, Ellerbeck EF. Determinants of targeted cancer therapy use in community oncology practice: a qualitative study using the Theoretical Domains Framework and Rummler-Brache process mapping. Implement Sci Commun 2023; 4:66. [PMID: 37308981 PMCID: PMC10259814 DOI: 10.1186/s43058-023-00441-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Accepted: 05/25/2023] [Indexed: 06/14/2023] Open
Abstract
BACKGROUND Precision medicine holds enormous potential to improve outcomes for cancer patients, offering improved rates of cancer control and quality of life. Not all patients who could benefit from targeted cancer therapy receive it, and some who may not benefit do receive targeted therapy. We sought to comprehensively identify determinants of targeted therapy use among community oncology programs, where most cancer patients receive their care. METHODS Guided by the Theoretical Domains Framework, we conducted semi-structured interviews with 24 community cancer care providers and mapped targeted therapy delivery across 11 cancer care delivery teams using a Rummler-Brache diagram. Transcripts were coded to the framework using template analysis, and inductive coding was used to identify key behaviors. Coding was revised until a consensus was reached. RESULTS Intention to deliver precision medicine was high across all participants interviewed, who also reported untenable knowledge demands. We identified distinctly different teams, processes, and determinants for (1) genomic test ordering and (2) delivery of targeted therapies. A key determinant of molecular testing was role alignment. The dominant expectation for oncologists to order and interpret genomic tests is at odds with their role as treatment decision-makers' and pathologists' typical role to stage tumors. Programs in which pathologists considered genomic test ordering as part of their staging responsibilities reported high and timely testing rates. Determinants of treatment delivery were contingent on resources and ability to offset delivery costs, which low- volume programs could not do. Rural programs faced additional treatment delivery challenges. CONCLUSIONS We identified novel determinants of targeted therapy delivery that potentially could be addressed through role re-alignment. Standardized, pathology-initiated genomic testing may prove fruitful in ensuring patients eligible for targeted therapy are identified, even if the care they need cannot be delivered at small and rural sites which may have distinct challenges in treatment delivery. Incorporating behavior specification and Rummler-Brache process mapping with determinant analysis may extend its usefulness beyond the identification of the need for contextual adaptation.
Collapse
Affiliation(s)
- Shellie D. Ellis
- University of Kansas School of Medicine, 3901 Rainbow Blvd., Kansas City, KS 66610 USA
| | - Joanna Veazey Brooks
- University of Kansas School of Medicine, 3901 Rainbow Blvd., Kansas City, KS 66610 USA
| | - Sarah A. Birken
- Wake Forest University School of Medicine, 525 Vine Street, Winston-Salem, NC 27101 USA
| | - Emily Morrow
- Kansas City Kansas Community College, 7250 State Ave., Kansas City, KS 66112 USA
| | - Zachary S. Hilbig
- University of Kansas School of Medicine, 3901 Rainbow Blvd., Kansas City, KS 66610 USA
| | | | - Anita Y. Kinney
- Rutgers Cancer Institute of New Jersey, Rutgers University, 195 Little Albany St., New Brunswick, NJ 08901 USA
| | - Edward F. Ellerbeck
- University of Kansas School of Medicine, 3901 Rainbow Blvd., Kansas City, KS 66610 USA
| |
Collapse
|
9
|
Husereau D, Villalba E, Muthu V, Mengel M, Ivany C, Steuten L, Spinner DS, Sheffield B, Yip S, Jacobs P, Sullivan T, Arshoff L. Progress toward Health System Readiness for Genome-Based Testing in Canada. Curr Oncol 2023; 30:5379-5394. [PMID: 37366891 DOI: 10.3390/curroncol30060408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 05/29/2023] [Accepted: 05/30/2023] [Indexed: 06/28/2023] Open
Abstract
(1) Background: Genomic medicine harbors the real potential to improve the health and healthcare journey of patients, care provider experiences, and improve the health system efficiency-even reducing healthcare costs. There is expected to be an exponential growth in medically necessary new genome-based tests and test approaches in the coming years. Testing can also create scientific research and commercial opportunities beyond healthcare decision making. The purpose of this research is to generate a better understanding of Canada's state of readiness for genomic medicine, and to provide some insights for other healthcare systems. (2) Methods: A mixed-methods approach of a review of the literature and key informant interviews with a purposive sample of experts was used. The health system readiness was assessed using a previously published set of conditions. (3) Results: Canada has created some of the established conditions, but further action needs to be taken to improve the state of readiness for genome-based medicine. The important gaps to be filled are the need for linked information systems and data integration; evaluative processes that are timely and transparent; navigational tools for care providers; dedicated funding to facilitate rapid onboarding and support test development and proficiency testing; and broader engagement with innovation stakeholders beyond care providers and patients. These findings highlight the role of the organizational context, social influence, and other factors that are known to affect the diffusion of innovation within health systems.
Collapse
Affiliation(s)
- Don Husereau
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, ON K1G 5Z3, Canada
| | - Eva Villalba
- Coalition Priorité Cancer au Québec, Saint-Lambert, QC J4P 2J7, Canada
| | - Vivek Muthu
- Marivek Healthcare Consulting, Epsom KT18 7PF, UK
| | - Michael Mengel
- Department of Laboratory Medicine & Pathology, University of Alberta, Edmonton, AB T6G 2S2, Canada
| | - Craig Ivany
- Provincial Health Services Authority, Vancouver, British Columbia, Vancouver, BC V5Z 1G1, Canada
| | - Lotte Steuten
- Office of Health Economics, London SE1 2HB, UK
- City University of London, London EC1V 0HB, UK
| | - Daryl S Spinner
- Menarini Silicon Biosystems Inc., Huntingdon Valley, PA 19006, USA
| | | | - Stephen Yip
- Department of Pathology & Laboratory Medicine, Faculty of Medicine, University of British Columbia, Vancouver, BC V6T 1Z7, Canada
| | - Philip Jacobs
- Faculty of Medicine and Dentistry, University of Alberta, Edmonton, AB T6G 2R3, Canada
| | - Terrence Sullivan
- Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, ON M5T 3M6, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada
| | - Larry Arshoff
- Diagnosis, Solutions & Results Inc., Thornhill, ON L4J 7N5, Canada
| |
Collapse
|
10
|
Callahan TJ, Stefanski AL, Wyrwa JM, Zeng C, Ostropolets A, Banda JM, Baumgartner WA, Boyce RD, Casiraghi E, Coleman BD, Collins JH, Deakyne Davies SJ, Feinstein JA, Lin AY, Martin B, Matentzoglu NA, Meeker D, Reese J, Sinclair J, Taneja SB, Trinkley KE, Vasilevsky NA, Williams AE, Zhang XA, Denny JC, Ryan PB, Hripcsak G, Bennett TD, Haendel MA, Robinson PN, Hunter LE, Kahn MG. Ontologizing health systems data at scale: making translational discovery a reality. NPJ Digit Med 2023; 6:89. [PMID: 37208468 PMCID: PMC10196319 DOI: 10.1038/s41746-023-00830-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2022] [Accepted: 04/28/2023] [Indexed: 05/21/2023] Open
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
Collapse
Affiliation(s)
- Tiffany J Callahan
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA.
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA.
| | - Adrianne L Stefanski
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Jordan M Wyrwa
- Department of Physical Medicine and Rehabilitation, School of Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Chenjie Zeng
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Anna Ostropolets
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Juan M Banda
- Department of Computer Science, Georgia State University, Atlanta, GA, 30303, USA
| | - William A Baumgartner
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh School of Medicine, Pittsburgh, PA, 15260, USA
| | - Elena Casiraghi
- Computer Science, Università degli Studi di Milano, Milan, Italy
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Ben D Coleman
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Janine H Collins
- Department of Haematology, University of Cambridge, Cambridge, UK
| | - Sara J Deakyne Davies
- Department of Research Informatics & Data Science, Analytics Resource Center, Children's Hospital Colorado, Aurora, CO, 80045, USA
| | - James A Feinstein
- Adult and Child Center for Health Outcomes Research and Delivery Science (ACCORDS), University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Asiyah Y Lin
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Blake Martin
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | | | | | - Justin Reese
- Division of Environmental Genomics and Systems Biology, Lawrence Berkeley National Laboratory, Berkeley, CA, 94720, USA
| | | | - Sanya B Taneja
- Intelligent Systems Program, University of Pittsburgh, Pittsburgh, PA, 15260, USA
| | - Katy E Trinkley
- Department of Family Medicine, University of Colorado Anschutz School of Medicine, Aurora, CO, 80045, USA
| | - Nicole A Vasilevsky
- Translational and Integrative Sciences Lab, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
| | - Andrew E Williams
- Tufts Institute for Clinical Research and Health Policy Studies, Tufts University, Boston, MA, 02155, USA
| | - Xingmin A Zhang
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Joshua C Denny
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, 20892, USA
| | - Patrick B Ryan
- Janssen Research and Development, Raritan, NJ, 08869, USA
| | - George Hripcsak
- Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY, 10032, USA
| | - Tellen D Bennett
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Melissa A Haendel
- Departments of Biomedical Informatics and Pediatrics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA
| | - Lawrence E Hunter
- Computational Bioscience Program, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| | - Michael G Kahn
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, 80045, USA
| |
Collapse
|
11
|
Alarcón Garavito GA, Moniz T, Déom N, Redin F, Pichini A, Vindrola-Padros C. The implementation of large-scale genomic screening or diagnostic programmes: A rapid evidence review. Eur J Hum Genet 2023; 31:282-295. [PMID: 36517584 PMCID: PMC9995480 DOI: 10.1038/s41431-022-01259-8] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2022] [Revised: 11/17/2022] [Accepted: 11/22/2022] [Indexed: 12/15/2022] Open
Abstract
Genomic healthcare programmes, both in a research and clinical context, have demonstrated a pivotal opportunity to prevent, diagnose, and treat rare diseases. However, implementation factors could increase overall costs and affect uptake. As well, uncertainties remain regarding effective training, guidelines and legislation. The purpose of this rapid evidence review was to draw together the available global evidence on the implementation of genomic testing programmes, particularly on population-based screening and diagnostic programmes implemented at the national level, to understand the range of factors influencing implementation. This review involved a search of terms related to genomics, implementation and health care. The search was limited to peer-reviewed articles published between 2017-2022 and found in five databases. The review included thirty articles drawing on sixteen countries. A wide range of factors was cited as critical to the successful implementation of genomics programmes. These included having policy frameworks, regulations, guidelines; clinical decision support tools; access to genetic counselling; and education and training for healthcare staff. The high costs of implementing and integrating genomics into healthcare were also often barriers to stakeholders. National genomics programmes are complex and require the generation of evidence and addressing implementation challenges. The findings from this review highlight that there is a strong emphasis on addressing genomic education and engagement among varied stakeholders, including the general public, policymakers, and governments. Articles also emphasised the development of appropriate policies and regulatory frameworks to govern genomic healthcare, with a focus on legislation that regulates the collection, storage, and sharing of personal genomic data.
Collapse
Affiliation(s)
| | - Thomas Moniz
- Rapid Research Evaluation and Appraisal Lab (RREAL), University College London, 43-45 Foley Street, W1W 7TY, London, UK
| | - Noémie Déom
- Rapid Research Evaluation and Appraisal Lab (RREAL), University College London, 43-45 Foley Street, W1W 7TY, London, UK
| | - Federico Redin
- Rapid Research Evaluation and Appraisal Lab (RREAL), University College London, 43-45 Foley Street, W1W 7TY, London, UK
| | | | - Cecilia Vindrola-Padros
- Rapid Research Evaluation and Appraisal Lab (RREAL), University College London, 43-45 Foley Street, W1W 7TY, London, UK.
| |
Collapse
|
12
|
Best S, Long JC, Braithwaite J, Taylor N. Standardizing variation: Scaling up clinical genomics in Australia. Genet Med 2023; 25:100109. [PMID: 35115231 DOI: 10.1016/j.gim.2022.01.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 01/03/2022] [Accepted: 01/06/2022] [Indexed: 02/07/2023] Open
Abstract
PURPOSE Clinical genomics demands close interaction of physicians, laboratory scientists, and genetic professionals. Taking genomics to scale requires an understanding of the underlying processes from the perspective of nongenetic physicians who are new to the field. We identified components of the processes amenable to adaptation when scaling up clinical genomics. METHODS Semistructured interviews informed by the Theoretical Domains Framework with nongenetic physicians, who were using clinical genomics in practice, were guided by an annotated process map with 7 steps following the patient's journey. Findings from the individual maps were synthesized into an overview process map and a series of individual maps by common location and specialty. Interviews were analyzed using the Theoretical Domains Framework. RESULTS In total, 16 nongenetic physicians (eg, nephrologists, immunologists) participated, generating 1 overview and 10 individual process maps. Sixteen common steps were identified across clinical specialties and locations, with variations over 9 steps. We report the potential for standardization across these 9 steps. CONCLUSION When scaling up complex interventions, it is essential to identify steps where variation can be accommodated. With these results we show how process mapping can be used to identify steps where variation is acceptable during scale up to accommodate adaptation to local context, allowing for the inevitable evolution of factors influencing ongoing implementation and sustainability.
