1
|
Cataldi M, Celentano C, Bencivenga L, Arcopinto M, Resnati C, Manes A, Dodani L, Comnes L, Vander Stichele R, Kalra D, Rengo G, Giallauria F, Trama U, Ferrara N, Cittadini A, Taglialatela M. Identification of Drugs Acting as Perpetrators in Common Drug Interactions in a Cohort of Geriatric Patients from Southern Italy and Analysis of the Gene Polymorphisms That Affect Their Interacting Potential. Geriatrics (Basel) 2023; 8:84. [PMID: 37736884 PMCID: PMC10514861 DOI: 10.3390/geriatrics8050084] [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: 07/04/2023] [Revised: 08/19/2023] [Accepted: 08/22/2023] [Indexed: 09/23/2023] Open
Abstract
BACKGROUND Pharmacogenomic factors affect the susceptibility to drug-drug interactions (DDI). We identified drug interaction perpetrators among the drugs prescribed to a cohort of 290 older adults and analysed the prevalence of gene polymorphisms that can increase their interacting potential. We also pinpointed clinical decision support systems (CDSSs) that incorporate pharmacogenomic factors in DDI risk evaluation. METHODS Perpetrator drugs were identified using the Drug Interactions Flockhart Table, the DRUGBANK website, and the Mayo Clinic Pharmacogenomics Association Table. Allelic variants affecting their activity were identified with the PharmVar, PharmGKB, dbSNP, ensembl and 1000 genome databases. RESULTS Amiodarone, amlodipine, atorvastatin, digoxin, esomperazole, omeprazole, pantoprazole, simvastatin and rosuvastatin were perpetrator drugs prescribed to >5% of our patients. Few allelic variants affecting their perpetrator activity showed a prevalence >2% in the European population: CYP3A4/5*22, *1G, *3, CYP2C9*2 and *3, CYP2C19*17 and *2, CYP2D6*4, *41, *5, *10 and *9 and SLC1B1*15 and *5. Few commercial CDSS include pharmacogenomic factors in DDI-risk evaluation and none of them was designed for use in older adults. CONCLUSIONS We provided a list of the allelic variants influencing the activity of drug perpetrators in older adults which should be included in pharmacogenomics-oriented CDSSs to be used in geriatric medicine.
Collapse
Affiliation(s)
- Mauro Cataldi
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Camilla Celentano
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Leonardo Bencivenga
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Gérontopôle de Toulouse, Institut du Vieillissement, CHU de Toulouse, Cité de la Santé, Place Lange, 31300 Toulouse, France
| | - Michele Arcopinto
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Chiara Resnati
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Annalaura Manes
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Loreta Dodani
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| | - Lucia Comnes
- Datawizard, Via Salaria 719a, 00138 Rome, Italy;
| | - Robert Vander Stichele
- Heymans Institute of Pharmacology, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; (R.V.S.); (D.K.)
- European Institute for Innovation through Health Data, c/o Department Medical Informatics and Statistics, Ghent University Hospital, C. Heymanslaan 10, 9000 Ghent, Belgium
| | - Dipak Kalra
- Heymans Institute of Pharmacology, Ghent University, C. Heymanslaan 10, 9000 Ghent, Belgium; (R.V.S.); (D.K.)
- European Institute for Innovation through Health Data, c/o Department Medical Informatics and Statistics, Ghent University Hospital, C. Heymanslaan 10, 9000 Ghent, Belgium
| | - Giuseppe Rengo
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Istituti Clinici Scientifici—ICS Maugeri S.p.A., Via Bagni Vecchi 1, 82037 Telese, Italy
| | - Francesco Giallauria
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Ugo Trama
- General Directorate for Health Protection and Coordination of the Regional Health System, Regione Campania, Centro Direzionale Is. C3, 80132 Naples, Italy;
| | - Nicola Ferrara
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
- Istituti Clinici Scientifici—ICS Maugeri S.p.A., Via Bagni Vecchi 1, 82037 Telese, Italy
| | - Antonio Cittadini
- Department of Translational Medical Sciences, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (L.B.); (M.A.); (G.R.); (F.G.); (N.F.); (A.C.)