Collapse
Affiliation(s)
- Stephanie Best
- Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia; Australian Genomics, Murdoch Children's Research Institute, Melbourne, Victoria, Australia.
| | - Janet C Long
- Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Jeffrey Braithwaite
- Australian Institute of Health Innovation, Faculty of Medicine, Health and Human Sciences, Macquarie University, Sydney, New South Wales, Australia
| | - Natalie Taylor
- School of Population Health, UNSW Sydney, Sydney, New South Wales, Australia
| |
Collapse
|
13
|
Shaw T, Fok R, Courtney E, Li ST, Chiang J, Ngeow J. Missed diagnosis or misdiagnosis: Common pitfalls in genetic testing. Singapore Med J 2023; 64:67-73. [PMID: 36722519 PMCID: PMC9979802 DOI: 10.4103/singaporemedj.smj-2021-467] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Genetic testing has the power to identify individuals with increased predisposition to disease, allowing individuals the opportunity to make informed management, treatment and reproductive decisions. As genomic medicine continues to be integrated into aspects of everyday patient care and the indications for genetic testing continue to expand, genetic services are increasingly being offered by non-genetic clinicians. The current complexities of genetic testing highlight the need to support and ensure non-genetic professionals are adequately equipped with the knowledge and skills to provide services. We describe a series of misdiagnosed/mismanaged cases, highlighting the common pitfalls in genetic testing to identify the knowledge gaps and where education and support is needed. We highlight that education focusing on differential diagnoses, test selection and result interpretation is needed. Collaboration and communication between genetic and non-genetic clinicians and integration of genetic counsellors into different medical settings are important. This will minimise the risks and maximise the benefits of genetic testing, ensuring adverse outcomes are mitigated.
Collapse
Affiliation(s)
- Tarryn Shaw
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Rose Fok
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Eliza Courtney
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Shao-Tzu Li
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Jianbang Chiang
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore,Oncology Academic Clinical Program, Duke-NUS Medical School, Singapore
| | - Joanne Ngeow
- Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore,Oncology Academic Clinical Program, Duke-NUS Medical School, Singapore,Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore,Correspondence: A/Prof Joanne Ngeow, Lee Kong Chian School of Medicine, Nanyang Technological University, Novena Campus, Headquarters & Clinical Sciences Building, 11 Mandalay Road, 308232, Singapore. E-mail:
| |
Collapse
|
14
|
Gawronski BE, Cicali EJ, McDonough CW, Cottler LB, Duarte JD. Exploring perceptions, knowledge, and attitudes regarding pharmacogenetic testing in the medically underserved. Front Genet 2023; 13:1085994. [PMID: 36712853 PMCID: PMC9880414 DOI: 10.3389/fgene.2022.1085994] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2022] [Accepted: 12/28/2022] [Indexed: 01/15/2023] Open
Abstract
Introduction: Pharmacogenetic testing may hold promise in addressing health disparities, as medically underserved patients appear to be prescribed medications with pharmacogenetic guidelines at higher rates. While routine clinical implementation of testing in medically underserved populations has not yet been achieved, using patient perspectives to inform implementation should increase the likelihood of success. The aim of this study was to assess the perceptions, knowledge, and attitudes regarding pharmacogenetic testing in medically underserved patients. Methods: We developed a survey instrument to assess respondent views on pharmacogenetic testing. The survey instrument was developed through a process of literature review, expert input, iterative pilot testing, and final refinement. The survey instrument was fielded to US adults with an estimated household income of $42,000 per year or less. Results: During the survey instrument development, 59 pilot testers provided 133 comments which lead to 38 revisions to the survey instrument. The nationwide survey resulted in 1,060 respondents, of which half (49.8%) reported having no health insurance or being on Medicaid. Most patients (78.9%) had not previously heard of pharmacogenetic testing. After being provided an explanation of pharmacogenetic testing, 60.5% were very or moderately interested in receiving testing if there were no cost and 75.8% of respondents agreed or strongly agreed that pharmacogenetic testing should be available to help with medication selection regardless of cost. Respondents shared that their greatest concern with pharmacogenetic testing was that the test would cost them money, which was expressed by over half (52.7%). This was followed by concerns that the results could reveal a risk for a disease, could affect health insurance, and would not improve care. Discussion: Our results indicate a strong interest in pharmacogenetic testing and identify key perceptions, attitudes, concerns, and potential barriers that can be addressed as pharmacogenetic testing is clinically implemented in medically underserved patient populations.
Collapse
Affiliation(s)
- Brian E. Gawronski
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Emily J. Cicali
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Caitrin W. McDonough
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, United States
| | - Linda B. Cottler
- Department of Epidemiology, College of Public Health and Health Professions and College of Medicine, University of Florida, Gainesville, FL, United States
| | - Julio D. Duarte
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL, United States
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, Gainesville, FL, United States
| |
Collapse
|
15
|
Moyo E, Moyo P, Mashe T, Dzobo M, Chitungo I, Dzinamarira T. Implementation of Public Health Genomics in Africa: Lessons from the COVID-19 pandemic, challenges, and recommendations. J Med Virol 2023; 95:e28295. [PMID: 36366938 PMCID: PMC9877907 DOI: 10.1002/jmv.28295] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2022] [Revised: 10/18/2022] [Accepted: 11/08/2022] [Indexed: 11/13/2022]
Abstract
Public Health Genomics (PHG) is a relatively new field. The wide application of genomic technologies played a pivotal role in elucidating the full genomic sequence of the SARS-CoV-2 virus. This breakthrough proved to be the starting point in the manufacture of diagnostic kits and the subsequent making of vaccines. Beyond the COVID-19 pandemic, many African countries can take advantage of the various investments in genomic technologies to introduce and intensify the use of genomics for public health gain. Public Health Genomics effectively monitors, prevents, and manages non-communicable and infectious diseases. However, there are several challenges to implementing PHG in Africa. In this perspective article, we discuss the utilization of PHG during the COVID-19 pandemic, the lessons learned from using PHG to manage and contain the COVID-19 pandemic, as well as potential challenges Africa may face when putting PHG into practice compared to challenges of other regions. We also discuss our recommendations for overcoming these challenges.
Collapse
Affiliation(s)
- Enos Moyo
- Medical Centre OshakatiOshakatiNamibia
| | | | | | - Mathias Dzobo
- School of Health Systems and Public HealthUniversity of PretoriaPretoriaSouth Africa
| | - Itai Chitungo
- College of Medicine and Health SciencesUniversity of ZimbabweHarareZimbabwe
| | - Tafadzwa Dzinamarira
- School of Health Systems and Public HealthUniversity of PretoriaPretoriaSouth Africa
| |
Collapse
|
16
|
Espinoza Moya ME, Guertin JR, Dorval M, Lapointe J, Bouchard K, Nabi H, Laberge M. Examining interprofessional collaboration in oncogenetic service delivery models for hereditary cancers: a scoping review protocol. BMJ Open 2022; 12:e066802. [PMID: 36523215 PMCID: PMC9748975 DOI: 10.1136/bmjopen-2022-066802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
INTRODUCTION In a context of limited genetic specialists, collaborative models have been proposed to ensure timely access to high quality oncogenetic services for individuals with inherited cancer susceptibility. Yet, extensive variability in the terminology used and lack of a clear understanding of how interprofessional collaboration is operationalised and evaluated currently constrains the development of a robust evidence base on the value of different approaches used to optimise access to these services. To fill in this knowledge gap, this scoping review aims to systematically unpack the nature and extent of collaboration proposed by these interventions, and synthesise the evidence available on their implementation, effectiveness and economic impact. METHODS AND ANALYSIS Following the Joanna Briggs Institute guidelines for scoping reviews, a comprehensive literature search will be conducted to identify peer-reviewed and grey literature on collaborative models used for adult patients with, or at increased risk of, hereditary breast, ovarian, colorectal and prostate cancers. An initial search was developed for Medline, Embase, CINAHL (Cumulative Index to Nursing and Allied Health Literature), Cochrane and Web of Science on 13 June 2022 and will be complemented by searches in Google and relevant websites. Documents describing either the theory of change, planning, implementation and/or evaluation of these interventions will be considered for inclusion. Results will be summarised descriptively and used to compare relevant model characteristics and synthesise evidence available on their implementation, effectiveness and economic impact. This process is expected to guide the development of a definition and typology of collaborative models in oncogenetics that could help strengthen the knowledge base on these interventions. Moreover, because we will be mapping the existing evidence on collaborative models in oncogenetics, the proposed review will help us identify areas where additional research might be needed. ETHICS AND DISSEMINATION This research does not require ethics approval. Results from this review will be disseminated through peer-reviewed articles and conferences.
Collapse
Affiliation(s)
- Maria Eugenia Espinoza Moya
- Population Health and Optimal Health Practices Unit, Centre de Recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval, Quebec City, Quebec, Canada
- Département des opérations et systèmes de décision, Faculté des sciences de l'administration, Université Laval, Quebec City, Quebec, Canada
| | - Jason Robert Guertin
- Population Health and Optimal Health Practices Unit, Centre de Recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval, Quebec City, Quebec, Canada
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada
| | - Michel Dorval
- Oncology Division, Centre de Recherche du CHU de Québec-Université Laval, Quebec City, Quebec, Canada
- Faculty of Pharmacy, Université Laval, Quebec City, Quebec, Canada
- CISSS, Chaudière-Appalaches Research Center, Lévis, Québec, Canada
| | - Julie Lapointe
- Oncology Division, Centre de Recherche du CHU de Québec-Université Laval, Quebec City, Quebec, Canada
| | - Karine Bouchard
- Département de cancérologie, CHU de Québec-Université Laval, Quebec City, Quebec, Canada
| | - Hermann Nabi
- Department of Social and Preventive Medicine, Faculty of Medicine, Université Laval, Quebec City, Quebec, Canada
- Oncology Division, Centre de Recherche du CHU de Québec-Université Laval, Quebec City, Quebec, Canada
| | - Maude Laberge
- Population Health and Optimal Health Practices Unit, Centre de Recherche du Centre hospitalier universitaire (CHU) de Québec-Université Laval, Quebec City, Quebec, Canada
- Département des opérations et systèmes de décision, Faculté des sciences de l'administration, Université Laval, Quebec City, Quebec, Canada
- Vitam, Centre de recherche en santé durable, Laval University, Quebec City, Quebec, Canada
| |
Collapse
|
17
|
Jia T, Wu C, Hu X, Li S, Zhang X, Cai Y, Chen J, Shi L, Lu CY, Nie X. Physicians' Knowledge, Attitude, and Experience of Pharmacogenomic Testing in China. J Pers Med 2022; 12:jpm12122021. [PMID: 36556242 PMCID: PMC9783535 DOI: 10.3390/jpm12122021] [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: 10/01/2022] [Revised: 12/01/2022] [Accepted: 12/05/2022] [Indexed: 12/13/2022] Open
Abstract
(1) Background: As prescribers, physicians play a decisive role in applying and promoting pharmacogenomic (PGx) testing in clinical practices. So far, little is known about physicians' perspectives on PGx testing in China. The aim of this study was to assess physicians' knowledge of, attitude towards, and experience of PGx testing in China. (2) Methods: A 39-question online survey was developed. Participants were physicians recruited through two platforms, MEDLINKER and "Dazhuanjia". (3) Results: A total of 450 respondents completed the survey and 366 questionnaires were eligible for analysis based on the inclusion criteria. Among all included physicians, 275 (75.1%) had heard of PGx testing before. More than half rated their knowledge of PGx testing as "Fair" (61.5%) while 20.0% chose "Excellent" or "Good" and 18.6% chose "Poor" or "Terrible". "Guidelines, consensus, and treatment paths for disease diagnosis and treatment" (72.7%) were the most preferred sources of information about PGx testing. Respondents were confident in their personal capacity to conduct PGx, with an average score of 3.30 ± 0.09 (out of 5.00). Most respondents (75.6%) believed that PGx could "help to improve efficacy and reduce the incidence of adverse reactions". Targeted cancer therapy (score 78.95 ± 1.26 out of 100) was considered the field where PGx testing had its highest value. Lack of professionals and knowledge (n = 186, 67.6%), high costs of testing (n = 170, 61.8%), and lack of hospitals to offer PGx testing (n = 166, 60.4%) were identified as the primary obstacles to increasing the uptake of PGx testing in China. Academic conference (n = 213, 72.4%) was considered the most efficient way for physicians to obtain information about PGx testing. (4) Conclusions: Physicians in China have poor knowledge about PGx testing; nonetheless, they generally had confidence in their capacity to order PGx testing and positive attitudes towards the use of PGx testing in routine clinical practices. Future efforts to promote the uptake of PGx testing should focus on foundational education and practical training.