| | - Maurizio Taglialatela
- Department of Neuroscience, Reproductive Sciences and Dentistry, Federico II University of Naples, Via Sergio Pansini 5, 80131 Naples, Italy; (C.C.); (C.R.); (A.M.); (L.D.); (M.T.)
| |
Collapse
|
2
|
Wind A, van der Linden C, Hartman E, Siesling S, van Harten W. Patient involvement in clinical pathway development, implementation and evaluation - A scoping review of international literature. PATIENT EDUCATION AND COUNSELING 2022; 105:1441-1448. [PMID: 34666931 DOI: 10.1016/j.pec.2021.10.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/16/2020] [Revised: 09/10/2021] [Accepted: 10/04/2021] [Indexed: 06/13/2023]
Abstract
OBJECTIVE Although various pathway design methods recognize patients as stakeholders, an overview of current practice is lacking. This article describes the results of a literature review assessing patient involvement in clinical cancer pathway development, implementation and evaluation. METHODS A scoping review was conducted following PRISMA-ScR. Two databases were searched to identify studies published in English between 2014 and 2021. RESULTS Of 12841articles identified 22 articles met the inclusion criteria and reported on one or more of the three phases: development phase (N = 2), implementation (N = 4), evaluation (N = 11), development/evaluation (N = 3), and implementation/evaluation (N = 2) of clinical pathways. The numbers of involved patients ranged from 10 to 793, and the reported methods varied considerably. CONCLUSION This review presents a synthesis of methods for involving patients in the clinical pathway lifecycle. No relationship was found between methods and the number of involved patients or between pathway complexity and methods. Although patients are seen as valuable stakeholders in the pathway design, to involve them in practice using the best practice can be improved. PRACTICE IMPLICATIONS The lack of a clear justification for the choice of methods and number of involved patients calls for further research and framework development to inform pathway developers.
Collapse
Affiliation(s)
- Anke Wind
- Rijnstate Hospital, Arnhem, the Netherland; Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, the Netherlands
| | | | - Elmar Hartman
- Rijnstate Hospital, Arnhem, the Netherland; Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands
| | - Sabine Siesling
- Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands; dept Research and Development, Netherlands Comprehensive Cancer Organisation (IKNL), Utrecht, the Netherlands
| | - Wim van Harten
- Rijnstate Hospital, Arnhem, the Netherland; Department of Health Technology and Services Research, University of Twente, Enschede, The Netherlands; The Netherlands Cancer Institute, Amsterdam. The Netherlands.
| |
Collapse
|
3
|
Khelifi M, Tarczy-Hornoch P, Devine EB, Pratt W. Design Recommendations for Pharmacogenomics Clinical Decision Support Systems. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE PROCEEDINGS. AMIA JOINT SUMMITS ON TRANSLATIONAL SCIENCE 2017; 2017:237-246. [PMID: 28815136 PMCID: PMC5543362] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/03/2022]
Abstract
The use of pharmacogenomics (PGx) in clinical practice still faces challenges to fully adopt genetic information in targeting drug therapy. To incorporate genetics into clinical practice, many support the use of Pharmacogenomics Clinical Decision Support Systems (PGx-CDS) for medication prescriptions. This support was fueled by new guidelines to incorporate genetics for optimizing drug dosage and reducing adverse events. In addition, the complexity of PGx led to exploring CDS outside the paradigm of the basic CDS tools embedded in commercial electronic health records. Therefore, designing the right CDS is key to unleashing the full potential of pharmacogenomics and making it a part of clinicians' daily workflow. In this work, we 1) identify challenges and barriers of the implementation of PGx-CDS in clinical settings, 2) develop a new design approach to CDS with functional characteristics that can improve the adoption of pharmacogenomics guidelines and thus patient safety, and 3) create design guidelines and recommendations for such PGx-CDS tools.
Collapse
|
4
|
Banjar H, Adelson D, Brown F, Chaudhri N. Intelligent Techniques Using Molecular Data Analysis in Leukaemia: An Opportunity for Personalized Medicine Support System. BIOMED RESEARCH INTERNATIONAL 2017; 2017:3587309. [PMID: 28812013 PMCID: PMC5547708 DOI: 10.1155/2017/3587309] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/05/2017] [Revised: 06/12/2017] [Accepted: 06/15/2017] [Indexed: 12/05/2022]
Abstract
The use of intelligent techniques in medicine has brought a ray of hope in terms of treating leukaemia patients. Personalized treatment uses patient's genetic profile to select a mode of treatment. This process makes use of molecular technology and machine learning, to determine the most suitable approach to treating a leukaemia patient. Until now, no reviews have been published from a computational perspective concerning the development of personalized medicine intelligent techniques for leukaemia patients using molecular data analysis. This review studies the published empirical research on personalized medicine in leukaemia and synthesizes findings across studies related to intelligence techniques in leukaemia, with specific attention to particular categories of these studies to help identify opportunities for further research into personalized medicine support systems in chronic myeloid leukaemia. A systematic search was carried out to identify studies using intelligence techniques in leukaemia and to categorize these studies based on leukaemia type and also the task, data source, and purpose of the studies. Most studies used molecular data analysis for personalized medicine, but future advancement for leukaemia patients requires molecular models that use advanced machine-learning methods to automate decision-making in treatment management to deliver supportive medical information to the patient in clinical practice.
Collapse
Affiliation(s)
- Haneen Banjar
- School of Computer Science, University of Adelaide, Adelaide, SA, Australia
- Department of Computer Science, King Abdulaziz University, Jeddah, Saudi Arabia
| | - David Adelson
- School of Molecular and Biomedical Science, University of Adelaide, Adelaide, SA, Australia
| | - Fred Brown
- School of Computer Science, University of Adelaide, Adelaide, SA, Australia
| | - Naeem Chaudhri
- Oncology Centre, Section of Hematology, HSCT, King Faisal Specialist Hospital and Research Centre, Riyadh, Saudi Arabia
| |
Collapse
|
5
|
Hinderer M, Boeker M, Wagner SA, Lablans M, Newe S, Hülsemann JL, Neumaier M, Binder H, Renz H, Acker T, Prokosch HU, Sedlmayr M. Integrating clinical decision support systems for pharmacogenomic testing into clinical routine - a scoping review of designs of user-system interactions in recent system development. BMC Med Inform Decis Mak 2017; 17:81. [PMID: 28587608 PMCID: PMC5461630 DOI: 10.1186/s12911-017-0480-y] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2016] [Accepted: 05/30/2017] [Indexed: 01/05/2023] Open
Abstract
Background Pharmacogenomic clinical decision support systems (CDSS) have the potential to help overcome some of the barriers for translating pharmacogenomic knowledge into clinical routine. Before developing a prototype it is crucial for developers to know which pharmacogenomic CDSS features and user-system interactions have yet been developed, implemented and tested in previous pharmacogenomic CDSS efforts and if they have been successfully applied. We address this issue by providing an overview of the designs of user-system interactions of recently developed pharmacogenomic CDSS. Methods We searched PubMed for pharmacogenomic CDSS published between January 1, 2012 and November 15, 2016. Thirty-two out of 118 identified articles were summarized and included in the final analysis. We then compared the designs of user-system interactions of the 20 pharmacogenomic CDSS we had identified. Results Alerts are the most widespread tools for physician-system interactions, but need to be implemented carefully to prevent alert fatigue and avoid liabilities. Pharmacogenomic test results and override reasons stored in the local EHR might help communicate pharmacogenomic information to other internal care providers. Integrating patients into user-system interactions through patient letters and online portals might be crucial for transferring pharmacogenomic data to external health care providers. Inbox messages inform physicians about new pharmacogenomic test results and enable them to request pharmacogenomic consultations. Search engines enable physicians to compare medical treatment options based on a patient’s genotype. Conclusions Within the last 5 years, several pharmacogenomic CDSS have been developed. However, most of the included articles are solely describing prototypes of pharmacogenomic CDSS rather than evaluating them. To support the development of prototypes further evaluation efforts will be necessary. In the future, pharmacogenomic CDSS will likely include prediction models to identify patients who are suitable for preemptive genotyping. Electronic supplementary material The online version of this article (doi:10.1186/s12911-017-0480-y) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Marc Hinderer
- Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 13, 91058, Erlangen, Germany.