Collapse
Affiliation(s)
- Tong Jia
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Caiying Wu
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Xiaowen Hu
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Sicong Li
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Xinyi Zhang
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Yuchun Cai
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
| | - Jing Chen
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- International Research Center for Medicinal Administration, Peking University, Beijing 100191, China
| | - Luwen Shi
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- International Research Center for Medicinal Administration, Peking University, Beijing 100191, China
| | - Christine Y. Lu
- Department of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, Boston, MA 02215, USA
| | - Xiaoyan Nie
- Department of Pharmacy Administration and Clinical Pharmacy, School of Pharmaceutical Sciences, Peking University, Beijing 100191, China
- International Research Center for Medicinal Administration, Peking University, Beijing 100191, China
- Correspondence: ; Tel.: +86-10-8280-5880
| |
Collapse
|
18
|
Buchanan J, Goranitis I, Slade I, Kerasidou A, Sheehan M, Sideri K, Wordsworth S. Resource allocation in genetic and genomic medicine. J Community Genet 2022; 13:463-466. [PMID: 36152236 DOI: 10.1007/s12687-022-00608-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022] Open
Affiliation(s)
- J Buchanan
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK. .,National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford, UK.
| | - I Goranitis
- Health Economics Unit, Centre for Health Policy, Melbourne School of Population and Global Health, University of Melbourne, Melbourne, Australia.,Australian Genomics, Murdoch Childrens Research Institute, Melbourne, Australia
| | - I Slade
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.,Wokingham Borough Council, Wokingham, UK
| | - A Kerasidou
- Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - M Sheehan
- National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford, UK.,Ethox Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - K Sideri
- Department of Political Science and History, Panteion University of Social and Political Sciences, Athens, Greece
| | - S Wordsworth
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK.,National Institute for Health Research (NIHR) Oxford Biomedical Research Centre, Oxford, UK
| |
Collapse
|
19
|
Kopylova OV, Ershova AI, Efimova IA, Blokhina AV, Limonova AS, Borisova AL, Pokrovskaya MS, Drapkina OM. Electronic medical records and biobanking. КАРДИОВАСКУЛЯРНАЯ ТЕРАПИЯ И ПРОФИЛАКТИКА 2022. [DOI: 10.15829/1728-8800-2022-3425] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Biosample preservation for future research is a fundamental component of translational medicine. At the same time, the value of stored biosamples is largely determined by the presence of related clinical data and other information. Electronic medical records are a unique source of a large amount of information received over a long period of time. In this regard, genetic and other types of data obtained from the biosample analysis can be associated with phenotypic and other types of information stored in electronic medical records, which pushes the boundaries in large-scale genetic research and improves healthcare. The aim of this review was to analyze the literature on the potential of combining electronic medical records and biobank databases in research and clinical practice.
Collapse
Affiliation(s)
- O. V. Kopylova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. I. Ershova
- National Medical Research Center for Therapy and Preventive Medicine
| | - I. A. Efimova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. V. Blokhina
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. S. Limonova
- National Medical Research Center for Therapy and Preventive Medicine
| | - A. L. Borisova
- National Medical Research Center for Therapy and Preventive Medicine
| | - M. S. Pokrovskaya
- National Medical Research Center for Therapy and Preventive Medicine
| | - O. M. Drapkina
- National Medical Research Center for Therapy and Preventive Medicine
| |
Collapse
|
20
|
McDermott JH, Wright S, Sharma V, Newman WG, Payne K, Wilson P. Characterizing pharmacogenetic programs using the consolidated framework for implementation research: A structured scoping review. Front Med (Lausanne) 2022; 9:945352. [PMID: 36059837 PMCID: PMC9433561 DOI: 10.3389/fmed.2022.945352] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2022] [Accepted: 07/29/2022] [Indexed: 12/11/2022] Open
Abstract
Several healthcare organizations have developed pre-emptive pharmacogenetic testing programs, where testing is undertaken prior to the prescription of a medicine. This review characterizes the barriers and facilitators which influenced the development of these programs. A bidirectional citation searching strategy identified relevant publications before a standardized data extraction approach was applied. Publications were grouped by program and data synthesis was undertaken using the Consolidated Framework for Implementation Research (CFIR). 104 publications were identified from 40 programs and 4 multi-center initiatives. 26 (66%) of the programs were based in the United States and 95% in high-income countries. The programs were heterogeneous in their design and scale. The Characteristics of the Intervention, Inner Setting, and Process domains were referenced by 92.5, 80, and 77.5% of programs, respectively. A positive institutional culture, leadership engagement, engaging stakeholders, and the use of clinical champions were frequently described as facilitators to implementation. Clinician self-efficacy, lack of stakeholder knowledge, and the cost of the intervention were commonly cited barriers. Despite variation between the programs, there were several similarities in approach which could be categorized via the CFIR. These form a resource for organizations planning the development of pharmacogenetic programs, highlighting key facilitators which can be leveraged to promote successful implementation.
Collapse
Affiliation(s)
- John H. McDermott
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, United Kingdom
- *Correspondence: John H. McDermott,
| | - Stuart Wright
- Division of Population Health, Manchester Centre for Health Economics, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| | - Videha Sharma
- Division of Informatics, Centre for Health Informatics, Imaging and Data Science, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| | - William G. Newman
- Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University Hospitals NHS Foundation Trust, Manchester, United Kingdom
- Division of Evolution, Infection and Genomics, School of Biological Sciences, The University of Manchester, Manchester, United Kingdom
| | - Katherine Payne
- Division of Population Health, Manchester Centre for Health Economics, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| | - Paul Wilson
- Division of Population Health, Centre for Primary Care and Health Services Research, Health Services Research and Primary Care, School of Health Sciences, The University of Manchester, Manchester, United Kingdom
| |
Collapse
|
21
|
Salloum RG, Bishop JR, Elchynski AL, Smith DM, Rowe E, Blake KV, Limdi NA, Aquilante CL, Bates J, Beitelshees AL, Cipriani A, Duong BQ, Empey PE, Formea CM, Hicks JK, Mroz P, Oslin D, Pasternak AL, Petry N, Ramsey LB, Schlichte A, Swain SM, Ward KM, Wiisanen K, Skaar TC, Van Driest SL, Cavallari LH, Tuteja S. Best-worst scaling methodology to evaluate constructs of the Consolidated Framework for Implementation Research: application to the implementation of pharmacogenetic testing for antidepressant therapy. Implement Sci Commun 2022; 3:52. [PMID: 35568931 PMCID: PMC9107643 DOI: 10.1186/s43058-022-00300-7] [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: 02/23/2022] [Accepted: 04/25/2022] [Indexed: 11/10/2022] Open
Abstract
Background Despite the increased demand for pharmacogenetic (PGx) testing to guide antidepressant use, little is known about how to implement testing in clinical practice. Best–worst scaling (BWS) is a stated preferences technique for determining the relative importance of alternative scenarios and is increasingly being used as a healthcare assessment tool, with potential applications in implementation research. We conducted a BWS experiment to evaluate the relative importance of implementation factors for PGx testing to guide antidepressant use. Methods We surveyed 17 healthcare organizations that either had implemented or were in the process of implementing PGx testing for antidepressants. The survey included a BWS experiment to evaluate the relative importance of Consolidated Framework for Implementation Research (CFIR) constructs from the perspective of implementing sites. Results Participating sites varied on their PGx testing platform and methods for returning recommendations to providers and patients, but they were consistent in ranking several CFIR constructs as most important for implementation: patient needs/resources, leadership engagement, intervention knowledge/beliefs, evidence strength and quality, and identification of champions. Conclusions This study demonstrates the feasibility of using choice experiments to systematically evaluate the relative importance of implementation determinants from the perspective of implementing organizations. BWS findings can inform other organizations interested in implementing PGx testing for mental health. Further, this study demonstrates the application of BWS to PGx, the findings of which may be used by other organizations to inform implementation of PGx testing for mental health disorders. Supplementary Information The online version contains supplementary material available at 10.1186/s43058-022-00300-7.
Collapse
Affiliation(s)
- Ramzi G Salloum
- University of Florida Clinical and Translational Science Institute, Gainesville, FL, USA.,University of Florida College of Medicine, Gainesville, FL, USA
| | - Jeffrey R Bishop
- University of Minnesota Medical School, Minneapolis, MN, USA.,University of Minnesota College of Pharmacy, Minneapolis, MN, USA
| | | | - D Max Smith
- MedStar Health, Georgetown University Medical Center, Washington, DC, USA
| | - Elizabeth Rowe
- Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Nita A Limdi
- University of Alabama Heersink School of Medicine, Birmingham, AL, USA
| | | | - Jill Bates
- Durham VA Healthcare System, Durham, NC, USA
| | | | - Amber Cipriani
- University of North Carolina Medical Center, Chapel Hill, NC, USA
| | | | - Philip E Empey
- University of Pittsburgh School of Pharmacy, Pittsburgh, PA, USA
| | | | | | - Pawel Mroz
- University of Minnesota Medical School, Minneapolis, MN, USA
| | - David Oslin
- Corporal Michael J. Cresenz VA Medical Center, Philadelphia, PA, USA
| | - Amy L Pasternak
- University of Michigan College of Pharmacy, Ann Arbor, MI, USA
| | - Natasha Petry
- North Dakota State University/Sanford Health, Fargo, ND, USA
| | - Laura B Ramsey
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | | | - Sandra M Swain
- MedStar Health, Georgetown University Medical Center, Washington, DC, USA
| | - Kristen M Ward
- University of Michigan College of Pharmacy, Ann Arbor, MI, USA
| | | | - Todd C Skaar
- Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Larisa H Cavallari
- University of Florida Clinical and Translational Science Institute, Gainesville, FL, USA.,University of Florida College of Pharmacy, Gainesville, FL, USA
| | - Sony Tuteja
- University of Pennsylvania Perelman School of Medicine, Smilow Center for Translational Research, 3400 Civic Center Boulevard, Bldg. 421 11th Floor, Room 143, Philadelphia, PA, 19104-5158, USA.
| |
Collapse
|
22
|
Iwelunmor J, Ezechi O, Obiezu-Umeh C, Oladele D, Nwaozuru U, Aifah A, Gyamfi J, Gbajabiamila T, Musa AZ, Onakomaiya D, Rakhra A, Jiyuan H, Odubela O, Idigbe I, Engelhart A, Tayo BO, Ogedegbe G. Factors influencing the integration of evidence-based task-strengthening strategies for hypertension control within HIV clinics in Nigeria. Implement Sci Commun 2022; 3:43. [PMID: 35428342 PMCID: PMC9013085 DOI: 10.1186/s43058-022-00289-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 03/23/2022] [Indexed: 11/29/2022] Open
Abstract
Background Evidence-based task-strengthening strategies for hypertension (HTN) control (TASSH) are not readily available for patients living with HIV in sub-Saharan Africa where the dual burden of HTN and HIV remains high. We are conducting a cluster randomized controlled trial comparing the effectiveness of practice facilitation versus a self-directed control (i.e., receipt of TASSH with no practice facilitation) in reducing blood pressure and increasing the adoption of task-strengthening strategies for HTN control within HIV clinics in Nigeria. Prior to implementing the trial, we conducted formative research to identify factors that may influence the integration of TASSH within HIV clinics in Nigeria. Methods This mixed-methods study was conducted with purposively selected healthcare providers at 29 HIV clinics, followed by a 1-day stakeholder meeting with 19 representatives of HIV clinics. We collected quantitative practice assessment data using two instruments: (a) an adapted Service Availability and Readiness Assessment (SARA) tool to assess the capacity of the clinic to manage NCDs and (b) Implementation Climate Scale to assess the degree to which there is a strategic organizational climate supportive of the evidence-based practice implementation. The quantitative data were analyzed using descriptive statistics and measures of scale reliability. We also used the Consolidated Framework for Implementation Research (CFIR), to thematically analyze qualitative data generated and relevant to the aims of this study. Results Across the 29 clinics surveyed, the focus on TASSH (mean=1.77 (SD=0.59)) and educational support (mean=1.32 (SD=0.68)) subscales demonstrated the highest mean score, with good–excellent internal consistency reliability (Cronbach’s alphas ranging from 0.84 to 0.96). Within the five CFIR domains explored, the major facilitators of the intervention included relative advantage of TASSH compared to current practice, compatibility with clinic organizational structures, support of patients’ needs, and intervention alignment with national guidelines. Barriers included the perceived complexity of TASSH, weak referral network and patient tracking mechanism within the clinics, and limited resources and diagnostic equipment for HTN. Conclusion Optimizing healthcare workers’ implementation of evidence-based TASSH within HIV clinics requires attention to both the implementation climate and contextual factors likely to influence adoption and long-term sustainability. These findings have implications for the development of effective practice facilitation strategies to further improve the delivery and integration of TASSH within HIV clinics in Nigeria. Trial registration ClinicalTrials.gov, NCT04704336 Supplementary Information The online version contains supplementary material available at 10.1186/s43058-022-00289-z.
Collapse
|
23
|
Community pharmacists’ experience relying on select implementation strategies in the delivery of comprehensive medication management. J Am Pharm Assoc (2003) 2022; 62:1648-1653.e1. [DOI: 10.1016/j.japh.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2021] [Revised: 03/22/2022] [Accepted: 04/07/2022] [Indexed: 11/22/2022]
|
24
|
A Model for the Integration of Genome Sequencing into a Paediatric Cardiology Clinic. Can J Cardiol 2022; 38:1454-1457. [DOI: 10.1016/j.cjca.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 04/06/2022] [Accepted: 04/18/2022] [Indexed: 11/17/2022] Open
|
25
|
Drug-drug-gene interaction risk among opioid users in the U.S. Department of Veterans Affairs. Pain 2022; 163:2390-2397. [PMID: 35319502 DOI: 10.1097/j.pain.0000000000002637] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2021] [Accepted: 02/13/2022] [Indexed: 11/25/2022]
Abstract
ABSTRACT Response to analgesic therapy is influenced by several factors including genetics and drug-drug interactions. Pharmacogenetic (PGx) variants in the CYP2D6 gene modify response to opioids by altering drug metabolism. We sought to determine the potential impact of PGx testing on the care of Veterans with noncancer pain prescribed opioids metabolized by CYP2D6 (codeine, hydrocodone, or tramadol). A retrospective analysis was performed within the Veterans Health Administration (VHA) evaluating prescription records for pain medications metabolized by CYP2D6 and interacting drugs from 2012-2017. Among 2,436,654 VHA pharmacy users with at least one opioid prescription, 34% met the definition of chronic use (longer than 90 days with more than 10 prescriptions or 120 days- supply). Opioids were commonly co-prescribed with antidepressants interacting with CYP2D6 (28%). An estimated 21.6% (n=526,905) of these patients are at elevated risk of an undesirable response to their opioid medication based on predicted phenotypes and drug-drug interactions: 3.5% are predicted CYP2D6 ultrarapid metabolizers and at increased risk for toxicity, 5.4% are poor metabolizer at higher risk for nonresponse, and 12.8% are normal or intermediate metabolizers co-prescribed a CYP2D6 inhibitor leading to phenoconversion into poor metabolizer. Despite the high rate of co-prescription of opioids and interacting drugs, CYP2D6 testing was infrequent in the sample (0.02%) and chart review suggest that test results were used to optimize antidepressant treatments rather than pain medications. Using pharmacogenetic testing combined with consideration of phenoconversion may allow for an enhanced precision medicine approach to pain management in Veterans.
Collapse
|
26
|
Berrios C, Sadaro SK, Sandritter T, Wagner JA, Soden S, Black B, Abdel-Rahman S. Parental understanding and attitudes following pharmacogenomic testing for pediatric neuropsychiatric patients. Pharmacogenomics 2022; 23:345-354. [DOI: 10.2217/pgs-2022-0002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: This study explores parental understanding and attitudes around pharmacogenomic results in their child(ren). Patients and methods: In-depth interviews with parents whose child(ren) had received a pharmacogenomic testing panel for management of neuropsychiatric medications were completed. Interviews were analyzed for themes and accuracy of understanding of results. Results: In 18 parents interviewed, 49/63 (78%) of statements made regarding results were accurate. Differences in understanding were seen by clinic, number of medications and result type. Parents expected results to guide prescribing and perceived the greatest utility in results that could impact current care. Results predicting normal drug metabolism may create mixed feelings. Conclusion: Parents perceive utility in pharmacogenomic testing for their children. Challenges exist in understanding probabilistic and multifactorial information about pharmacogenomic results.
Collapse
Affiliation(s)
- Courtney Berrios
- Genomic Medicine Center, Children's Mercy, Kansas City, MO 64108, USA
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA
| | - Sophia K Sadaro
- Genomic Medicine Center, Children's Mercy, Kansas City, MO 64108, USA
| | - Tracy Sandritter
- Clinical Pharmacology, Toxicology, & Therapeutic Innovation, Children's Mercy, Kansas City, MO 64108, USA
- School of Pharmacy, University of Missouri Kansas City, Kansas City, MO 64108, USA
| | - Jennifer A Wagner
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA
- Clinical Pharmacology, Toxicology, & Therapeutic Innovation, Children's Mercy, Kansas City, MO 64108, USA
| | - Sarah Soden
- School of Medicine, University of Missouri Kansas City, Kansas City, MO 64108, USA
- Developmental & Behavioral Sciences, Children's Mercy, Kansas City, MO 64108, USA
| | - Benjamin Black
- The Thompson Center for Autism & Neurodevelopmental Disorders, University of Missouri, Columbia, MO 65201, USA
| | - Susan Abdel-Rahman
- Clinical Pharmacology, Toxicology, & Therapeutic Innovation, Children's Mercy, Kansas City, MO 64108, USA
- School of Pharmacy, University of Missouri Kansas City, Kansas City, MO 64108, USA
| |
Collapse
|
27
|
Shugg T, Pasternak AL, Luzum JA. Comparison of clinical pharmacogenetic recommendations across therapeutic areas. Pharmacogenet Genomics 2022; 32:51-59. [PMID: 34412102 PMCID: PMC8702450 DOI: 10.1097/fpc.0000000000000452] [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] [Indexed: 02/03/2023]
Abstract
OBJECTIVES Evaluations from pharmacogenetics implementation programs at major US medical centers have reported variability in the clinical adoption of pharmacogenetics across therapeutic areas. A potential cause for this variability may involve therapeutic area-specific differences in published pharmacogenetics recommendations to clinicians. To date, however, the potential for differences in clinical pharmacogenetics recommendations by therapeutic areas from prominent US guidance sources has not been assessed. Accordingly, our objective was to comprehensively compare essential elements from clinical pharmacogenetics recommendations contained within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and clinical practice guidelines from US professional medical organizations across therapeutic areas. METHODS We analyzed clinical pharmacogenetics recommendation elements within Clinical Pharmacogenetics Implementation Consortium guidelines, US Food and Drug Administration drug labels and professional clinical practice guidelines through 05/24/19. RESULTS We identified 606 unique clinical pharmacogenetics recommendations, with the most recommendations involving oncology (217 recommendations), hematology (79), psychiatry (65), cardiovascular (43) and anesthetic (37) medications. Within our analyses, we observed considerable variability across therapeutic areas within the following essential pharmacogenetics recommendation elements: the recommended clinical management strategy; the relevant genetic biomarkers; the organizations providing pharmacogenetics recommendations; whether routine genetic screening was recommended; and the time since recommendations were published. CONCLUSIONS On the basis of our results, we infer that observed differences in clinical pharmacogenetics recommendations across therapeutic areas may result from specific factors associated with individual disease states, the associated genetic biomarkers, and the characteristics of the organizations providing recommendations.
Collapse
Affiliation(s)
- Tyler Shugg
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, Indianapolis, IN
| | - Amy L. Pasternak
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI
| | - Jasmine A. Luzum
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI
| |
Collapse
|
28
|
Grande KJ, Dalton R, Moyer NA, Arwood MJ, Nguyen KA, Sumfest J, Ashcraft KC, Cooper-DeHoff RM. Assessment of a Manual Method versus an Automated, Probability-Based Algorithm to Identify Patients at High Risk for Pharmacogenomic Adverse Drug Outcomes in a University-Based Health Insurance Program. J Pers Med 2022; 12:jpm12020161. [PMID: 35207649 PMCID: PMC8878761 DOI: 10.3390/jpm12020161] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2021] [Revised: 12/21/2021] [Accepted: 12/29/2021] [Indexed: 12/21/2022] Open
Abstract
We compared patient cohorts selected for pharmacogenomic testing using a manual method or automated algorithm in a university-based health insurance network. The medication list was compiled from claims data during 4th quarter 2018. The manual method selected patients by number of medications by the health system’s list of medications for pharmacogenomic testing. The automated method used YouScript’s pharmacogenetic interaction probability (PIP) algorithm to select patients based on the probability that testing would result in detection of one or more clinically significant pharmacogenetic interactions. A total of 6916 patients were included. Patient cohorts selected by each method differed substantially, including size (manual n = 218, automated n = 286) and overlap (n = 41). The automated method was over twice as likely to identify patients where testing may reveal a clinically significant pharmacogenetic interaction than the manual method (62% vs. 29%, p < 0.0001). The manual method captured more patients with significant drug–drug or multi-drug interactions (80.3% vs. 40.2%, respectively, p < 0.0001), higher average number of significant drug interactions per patient (3.3 vs. 1.1, p < 0.0001), and higher average number of unique medications per patient (9.8 vs. 7.4, p < 0.0001). It is possible to identify a cohort of patients who would likely benefit from pharmacogenomic testing using manual or automated methods.
Collapse
Affiliation(s)
| | - Rachel Dalton
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA; (R.D.); (K.A.N.)
| | | | | | - Khoa A. Nguyen
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA; (R.D.); (K.A.N.)
| | - Jill Sumfest
- GatorCare, University of Florida, Gainesville, FL 32610, USA;
| | | | - Rhonda M. Cooper-DeHoff
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, Gainesville, FL 32610, USA; (R.D.); (K.A.N.)
- Division of Cardiology, College of Medicine, University of Florida, Gainesville, FL 32610, USA
- Correspondence: ; Tel.: +1-352-359-2658
| |
Collapse
|
29
|
Zhang J, Qi G, Han C, Zhou Y, Yang Y, Wang X, Liu S, Zhang X. The Landscape of Clinical Implementation of Pharmacogenetic Testing in Central China: A Single-Center Study. Pharmgenomics Pers Med 2021; 14:1619-1628. [PMID: 34934339 PMCID: PMC8684419 DOI: 10.2147/pgpm.s338198] [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: 09/24/2021] [Accepted: 12/02/2021] [Indexed: 11/23/2022] Open
Abstract
Purpose Pharmacogenetic testing is recognized as the major method for the individualized pharmacotherapy in clinical pharmacy practice, but information about the clinical implementation of pharmacogenetic testing in China is limited. The present study aimed to determine the situation of clinical implementation for pharmacogenetic testing in central China. Methods The study is conducted in the department of clinical pharmacy in The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China. We collected and analyzed pharmacogenetic testing results from November 1, 2013 to November 2, 2018 in our hospital, which were checked in the electronic medical record system. The main outcome measures were the number and type of pharmacogenetic testing across five years. Results A total of 47,265 (56.9% male, mean age = 51.5 years) pharmacogenetic testing results were obtained with an average annual rate of growth of 63.0% across five years. A 50.2% (23,748/47,265) of all the pharmacogenetic testing results were for the determination of cytochrome P450 2C19 (CYP2C19) *2, *3 genotypes, and 41.7% were for the methylene tetrahydrofolate reductase (MTHFR) C677T genotype. The number of departments performing the pharmacogenetic testing was 35, 63, 55, 52, 52 and 39 for 2013–2018, respectively, and the main top five departments were cardiology, psychiatry, ICU, cardiac surgery and intervention. Conclusion Clinical implementation of pharmacogenetic testing in China is growing rapidly, but the types and implementing departments of pharmacogenetic testing were limited. Our present study reported the real-world implementation modality of pharmacogenomic tests in China. It will help us to understand the testing of pharmacogenetics in China in order to promote the rational development of pharmacogenetics.