| | - Martin Boeker
- Medical Informatics, Institute of Medical Biometry and Statistics, Faculty of Medicine and Medical Center - University of Freiburg, Freiburg, Germany
| | - Sebastian A Wagner
- Department of Medicine, Hematology/Oncology, Goethe University, Frankfurt, Germany
| | - Martin Lablans
- Medical Informatics in Translational Oncology, German Cancer Research Center, Heidelberg, Germany
| | - Stephanie Newe
- Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 13, 91058, Erlangen, Germany
| | | | - Michael Neumaier
- Institute for Clinical Chemistry, Medical Faculty Mannheim, Ruprecht-Karls-University Heidelberg, Mannheim, Germany
| | - Harald Binder
- Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Harald Renz
- University of Marburg, Institute of Laboratory Medicine, Marburg, Germany
| | - Till Acker
- Institute of Neuropathology, University of Giessen, Giessen, Germany
| | - Hans-Ulrich Prokosch
- Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 13, 91058, Erlangen, Germany
| | - Martin Sedlmayr
- Medical Informatics, Friedrich-Alexander-Universität Erlangen-Nürnberg, Wetterkreuz 13, 91058, Erlangen, Germany
| |
Collapse
|
6
|
Hizel HC. Highly personalized reports for personalized drug selection by expert systems as clinical decision support. Per Med 2017; 14:93-97. [PMID: 29754552 DOI: 10.2217/pme-2016-0083] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Affiliation(s)
- H Candan Hizel
- OPTI-THERA Inc., CHUM Pavilion R14-406 900, St-Denis street, Montreal (Quebec), H2X 0A9, Canada.,International & Interdisciplinary Association on the Pharmaceutical Life Cycle (IIAPC), Faculty of Law Montreal University C.P. 6128, Montreal (Quebec), H3C 3J7, Canada
| |
Collapse
|
7
|
Weitzel KW, Aquilante CL, Johnson S, Kisor DF, Empey PE. Educational strategies to enable expansion of pharmacogenomics-based care. Am J Health Syst Pharm 2016; 73:1986-1998. [PMID: 27864206 PMCID: PMC5665396 DOI: 10.2146/ajhp160104] [Citation(s) in RCA: 72] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023] Open
Abstract
PURPOSE The current state of pharmacogenomics education for pharmacy students and practitioners is discussed, and resources and strategies to address persistent challenges in this area are reviewed. SUMMARY Consensus-based pharmacist competencies and guidelines have been published to guide pharmacogenomics knowledge attainment and application in clinical practice. Pharmacogenomics education is integrated into various pharmacy school courses and, increasingly, into Pharm.D. curricula in the form of required standalone courses. Continuing-education programs and a limited number of postgraduate training opportunities are available to practicing pharmacists. For colleges and schools of pharmacy, identifying the optimal structure and content of pharmacogenomics education remains a challenge; insufficient numbers of faculty members with pharmacogenomics expertise and the inadequate availability of practice settings for experiential education are other limiting factors. Strategies for overcoming those challenges include providing early exposure to pharmacogenomics through foundational courses and incorporating pharmacogenomics into practice-based therapeutics courses and introductory and advanced pharmacy practice experiences. For practitioner education, online resources, clinical decision support-based tools, and certificate programs can be used to supplement structured postgraduate training in pharmacogenomics. Recently published data indicate successful use of "shared curricula" and participatory education models involving opportunities for learners to undergo personal genomic testing. CONCLUSION The pharmacy profession has taken a leadership role in expanding student and practitioner education to meet the demand for increased pharmacist involvement in precision medicine initiatives. Effective approaches to teaching pharmacogenomics knowledge and driving its appropriate application in clinical practice are increasingly available.