Collapse
Affiliation(s)
- Jingmin Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Guangzhao Qi
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Chao Han
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yubing Zhou
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Yongjie Yang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xinru Wang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Suna Liu
- Newborn Screening Center, Department of Pediatrics, The Third Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| | - Xiaojian Zhang
- Department of Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China.,Henan Key Laboratory for Precision Clinical Pharmacy, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, People's Republic of China
| |
Collapse
|
30
|
Dong OM, Roberts MC, Wu RR, Voils CI, Sperber N, Gavin KL, Bates J, Chanfreau-Coffinier C, Naglich M, Kelley MJ, Vassy JL, Sriram P, Heise CW, Rivas S, Ribeiro M, Chapman JG, Voora D. Evaluation of the Veterans Affairs Pharmacogenomic Testing for Veterans (PHASER) clinical program at initial test sites. Pharmacogenomics 2021; 22:1121-1133. [PMID: 34704830 DOI: 10.2217/pgs-2021-0089] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022] Open
Abstract
Aim: The first Plan-Do-Study-Act cycle for the Veterans Affairs Pharmacogenomic Testing for Veterans pharmacogenomic clinical testing program is described. Materials & methods: Surveys evaluating implementation resources and processes were distributed to implementation teams, providers, laboratory and health informatics staff. Survey responses were mapped to the Consolidated Framework for Implementation Research constructs to identify implementation barriers. The Expert Recommendation for Implementing Change strategies were used to address implementation barriers. Results: Survey response rate was 23-73% across personnel groups at six Veterans Affairs sites. Nine Consolidated Framework for Implementation Research constructs were most salient implementation barriers. Program revisions addressed these barriers using the Expert Recommendation for Implementing Change strategies related to three domains. Conclusion: Beyond providing free pharmacogenomic testing, additional implementation barriers need to be addressed for improved program uptake.
Collapse
Affiliation(s)
- Olivia M Dong
- Durham VA Health Care System, Durham, NC 27705, USA.,Department of Medicine, Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA
| | - Megan C Roberts
- Division of Pharmaceutical Outcomes & Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - R Ryanne Wu
- Durham VA Health Care System, Durham, NC 27705, USA.,Department of Medicine, Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA
| | - Corrine I Voils
- William S Middleton Memorial Veterans Hospital, Madison, WI 53705, USA.,Department of Surgery, University of Wisconsin School of Medicine & Public Health, Madison, WI 53792, USA
| | - Nina Sperber
- Duke Department of Population Health Sciences, Duke University School of Medicine, Durham, NC 27701, USA
| | - Kara L Gavin
- William S Middleton Memorial Veterans Hospital, Madison, WI 53705, USA.,Department of Surgery, University of Wisconsin School of Medicine & Public Health, Madison, WI 53792, USA
| | - Jill Bates
- Durham VA Health Care System, Durham, NC 27705, USA.,Division of Practice Advancement & Clinical Education, UNC Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Catherine Chanfreau-Coffinier
- VA Informatics & Computing Infrastructure (VINCI), Salt Lake City VA Health Care System, Salt Lake City, UT 84148, USA
| | - Michael Naglich
- Institute for Medical Research, Durham VA Medical Center, Durham, NC 27705, USA
| | - Michael J Kelley
- Durham VA Health Care System, Durham, NC 27705, USA.,Department of Medicine, Duke University Medical Center, Durham, NC 27708, USA.,National Oncology Program Office, Office of Specialty Care, Department of Veterans Affairs, Durham, NC 27705, USA
| | - Jason L Vassy
- VA Boston Healthcare System, Boston, MA 02130, USA.,Harvard Medical School, Boston, MA 02115, USA
| | - Peruvemba Sriram
- North Florida/South Georgia Veterans Health System, Gainesville, FL 32608, USA
| | - C William Heise
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA.,The University of Arizona College of Medicine - Phoenix, Phoenix, AZ 85004, USA
| | - Salvador Rivas
- Phoenix VA Health Care System, Phoenix, AZ 85012, USA.,The University of Arizona College of Medicine - Phoenix, Phoenix, AZ 85004, USA
| | - Maria Ribeiro
- Atlanta VA Medical Center, Atlanta, GA 30033, USA.,Department of Hematology & Medical Oncology, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Jennifer G Chapman
- Institute for Medical Research, Durham VA Medical Center, Durham, NC 27705, USA
| | - Deepak Voora
- Durham VA Health Care System, Durham, NC 27705, USA.,Department of Medicine, Duke Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA
| |
Collapse
|
31
|
Tuteja S, Salloum RG, Elchynski AL, Smith DM, Rowe E, Blake KV, Limdi NA, Aquilante CL, Bates J, Beitelshees AL, Cipriani A, Duong BQ, Empey PE, Formea CM, Hicks JK, Mroz P, Oslin D, Pasternak AL, Petry N, Ramsey LB, Schlichte A, Swain SM, Ward KM, Wiisanen K, Skaar TC, Van Driest SL, Cavallari LH, Bishop JR. Multisite evaluation of institutional processes and implementation determinants for pharmacogenetic testing to guide antidepressant therapy. Clin Transl Sci 2021; 15:371-383. [PMID: 34562070 PMCID: PMC8841452 DOI: 10.1111/cts.13154] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 08/11/2021] [Accepted: 08/16/2021] [Indexed: 12/11/2022] Open
Abstract
There is growing interest in utilizing pharmacogenetic (PGx) testing to guide antidepressant use, but there is lack of clarity on how to implement testing into clinical practice. We administered two surveys at 17 sites that had implemented or were in the process of implementing PGx testing for antidepressants. Survey 1 collected data on the process and logistics of testing. Survey 2 asked sites to rank the importance of Consolidated Framework for Implementation Research (CFIR) constructs using best‐worst scaling choice experiments. Of the 17 sites, 13 had implemented testing and four were in the planning stage. Thirteen offered testing in the outpatient setting, and nine in both outpatient/inpatient settings. PGx tests were mainly ordered by psychiatry (92%) and primary care (69%) providers. CYP2C19 and CYP2D6 were the most commonly tested genes. The justification for antidepressants selected for PGx guidance was based on Clinical Pharmacogenetics Implementation Consortium guidelines (94%) and US Food and Drug Administration (FDA; 75.6%) guidance. Both institutional (53%) and commercial laboratories (53%) were used for testing. Sites varied on the methods for returning results to providers and patients. Sites were consistent in ranking CFIR constructs and identified patient needs/resources, leadership engagement, intervention knowledge/beliefs, evidence strength and quality, and the identification of champions as most important for implementation. Sites deployed similar implementation strategies and measured similar outcomes. The process of implementing PGx testing to guide antidepressant therapy varied across sites, but key drivers for successful implementation were similar and may help guide other institutions interested in providing PGx‐guided pharmacotherapy for antidepressant management.
Collapse
Affiliation(s)
- Sony Tuteja
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA
| | - Ramzi G Salloum
- University of Florida College of Medicine, Gainesville, Florida, USA
| | | | - D Max Smith
- MedStar Health, Georgetown University Medical Center, Washington, DC, USA
| | - Elizabeth Rowe
- Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | - Nita A Limdi
- University of Alabama School of Medicine, Birmingham, Alabama, USA
| | | | - Jill Bates
- Durham VA Healthcare System, Durham, North Carolina, USA
| | | | - Amber Cipriani
- University of North Carolina Medical Center, Chapel Hill, North Carolina, USA
| | | | - Philip E Empey
- University of Pittsburgh School of Pharmacy, Pittsburgh, Pennsylvania, USA
| | | | | | - Pawel Mroz
- University of Minnesota Medical School, Minneapolis, Minnesota, USA
| | - David Oslin
- University of Pennsylvania Perelman School of Medicine, Philadelphia, Pennsylvania, USA.,Corporal Michael J. Crescenz VA Medical Center, Philadelphia, Pennsylvania, USA
| | - Amy L Pasternak
- University of Michigan College of Pharmacy, Ann Arbor, Michigan, USA
| | - Natasha Petry
- North Dakota State University/Sanford Health, Fargo, North Dakota, USA
| | - Laura B Ramsey
- Cincinnati Children's Hospital Medical Center, University of Cincinnati College of Medicine, Cincinnati, Ohio, USA
| | | | - Sandra M Swain
- MedStar Health, Georgetown University Medical Center, Washington, DC, USA
| | - Kristen M Ward
- University of Michigan College of Pharmacy, Ann Arbor, Michigan, USA
| | - Kristin Wiisanen
- University of Florida College of Pharmacy, Gainesville, Florida, USA
| | - Todd C Skaar
- Indiana University School of Medicine, Indianapolis, Indiana, USA
| | | | | | - Jeffrey R Bishop
- University of Minnesota Medical School, Minneapolis, Minnesota, USA.,University of Minnesota College of Pharmacy, Minneapolis, Minnesota, USA
| | | |
Collapse
|
32
|
Abstract
The reference human genome sequence is inarguably the most important and widely used resource in the fields of human genetics and genomics. It has transformed the conduct of biomedical sciences and brought invaluable benefits to the understanding and improvement of human health. However, the commonly used reference sequence has profound limitations, because across much of its span, it represents the sequence of just one human haplotype. This single, monoploid reference structure presents a critical barrier to representing the broad genomic diversity in the human population. In this review, we discuss the modernization of the reference human genome sequence to a more complete reference of human genomic diversity, known as a human pangenome.
Collapse
Affiliation(s)
- Karen H Miga
- UC Santa Cruz Genomics Institute and Department of Biomedical Engineering, University of California, Santa Cruz, California 95064, USA;
| | - Ting Wang
- Department of Genetics, Edison Family Center for Genome Sciences and Systems Biology, and McDonnell Genome Institute, Washington University School of Medicine, St. Louis, Missouri 63110, USA;
| |
Collapse
|
33
|
Linder JE, Bastarache L, Hughey JJ, Peterson JF. The Role of Electronic Health Records in Advancing Genomic Medicine. Annu Rev Genomics Hum Genet 2021; 22:219-238. [PMID: 34038146 PMCID: PMC9297710 DOI: 10.1146/annurev-genom-121120-125204] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Recent advances in genomic technology and widespread adoption of electronic health records (EHRs) have accelerated the development of genomic medicine, bringing promising research findings from genome science into clinical practice. Genomic and phenomic data, accrued across large populations through biobanks linked to EHRs, have enabled the study of genetic variation at a phenome-wide scale. Through new quantitative techniques, pleiotropy can be explored with phenome-wide association studies, the occurrence of common complex diseases can be predicted using the cumulative influence of many genetic variants (polygenic risk scores), and undiagnosed Mendelian syndromes can be identified using EHR-based phenotypic signatures (phenotype risk scores). In this review, we trace the role of EHRs from the development of genome-wide analytic techniques to translational efforts to test these new interventions to the clinic. Throughout, we describe the challenges that remain when combining EHRs with genetics to improve clinical care.