Collapse
Affiliation(s)
- Kristin Wiisanen Weitzel
- Personalized Medicine Program, UF Health, Gainesville, FL
- Department of Pharmacotherapy and Translational Research, University of Florida College of Pharmacy, Gainesville, FL
| | - Christina L Aquilante
- Department of Pharmaceutical Sciences, Skaggs School of Pharmacy and Pharmaceutical Sciences, University of Colorado, Aurora, CO
| | - Samuel Johnson
- Government and Professional Affairs, American College of Clinical Pharmacy, Washington, DC
| | - David F Kisor
- Department of Pharmaceutical Sciences, Manchester University College of Pharmacy, Natural and Health Sciences, Fort Wayne, IN
| | - Philip E Empey
- Department of Pharmacy and Therapeutics, School of Pharmacy and Institute for Precision Medicine, University of Pittsburgh, Pittsburgh, PA.
| |
Collapse
|
8
|
Heale BSE, Overby CL, Del Fiol G, Rubinstein WS, Maglott DR, Nelson TH, Milosavljevic A, Martin CL, Goehringer SR, Freimuth R, Williams MS. Integrating Genomic Resources with Electronic Health Records using the HL7 Infobutton Standard. Appl Clin Inform 2016; 7:817-31. [PMID: 27579472 DOI: 10.4338/aci-2016-04-ra-0058] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2016] [Accepted: 07/25/2016] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND The Clinical Genome Resource (ClinGen) Electronic Health Record (EHR) Workgroup aims to integrate ClinGen resources with EHRs. A promising option to enable this integration is through the Health Level Seven (HL7) Infobutton Standard. EHR systems that are certified according to the US Meaningful Use program provide HL7-compliant infobutton capabilities, which can be leveraged to support clinical decision-making in genomics. OBJECTIVES To integrate genomic knowledge resources using the HL7 infobutton standard. Two tactics to achieve this objective were: (1) creating an HL7-compliant search interface for ClinGen, and (2) proposing guidance for genomic resources on achieving HL7 Infobutton standard accessibility and compliance. METHODS We built a search interface utilizing OpenInfobutton, an open source reference implementation of the HL7 Infobutton standard. ClinGen resources were assessed for readiness towards HL7 compliance. Finally, based upon our experiences we provide recommendations for publishers seeking to achieve HL7 compliance. RESULTS Eight genomic resources and two sub-resources were integrated with the ClinGen search engine via OpenInfobutton and the HL7 infobutton standard. Resources we assessed have varying levels of readiness towards HL7-compliance. Furthermore, we found that adoption of standard terminologies used by EHR systems is the main gap to achieve compliance. CONCLUSION Genomic resources can be integrated with EHR systems via the HL7 Infobutton standard using OpenInfobutton. Full compliance of genomic resources with the Infobutton standard would further enhance interoperability with EHR systems.