Collapse
Affiliation(s)
- Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA;
| | - Lisa Bastarache
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
| | - Jacob J Hughey
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
| | - Josh F Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA; , ,
- Department of Medicine, Vanderbilt University Medical Center, Nashville, Tennessee 37203, USA
| |
Collapse
|
34
|
Sperber NR, Dong OM, Roberts MC, Dexter P, Elsey AR, Ginsburg GS, Horowitz CR, Johnson JA, Levy KD, Ong H, Peterson JF, Pollin TI, Rakhra-Burris T, Ramos MA, Skaar T, Orlando LA. Strategies to Integrate Genomic Medicine into Clinical Care: Evidence from the IGNITE Network. J Pers Med 2021; 11:647. [PMID: 34357114 PMCID: PMC8306482 DOI: 10.3390/jpm11070647] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2021] [Revised: 06/29/2021] [Accepted: 07/02/2021] [Indexed: 11/16/2022] Open
Abstract
The complexity of genomic medicine can be streamlined by implementing some form of clinical decision support (CDS) to guide clinicians in how to use and interpret personalized data; however, it is not yet clear which strategies are best suited for this purpose. In this study, we used implementation science to identify common strategies for applying provider-based CDS interventions across six genomic medicine clinical research projects funded by an NIH consortium. Each project's strategies were elicited via a structured survey derived from a typology of implementation strategies, the Expert Recommendations for Implementing Change (ERIC), and follow-up interviews guided by both implementation strategy reporting criteria and a planning framework, RE-AIM, to obtain more detail about implementation strategies and desired outcomes. We found that, on average, the three pharmacogenomics implementation projects used more strategies than the disease-focused projects. Overall, projects had four implementation strategies in common; however, operationalization of each differed in accordance with each study's implementation outcomes. These four common strategies may be important for precision medicine program implementation, and pharmacogenomics may require more integration into clinical care. Understanding how and why these strategies were successfully employed could be useful for others implementing genomic or precision medicine programs in different contexts.
Collapse
Affiliation(s)
- Nina R. Sperber
- Duke Department of Population Health Sciences, Duke University School of Medicine, Durham, NC 27701, USA
- Durham VA Health Care System, Durham, NC 27705, USA
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA; (O.M.D.); (G.S.G.); (T.R.-B.); (L.A.O.)
| | - Olivia M. Dong
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA; (O.M.D.); (G.S.G.); (T.R.-B.); (L.A.O.)
| | - Megan C. Roberts
- Division of Pharmaceutical Outcomes and Policy, Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA;
| | - Paul Dexter
- Regenstrief Institute, Indianapolis, Indiana University School of Medicine and Clem McDonald Center for Biomedical Informatics, Indianapolis, IN 46202, USA;
| | - Amanda R. Elsey
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville, FL 32610, USA; (A.R.E.); (J.A.J.)
| | - Geoffrey S. Ginsburg
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA; (O.M.D.); (G.S.G.); (T.R.-B.); (L.A.O.)
| | - Carol R. Horowitz
- Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Julie A. Johnson
- Center for Pharmacogenomics and Precision Medicine, Department of Pharmacotherapy and Translational Research, University of Florida, Gainesville, FL 32610, USA; (A.R.E.); (J.A.J.)
| | - Kenneth D. Levy
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street, Indianapolis, IN 46202, USA; (K.D.L.); (T.S.)
| | - Henry Ong
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (H.O.); (J.F.P.)
| | - Josh F. Peterson
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37232, USA; (H.O.); (J.F.P.)
| | - Toni I. Pollin
- Division of Endocrinology, Diabetes and Nutrition, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA;
| | - Tejinder Rakhra-Burris
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA; (O.M.D.); (G.S.G.); (T.R.-B.); (L.A.O.)
| | - Michelle A. Ramos
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA;
| | - Todd Skaar
- Division of Clinical Pharmacology, Department of Medicine, Indiana University School of Medicine, 950 W. Walnut Street, Indianapolis, IN 46202, USA; (K.D.L.); (T.S.)
| | - Lori A. Orlando
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, Durham, NC 27708, USA; (O.M.D.); (G.S.G.); (T.R.-B.); (L.A.O.)
| |
Collapse
|
35
|
Gardner B, Doose M, Sanchez JI, Freedman AN, de Moor JS. Distribution of Genomic Testing Resources by Oncology Practice and Rurality: A Nationally Representative Study. JCO Precis Oncol 2021; 5:PO.21.00109. [PMID: 34568717 PMCID: PMC8457818 DOI: 10.1200/po.21.00109] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 05/04/2021] [Accepted: 05/11/2021] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Oncologists are increasingly using molecular profiling to inform personalized patient treatment decisions. Despite its promising utility, the integration of genomic testing into diverse clinical health care settings across geographic settings has been understudied. METHODS We used data from the National Survey of Precision Medicine in Cancer Treatment, a nationally representative sample of practicing US oncologists, to assess the availability of six genomic testing resources, including on-site pathology, contracts with outside laboratories, on-site genetic counselors, internal policies or protocols for using genomic and biomarker testing, electronic medical record alerts, and genomic or molecular tumor boards. We used multivariate logistic regression models to examine differences in the availability of each genomic testing resource by practice type and rurality while adjusting for payer mix and patient volume. RESULTS A larger proportion of multispecialty group and academic practices had genomic testing resources available compared with solo and nonacademic practices. Electronic medical record alerts were the least available resource, whereas contracts with outside laboratories were the most available resource. Compared with urban practices, there were significantly fewer practices located in rural areas that had on-site pathology, on-site genetic counselors, protocols for genomic tests, and molecular tumor boards. CONCLUSION Genomic testing resources varied by practice type and geography among a nationally representative sample of practicing oncologists. This variation has important implications for the development of interventions and policies to support the more equitable delivery of precision oncology to patients with cancer.
Collapse
Affiliation(s)
- Brittany Gardner
- Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Michelle Doose
- Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Janeth I. Sanchez
- Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Andrew N. Freedman
- Epidemiology and Genomics Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| | - Janet S. de Moor
- Healthcare Delivery Research Program, Division of Cancer Control and Population Sciences, National Cancer Institute, Rockville, MD
| |
Collapse
|
36
|
Subasri M, Barrett D, Sibalija J, Bitacola L, Kim RB. Pharmacogenomic-based personalized medicine: Multistakeholder perspectives on implementational drivers and barriers in the Canadian healthcare system. Clin Transl Sci 2021; 14:2231-2241. [PMID: 34080317 PMCID: PMC8604218 DOI: 10.1111/cts.13083] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2021] [Revised: 04/19/2021] [Accepted: 05/02/2021] [Indexed: 01/04/2023] Open
Abstract
Pharmacogenomics (PGx)-based personalized medicine (PM) is increasingly utilized to guide treatment decisions for many drug-disease combinations. Notably, London Health Sciences Centre (LHSC) has pioneered a PGx program that has become a staple for London-based specialists. Although implementational studies have been conducted in other jurisdictions, the Canadian healthcare system is understudied. Herein, the multistakeholder perspectives on implementational drivers and barriers are elucidated. Using a mixed-method qualitative model, key stakeholders, and patients from LHSC's PGx-based PM clinic were interviewed and surveyed, respectively. Interview transcripts were thematically analyzed in a stepwise process of customer profiling, value mapping, and business model canvasing. Value for LHSC located specialist users of PGx was driven by the quick turnaround time, independence of the PGx clinic, and the quality of information. Engagement of external specialists was only limited by access and awareness, whereas other healthcare nonusers were limited by education and applicability. The major determinant of successful adoption at novel sites were institutional champions. Patients valued and approved of the service, expressed a general willingness to pay, but often traveled far to receive genotyping. This paper discusses the critical pillars of education, awareness, advocacy, and efficiency required to address implementation barriers to healthcare service innovation in Canada. Further adoption of PGx practices into Canadian hospitals is an important factor for advancing system-level changes in care delivery, patient experiences, and outcomes. The findings in this paper can help inform efforts to advance clinical PGx practices, but also the potential adoption and implementation of other innovative healthcare service solutions.
Collapse
Affiliation(s)
- Mathushan Subasri
- Ivey Business School, University of Western Ontario, London, Ontario, Canada.,London Health Sciences Centre, London, Ontario, Canada
| | - David Barrett
- Ivey Business School, University of Western Ontario, London, Ontario, Canada
| | - Jovana Sibalija
- Ivey Business School, University of Western Ontario, London, Ontario, Canada.,Faculty of Social Science, University of Western Ontario, London, Ontario, Canada
| | | | - Richard B Kim
- Ivey Business School, University of Western Ontario, London, Ontario, Canada.,London Health Sciences Centre, London, Ontario, Canada
| |
Collapse
|
37
|
Bourdon JL, Dorsey A, Zalik M, Pietka A, Salyer P, Bray MJ, Bierut LJ, Ramsey AT. In-vivo design feedback and perceived utility of a genetically-informed smoking risk tool among current smokers in the community. BMC Med Genomics 2021; 14:139. [PMID: 34039360 PMCID: PMC8152342 DOI: 10.1186/s12920-021-00976-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2020] [Accepted: 05/05/2021] [Indexed: 12/03/2022] Open
Abstract
Background The use of genetically-informed personalized risk information for behavioral disorders, namely smoking and smoking-related behaviors, is a promising yet understudied area. The Genetics and Smoking Risk Profile, or RiskProfile, leverages genetic and environmental information to communicate one’s risk for smoking-related diseases. Although prior studies have examined attitudes toward genetic results, little research has investigated these perceptions through a lens of in-vivo testing; that is, user-centered design feedback in response to personalized genetic results being returned contemporaneously. This qualitative study engaged current smokers in usability testing of the RiskProfile within the context of concurrently receiving this personalized, genetically-informed smoking cessation intervention. Methods Eighty-nine participants who were current smokers responded to open-ended interview questions on perceptions of smoking-related genetic information and the content and format of the RiskProfile intervention that they had received moments before. Data were analyzed via the conventional content analysis approach in which themes were allowed to emerge throughout the analysis. Results Participants were able to reference and offer design input on specific elements of the RiskProfile. Overall, current smokers perceived the RiskProfile to have high potential utility. Constructive feedback that current smokers offered about the tool centered around suggested improvements to optimize its usability and technical content. Conclusions The detailed and constructive feedback from participants highlights that in-vivo feedback offers a useful design approach that addresses concerns of rigor and relevance when returning genetic results. This unique method demonstrated perceived utility and constructive design feedback for the RiskProfile among current smokers and can play an important role in optimizing the design and implementation of personalized genetic risk interventions moving forward. Supplementary Information The online version contains supplementary material available at 10.1186/s12920-021-00976-1.
Collapse
Affiliation(s)
- Jessica L Bourdon
- Wellbridge Center for Addiction Treatment and Research, Center for Addiction Science, 525 Jan Way, Room 1523, Calverton, NY, 11922, USA.
| | - Amelia Dorsey
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Maia Zalik
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Amanda Pietka
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Patricia Salyer
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Michael J Bray
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Laura J Bierut
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| | - Alex T Ramsey
- Department of Psychiatry, Washington University in St. Louis, St. Louis, MO, USA
| |
Collapse
|
38
|
Hicks JK, Howard R, Reisman P, Adashek JJ, Fields KK, Gray JE, McIver B, McKee K, O'Leary MF, Perkins RM, Robinson E, Tandon A, Teer JK, Markowitz J, Rollison DE. Integrating Somatic and Germline Next-Generation Sequencing Into Routine Clinical Oncology Practice. JCO Precis Oncol 2021; 5:PO.20.00513. [PMID: 34095711 PMCID: PMC8169076 DOI: 10.1200/po.20.00513] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2020] [Revised: 02/14/2021] [Accepted: 04/20/2021] [Indexed: 12/27/2022] Open
Abstract
Next-generation sequencing (NGS) is rapidly expanding into routine oncology practice. Genetic variations in both the cancer and inherited genomes are informative for hereditary cancer risk, prognosis, and treatment strategies. Herein, we focus on the clinical perspective of integrating NGS results into patient care to assist with therapeutic decision making. Five key considerations are addressed for operationalization of NGS testing and application of results to patient care as follows: (1) NGS test ordering and workflow design; (2) result reporting, curation, and storage; (3) clinical consultation services that provide test interpretations and identify opportunities for molecularly guided therapy; (4) presentation of genetic information within the electronic health record; and (5) education of providers and patients. Several of these key considerations center on informatics tools that support NGS test ordering and referencing back to the results for therapeutic purposes. Clinical decision support tools embedded within the electronic health record can assist with NGS test utilization and identifying opportunities for targeted therapy including clinical trial eligibility. Challenges for project and change management in operationalizing NGS-supported, evidence-based patient care in the context of current information technology systems with appropriate clinical data standards are discussed, and solutions for overcoming barriers are provided.
Collapse
Affiliation(s)
- J. Kevin Hicks
- Department of Individualized Cancer Management, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
| | - Rachel Howard
- Department of Health Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Phillip Reisman
- Department of Health Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jacob J. Adashek
- Department of Internal Medicine, University of South Florida, Tampa, FL
| | - Karen K. Fields
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Clinical Pathways, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Jhanelle E. Gray
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Thoracic Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Bryan McIver
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Head and Neck-Endocrine Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Kelly McKee
- Department of Clinical Pathways, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Mandy F. O'Leary
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Pathology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Randa M. Perkins
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Clinical Informatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Edmondo Robinson
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Internal Medicine, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Ankita Tandon
- Department of Internal Medicine, University of South Florida, Tampa, FL
| | - Jamie K. Teer
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Biostatistics and Bioinformatics, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Joseph Markowitz
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Cutaneous Oncology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| | - Dana E. Rollison
- Department of Oncologic Sciences, University of South Florida, Tampa, FL
- Department of Epidemiology, H. Lee Moffitt Cancer Center and Research Institute, Tampa, FL
| |
Collapse
|
39
|
Allen CG, Peterson S, Khoury MJ, Brody LC, McBride CM. A scoping review of social and behavioral science research to translate genomic discoveries into population health impact. Transl Behav Med 2021; 11:901-911. [PMID: 32902617 PMCID: PMC8240657 DOI: 10.1093/tbm/ibaa076] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
Abstract
Since the completion of the Human Genome Project, progress toward translating genomic research discoveries to address population health issues has been limited. Several meetings of social and behavioral scientists have outlined priority research areas where advancement of translational research could increase population health benefits of genomic discoveries. In this review, we track the pace of progress, study size and design, and focus of genomics translational research from 2012 to 2018 and its concordance with five social and behavioral science recommended priorities. We conducted a review of the literature following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Guidelines for Scoping Reviews. Steps involved completing a search in five databases and a hand search of bibliographies of relevant literature. Our search (from 2012 to 2018) yielded 4,538 unique studies; 117 were included in the final analyses. Two coders extracted data including items from the PICOTS framework. Analysis included descriptive statistics to help identify trends in pace, study size and design, and translational priority area. Among the 117 studies included in our final sample, nearly half focused on genomics applications that have evidence to support translation or implementation into practice (Centers for Disease Control and Prevention Tier 1 applications). Common study designs were cross-sectional (40.2%) and qualitative (24.8%), with average sample sizes of 716 across all studies. Most often, studies addressed public understanding of genetics and genomics (33.3%), risk communication (29.1%), and intervention development and testing of interventions to promote behavior change (19.7%). The number of studies that address social and behavioral science priority areas is extremely limited and the pace of this research continues to lag behind basic science advances. Much of the research identified in this review is descriptive and related to public understanding, risk communication, and intervention development and testing of interventions to promote behavior change. The field has been slow to develop and evaluate public health-friendly interventions and test implementation approaches that could enable health benefits and equitable access to genomic discoveries. As the completion of the human genome approaches its 20th anniversary, full engagement of transdisciplinary efforts to address translation challenges will be required to close this gap.
Collapse
Affiliation(s)
- Caitlin G Allen
- Behavioral, Social and Health Education Sciences Department, Emory University, Atlanta, GA, USA
| | - Shenita Peterson
- Woodruff Health Science Center Library, Emory University, Atlanta, GA, USA
| | - Muin J Khoury
- Office of Genomics and Precision Public Health, Office of Science, Centers for Disease Control and Prevention, Atlanta, GA, USA
| | - Lawrence C Brody
- Gene and Environment Interaction Section, National Human Genome Research Institute, Bethesda, MD, USA
| | - Colleen M McBride
- Behavioral, Social and Health Education Sciences Department, Emory University, Atlanta, GA, USA
| |
Collapse
|
40
|
Miller DM, Gaviglio A, Zierhut HA. Development of an Implementation Framework for Overcoming Underdiagnoses of Familial Hypercholesterolemia in the USA. Public Health Genomics 2021; 24:110-122. [PMID: 33853081 DOI: 10.1159/000513872] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2020] [Accepted: 12/16/2020] [Indexed: 11/19/2022] Open
Abstract
Familial hypercholesterolemia (FH) is a genetic condition which causes elevated low-density lipoprotein cholesterol from birth. With a prevalence of 1 in 250 and the availability of effective treatments, the diagnostic rate of <1 to 10% is unacceptably low. Screening for FH is supported by multiple organizations, but it has not been broadly adopted and implemented across the USA. To investigate the implementation of FH screening, key informants were recruited from across the USA for their expertise in FH-related literature, guidelines, public health, and/or advocacy to complete -semistructured interviews guided by implementation science (RE-AIM framework). Sixteen semistructured interviews were analyzed with directed content and thematic analyses, yielding specific barriers and recommendations to improve FH screening. Barriers to FH screening included patient recruitment and participation, equitable access to healthcare, provider discomfort with screening and treating FH, provider burden, lack of public health and legislative support, FH awareness, guideline complexity, facilitation of genetic testing and cascade screening, and lack of coordination between stakeholders. Awareness, engagement, communication, and collaboration between stakeholders is integral to successful FH screening. Individualized plans will be required at national, regional, and institutional levels. FH screening implementation can be achieved through practice facilitation, streamlined screening approaches, electric medical record tools, and consensus guidelines to increase screening adoption and consistent delivery. Reliable funding and established lines of communication between stakeholders can maintain efforts as FH screening progresses.
Collapse
Affiliation(s)
- Dana M Miller
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, USA
| | - Amy Gaviglio
- G2S Corporation/CDC Newborn Screening and Molecular Biology Branch, Atlanta, Georgia, USA
| | - Heather A Zierhut
- Department of Genetics, Cell Biology, and Development, University of Minnesota, Minneapolis, Minnesota, USA
| |
Collapse
|
41
|
Best S, Long JC, Gaff C, Braithwaite J, Taylor N. Investigating the Adoption of Clinical Genomics in Australia. An Implementation Science Case Study. Genes (Basel) 2021; 12:genes12020317. [PMID: 33672413 PMCID: PMC7926693 DOI: 10.3390/genes12020317] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2021] [Revised: 02/10/2021] [Accepted: 02/19/2021] [Indexed: 12/02/2022] Open
Abstract
Despite the overwhelming interest in clinical genomics, uptake has been slow. Implementation science offers a systematic approach to reveal pathways to adoption and a theory informed approach to addressing barriers presented. Using case study methodology, we undertook 16 in-depth interviews with nongenetic medical specialists to identify barriers and enablers to the uptake of clinical genomics. Data collection and analysis was guided by two evidence-based behaviour change models: the Theoretical Domains Framework (TDF), and the Capability, Opportunity Motivation Behaviour model (COM-B). Our findings revealed the use of implementation science not only provided a theoretical structure to frame the study but also facilitated uncovering of traditionally difficult to access responses from participants, e.g., “safety in feeling vulnerable” (TDF code emotion/COM-B code motivation). The most challenging phase for participants was ensuring appropriate patients were offered genomic testing. There were several consistent TDF codes: professional identity, social influences, and environmental context and resources and COM-B codes opportunity and motivation, with others varying along the patient journey. We conclude that implementation science methods can maximise the value created by the exploration of factors affecting the uptake of clinical genomics to ensure future interventions are designed to meet the needs of novice nongenetic medical specialists.
Collapse
Affiliation(s)
- Stephanie Best
- Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia; (J.C.L.); (J.B.)
- Australian Genomics Health Alliance, Murdoch Childrens Research Institute, Melbourne, VIC 3052, Australia
- Correspondence: ; Tel.: +61-468384565
| | - Janet C. Long
- Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia; (J.C.L.); (J.B.)
| | - Clara Gaff
- Melbourne Genomics Health Alliance, Walter and Eliza Hall Institute, Melbourne, VIC 3052, Australia;
- Department of Paediatrics, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Jeffrey Braithwaite
- Australian Institute of Health Innovation, Macquarie University, Sydney, NSW 2113, Australia; (J.C.L.); (J.B.)
| | - Natalie Taylor
- Cancer Research Division, Cancer Council New South Wales, Sydney, NSW 2011, Australia;
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW 2050, Australia
| |
Collapse
|
42
|
Learning from scaling up ultra-rapid genomic testing for critically ill children to a national level. NPJ Genom Med 2021; 6:5. [PMID: 33510162 PMCID: PMC7843635 DOI: 10.1038/s41525-020-00168-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Accepted: 12/15/2020] [Indexed: 12/25/2022] Open
Abstract
In scaling up an ultra-rapid genomics program, we used implementation science principles to design and investigate influences on implementation and identify strategies required for sustainable “real-world” services. Interviews with key professionals revealed the importance of networks and relationship building, leadership, culture, and the relative advantage afforded by ultra-rapid genomics in the care of critically ill children. Although clinical geneticists focused on intervention characteristics and the fit with patient-centered care, intensivists emphasized the importance of access to knowledge, in particular from clinical geneticists. The relative advantage of ultra-rapid genomics and trust in consistent and transparent delivery were significant in creating engagement at initial implementation, with appropriate resourcing highlighted as important for longer term sustainability of implementation. Our findings demonstrate where common approaches can be used and, significantly, where there is a need to tailor support by professional role and implementation phase, to maximize the potential of ultra-rapid genomic testing to improve patient care.
Collapse
|
43
|
A Network-Based Mixed Methods Approach to Analyze Current Perspectives on Personalized Oncological Medicine in Austria. J Pers Med 2020; 10:jpm10040276. [PMID: 33322735 PMCID: PMC7768434 DOI: 10.3390/jpm10040276] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2020] [Revised: 12/08/2020] [Accepted: 12/10/2020] [Indexed: 12/15/2022] Open
Abstract
Personalized medicine (PM) to tailor healthcare (HC) to the individual, is a promising but challenging concept. So far, no study exists investigating stakeholders’ perspectives on PM in oncology in Austria potentially hindering implementation, which was the aim of this study. We performed semi-structured interviews among experts (n = 14) and cancer patients (n = 2) of the Vienna General Hospital and the Medical University of Vienna and analyzed them by a mixed methods network theoretical approach. Study results show a great variety of topics addressed by the interviewees. Clear differences in the topic selection between patients and experts could be observed. Patient-doctor relationship was the most prominent theme among experts, whereas HC systems and public health in PM was more relevant for the patients. Although promising new molecular pathology methods were explicitly mentioned, the experts believed that their practical implementation and the implementation of PM in standard care will take a long time in Austria. A variety of concerns regarding PM were mentioned by the experts, including communication issues and knowledge gaps. Besides important insights into the current situation of PM in Austria, the study has shown that network theory is a powerful tool for analyzing qualitative interview data.
Collapse
|
44
|
Snir M, Nazareth S, Simmons E, Hayward L, Ashcraft K, Bristow SL, Esplin ED, Aradhya S. Democratizing genomics: Leveraging software to make genetics an integral part of routine care. AMERICAN JOURNAL OF MEDICAL GENETICS. PART C, SEMINARS IN MEDICAL GENETICS 2020; 187:14-27. [PMID: 33296144 DOI: 10.1002/ajmg.c.31866] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 11/16/2020] [Accepted: 11/17/2020] [Indexed: 12/25/2022]
Abstract
Genetic testing can provide definitive molecular diagnoses and guide clinical management decisions from preconception through adulthood. Innovative solutions for scaling clinical genomics services are necessary if they are to transition from a niche specialty to a routine part of patient care. The expertise of specialists, like genetic counselors and medical geneticists, has traditionally been relied upon to facilitate testing and follow-up, and while ideal, this approach is limited in its ability to integrate genetics into primary care. As individuals, payors, and providers increasingly realize the value of genetics in mainstream medicine, several implementation challenges need to be overcome. These include electronic health record integration, patient and provider education, tools to stay abreast of guidelines, and simplification of the test ordering process. Currently, no single platform offers a holistic view of genetic testing that streamlines the entire process across specialties that begins with identifying at-risk patients in mainstream care settings, providing pretest education, facilitating consent and test ordering, and following up as a "genetic companion" for ongoing management. We describe our vision for using software that includes clinical-grade chatbots and decision support tools, with direct access to genetic counselors and pharmacists within a modular, integrated, end-to-end testing journey.
Collapse
|
45
|
Bourdon JL, Davies RA, Long EC. Four Actionable Bottlenecks and Potential Solutions to Translating Psychiatric Genetics Research: An Expert Review. Public Health Genomics 2020; 23:171-183. [PMID: 33147585 PMCID: PMC7854816 DOI: 10.1159/000510832] [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: 03/23/2020] [Accepted: 07/27/2020] [Indexed: 11/19/2022] Open
Abstract
BACKGROUND Psychiatric genetics has had limited success in translational efforts. A thorough understanding of the present state of translation in this field will be useful in the facilitation and assessment of future translational progress. PURPOSE A narrative literature review was conducted. Combinations of 3 groups of terms were searched in EBSCOhost, Google Scholar, and PubMed. The review occurred in multiple steps, including abstract collection, inclusion/exclusion criteria review, coding, and analysis of included papers. RESULTS One hundred and fourteen articles were analyzed for the narrative review. Across those, 4 bottlenecks were noted that, if addressed, may provide insights and help improve and increase translation in the field of psychiatric genetics. These 4 bottlenecks are emphasizing linear translational frameworks, relying on molecular genomic findings, prioritizing certain psychiatric disorders, and publishing more reviews than experiments. CONCLUSIONS These entwined bottlenecks are examined with one another. Awareness of these bottlenecks can inform stakeholders who work to translate and/or utilize psychiatric genetic information. Potential solutions include utilizing nonlinear translational frameworks as well as a wider array of psychiatric genetic information (e.g., family history and gene-environment interplay) in this area of research, expanding which psychiatric disorders are considered for translation, and when possible, conducting original research. Researchers are urged to consider how their research is translational in the context of the frameworks, genetic information, and psychiatric disorders discussed in this review. At a broader level, these efforts should be supported with translational efforts in funding and policy shifts.
Collapse
Affiliation(s)
- Jessica L Bourdon
- Department of Psychiatry, Brown School of Social Work, Washington University in St. Louis, St. Louis, Missouri, USA,
| | - Rachel A Davies
- Yerkes National Primate Research Center, Division of Behavioral Neuroscience and Psychiatric Disorders, Emory University, Atlanta, Georgia, USA
| | - Elizabeth C Long
- Edna Bennett Pierce Prevention Research Center, Pennsylvania State University, University Park, Pennsylvania, USA
| |
Collapse
|
46
|
Shugg T, Pasternak AL, London B, Luzum JA. Prevalence and types of inconsistencies in clinical pharmacogenetic recommendations among major U.S. sources. NPJ Genom Med 2020; 5:48. [PMID: 33145028 PMCID: PMC7603298 DOI: 10.1038/s41525-020-00156-7] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2020] [Accepted: 10/05/2020] [Indexed: 12/30/2022] Open
Abstract
Clinical implementation of pharmacogenomics (PGx) is slow. Previous studies have identified some inconsistencies among clinical PGx recommendations, but the prevalence and types of inconsistencies have not been comprehensively analyzed among major PGx guidance sources in the U.S. PGx recommendations from the Clinical Pharmacogenetics Implementation Consortium, U.S. Food and Drug Administration drug labels, and major U.S. professional medical organizations were analyzed through May 24, 2019. Inconsistencies were analyzed within the following elements: recommendation category; whether routine screening was recommended; and the specific biomarkers, variants, and patient groups involved. We identified 606 total clinical PGx recommendations, which contained 267 unique drugs. Composite inconsistencies occurred in 48.1% of clinical PGx recommendations overall, and in 93.3% of recommendations from three sources. Inconsistencies occurred in the recommendation category (29.8%), the patient group (35.4%), and routine screening (15.2%). In conclusion, almost one-half of clinical PGx recommendations from prominent U.S. guidance sources contain inconsistencies, which can potentially slow clinical implementation.
Collapse
Affiliation(s)
- Tyler Shugg
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA.,Division of Clinical Pharmacology, Indiana University School of Medicine, Indianapolis, IN USA
| | - Amy L Pasternak
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA
| | - Bianca London
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA.,Senior Health Services at Blue Cross Blue Shield of Michigan Emerging Markets, Southfield, MI USA
| | - Jasmine A Luzum
- Department of Clinical Pharmacy, University of Michigan College of Pharmacy, Ann Arbor, MI USA
| |
Collapse
|
47
|
Abstract
The field of pharmacogenetic testing was hailed as one of the early successful clinical applications arising from the personalized (or precision) medicine revolution. Substantial progress has been made to identify genes and genetic variants involved in drug response and establish clinical implementation programs. Yet, drug response is a complex trait and recent work has highlighted the key role played by the gut microbiome. As the study of the gut microbiome and pharmacogenetics converge, it may be possible to generate more precise predictions of drug response and improve health outcomes to treatments. Substantial effort will be needed to understand the dynamic impact of the microbiome and the interplay with host genetics and how to implement expanded pharmacogenetic testing.
Collapse
Affiliation(s)
- Susanne B Haga
- Center for Applied Genomics & Precision Medicine, Duke University School of Medicine, 101 Science Drive, Box 3382, Durham, NC 27708, USA
| |
Collapse
|
48
|
Ball J, Thompson J, Wulff-Burchfield E, Ellerbeck E, Kimminau K, Brooks JV, Petersen S, Rotich D, Kinney AY, Ellis SD. Precision community: a mixed methods study to identify determinants of adoption and implementation of targeted cancer therapy in community oncology. Implement Sci Commun 2020. [DOI: 10.1186/s43058-020-00064-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
Abstract
Background
Precision medicine has enormous potential to improve cancer outcomes. Over one third of the 1.5 million Americans diagnosed with cancer each year have genetic mutations that could be targeted with an FDA-approved drug to treat their disease more effectively. However, the current uptake of targeted cancer therapy in clinical practice is suboptimal. Tumor testing is not widely used, and treatments based on molecular and genomic profiling are often not prescribed when indicated. Challenges with the uptake of precision medicine may disproportionately impact cancer patients in rural communities and other underserved populations. The objective of this study is to identify the determinants of adoption and implementation of precision cancer therapy to design an implementation strategy for community oncology practices, including those in rural areas.
Methods
This study is an explanatory sequential mixed methods study to identify factors associated with the use of targeted cancer therapy. Levels of targeted therapy use will be ascertained by secondary analysis of medical records to identify concordance with 18 national guideline recommendations for use of precision medicine in the treatment of breast, colorectal, lung, and melanoma skin cancer. Concurrently, facilitators and barriers associated with the use of precision cancer therapy will be elicited from interviews with up to a total of 40 oncologists, administrators, pathology, and pharmacy staff across the participating sites. Qualitative analysis will be a template analysis based on the Theoretical Domains Framework. Quantitative data aggregated at the practice level will be used to rank oncology practices’ adherence to targeted cancer therapy guidelines. Determinants will be compared among high and low users to isolate factors likely to facilitate targeted therapy use. The study will be conducted in eight community oncology practices, with an estimated 4121 targeted therapy treatment decision-making opportunities over a 3-year period.
Discussion
Despite unprecedented investment in precision medicine, translation into practice is suboptimal. Our study will identify factors associated with the uptake of precision medicine in community settings. These findings will inform future interventions to increase equitable uptake of evidence-based targeted cancer treatment.
Collapse
|
49
|
Optimising Seniors' Metabolism of Medications and Avoiding Adverse Drug Events Using Data on How Metabolism by Their P450 Enzymes Varies with Ancestry and Drug-Drug and Drug-Drug-Gene Interactions. J Pers Med 2020; 10:jpm10030084. [PMID: 32796505 PMCID: PMC7563167 DOI: 10.3390/jpm10030084] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2020] [Revised: 08/01/2020] [Accepted: 08/06/2020] [Indexed: 12/16/2022] Open
Abstract
Many individuals ≥65 have multiple illnesses and polypharmacy. Primary care physicians prescribe >70% of their medications and renew specialists’ prescriptions. Seventy-five percent of all medications are metabolised by P450 cytochrome enzymes. This article provides unique detailed tables how to avoid adverse drug events and optimise prescribing based on two key databases. DrugBank is a detailed database of 13,000 medications and both the P450 and other complex pathways that metabolise them. The Flockhart Tables are detailed lists of the P450 enzymes and also include all the medications which inhibit or induce metabolism by P450 cytochrome enzymes, which can result in undertreatment, overtreatment, or potentially toxic levels. Humans have used medications for a few decades and these enzymes have not been subject to evolutionary pressure. Thus, there is enormous variation in enzymatic functioning and by ancestry. Differences for ancestry groups in genetic metabolism based on a worldwide meta-analysis are discussed and this article provides advice how to prescribe for individuals of different ancestry. Prescribing advice from two key organisations, the Dutch Pharmacogenetics Working Group and the Clinical Pharmacogenetics Implementation Consortium is summarised. Currently, detailed pharmacogenomic advice is only available in some specialist clinics in major hospitals. However, this article provides detailed pharmacogenomic advice for primary care and other physicians and also physicians working in rural and remote areas worldwide. Physicians could quickly search the tables for the medications they intend to prescribe.
Collapse
|
50
|
Arwood MJ, Dietrich EA, Duong BQ, Smith DM, Cook K, Elchynski A, Rosenberg EI, Huber KN, Nagoshi YL, Wright A, Budd JT, Holland NP, Maska E, Panna D, Elsey AR, Cavallari LH, Wiisanen K, Johnson JA, Gums JG. Design and Early Implementation Successes and Challenges of a Pharmacogenetics Consult Clinic. J Clin Med 2020; 9:E2274. [PMID: 32708920 PMCID: PMC7408871 DOI: 10.3390/jcm9072274] [Citation(s) in RCA: 23] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Revised: 07/13/2020] [Accepted: 07/15/2020] [Indexed: 12/13/2022] Open
Abstract
Pharmacogenetic testing (PGT) is increasingly being used as a tool to guide clinical decisions. This article describes the development of an outpatient, pharmacist-led, pharmacogenetics consult clinic within internal medicine, its workflow, and early results, along with successes and challenges. A pharmacogenetics-trained pharmacist encouraged primary care physicians (PCPs) to refer patients who were experiencing side effects/ineffectiveness from certain antidepressants, opioids, and/or proton pump inhibitors. In clinic, the pharmacist confirmed the need for and ordered CYP2C19 and/or CYP2D6 testing, provided evidence-based pharmacogenetic recommendations to PCPs, and educated PCPs and patients on the results. Operational and clinical metrics were analyzed. In two years, 91 referred patients were seen in clinic (mean age 57, 67% women, 91% European-American). Of patients who received PGT, 77% had at least one CYP2C19 and/or CYP2D6 phenotype that would make conventional prescribing unfavorable. Recommendations suggested that physicians change a medication/dose for 59% of patients; excluding two patients lost to follow-up, 87% of recommendations were accepted. Challenges included PGT reimbursement and referral maintenance. High frequency of actionable results suggests physician education on who to refer was successful and illustrates the potential to reduce trial-and-error prescribing. High recommendation acceptance rate demonstrates the pharmacist's effectiveness in providing genotype-guided recommendations, emphasizing a successful pharmacist-physician collaboration.
Collapse
Affiliation(s)
- Meghan J. Arwood
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Eric A. Dietrich
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
| | - Benjamin Q. Duong
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - D. Max Smith
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Kelsey Cook
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Amanda Elchynski
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Eric I. Rosenberg
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Katherine N. Huber
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Ying L. Nagoshi
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Ashleigh Wright
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Jeffrey T. Budd
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Neal P. Holland
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Edlira Maska
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Danielle Panna
- Division of General Internal Medicine, College of Medicine, University of Florida, 1329 SW 16th St, Gainesville, FL 32608, USA; (E.I.R.); (K.N.H.); (Y.L.N.); (A.W.); (J.T.B.); (N.P.H.); (E.M.); (D.P.)
| | - Amanda R. Elsey
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Clinical and Translational Science Institute, University of Florida, 2004 Mowry Rd, Gainesville, FL 32610, USA
| | - Larisa H. Cavallari
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Kristin Wiisanen
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - Julie A. Johnson
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
- Center for Pharmacogenomics and Precision Medicine, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA
| | - John G. Gums
- Department of Pharmacotherapy and Translational Research, College of Pharmacy, University of Florida, 1345 Center Dr, Gainesville, FL 32603, USA; (E.A.D.); (B.Q.D.); (D.M.S.); (K.C.); (A.E.); (A.R.E.); (L.H.C.); (K.W.); (J.A.J.); (J.G.G.)
| |
Collapse
|