Collapse
Affiliation(s)
- Bret S E Heale
- Bret S.E. Heale, Ph.D., 421 Wakara Way #140, Salt Lake City, UT 84108,
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
9
|
Blagec K, Romagnoli KM, Boyce RD, Samwald M. Examining perceptions of the usefulness and usability of a mobile-based system for pharmacogenomics clinical decision support: a mixed methods study. PeerJ 2016; 4:e1671. [PMID: 26925317 PMCID: PMC4768706 DOI: 10.7717/peerj.1671] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Accepted: 01/19/2016] [Indexed: 12/12/2022] Open
Abstract
Background. Pharmacogenomic testing has the potential to improve the safety and efficacy of pharmacotherapy, but clinical application of pharmacogenetic knowledge has remained uncommon. Clinical Decision Support (CDS) systems could help overcome some of the barriers to clinical implementation. The aim of this study was to evaluate the perception and usability of a web- and mobile-enabled CDS system for pharmacogenetics-guided drug therapy–the Medication Safety Code (MSC) system–among potential users (i.e., physicians and pharmacists). Furthermore, this study sought to collect data on the practicability and comprehensibility of potential layouts of a proposed personalized pocket card that is intended to not only contain the machine-readable data for use with the MSC system but also human-readable data on the patient’s pharmacogenomic profile. Methods. We deployed an emergent mixed methods design encompassing (1) qualitative interviews with pharmacists and pharmacy students, (2) a survey among pharmacogenomics experts that included both qualitative and quantitative elements and (3) a quantitative survey among physicians and pharmacists. The interviews followed a semi-structured guide including a hypothetical patient scenario that had to be solved by using the MSC system. The survey among pharmacogenomics experts focused on what information should be printed on the card and how this information should be arranged. Furthermore, the MSC system was evaluated based on two hypothetical patient scenarios and four follow-up questions on the perceived usability. The second survey assessed physicians’ and pharmacists’ attitude towards the MSC system. Results. In total, 101 physicians, pharmacists and PGx experts coming from various relevant fields evaluated the MSC system. Overall, the reaction to the MSC system was positive across all investigated parameters and among all user groups. The majority of participants were able to solve the patient scenarios based on the recommendations displayed on the MSC interface. A frequent request among participants was to provide specific listings of alternative drugs and concrete dosage instructions. Negligence of other patient-specific factors for choosing the right treatment such as renal function and co-medication was a common concern related to the MSC system, while data privacy and cost-benefit considerations emerged as the participants’ major concerns regarding pharmacogenetic testing in general. The results of the card layout evaluation indicate that a gene-centered and tabulated presentation of the patient’s pharmacogenomic profile is helpful and well-accepted. Conclusions. We found that the MSC system was well-received among the physicians and pharmacists included in this study. A personalized pocket card that lists a patient’s metabolizer status along with critically affected drugs can alert physicians and pharmacists to the availability of essential therapy modifications.
Collapse
Affiliation(s)
- Kathrin Blagec
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna , Vienna , Austria
| | - Katrina M Romagnoli
- Department of Biomedical Informatics, University of Pittsburgh , Pittsburgh, Pennsylvania , United States
| | - Richard D Boyce
- Department of Biomedical Informatics, University of Pittsburgh , Pittsburgh, Pennsylvania , United States
| | - Matthias Samwald
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna , Vienna , Austria
| |
Collapse
|
10
|
Cutting EM, Overby CL, Banchero M, Pollin T, Kelemen M, Shuldiner AR, Beitelshees AL. Using Workflow Modeling to Identify Areas to Improve Genetic Test Processes in the University of Maryland Translational Pharmacogenomics Project. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2015; 2015:466-474. [PMID: 26958179 PMCID: PMC4765659] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/05/2023]
Abstract
Delivering genetic test results to clinicians is a complex process. It involves many actors and multiple steps, requiring all of these to work together in order to create an optimal course of treatment for the patient. We used information gained from focus groups in order to illustrate the current process of delivering genetic test results to clinicians. We propose a business process model and notation (BPMN) representation of this process for a Translational Pharmacogenomics Project being implemented at the University of Maryland Medical Center, so that personalized medicine program implementers can identify areas to improve genetic testing processes. We found that the current process could be improved to reduce input errors, better inform and notify clinicians about the implications of certain genetic tests, and make results more easily understood. We demonstrate our use of BPMN to improve this important clinical process for CYP2C19 genetic testing in patients undergoing invasive treatment of coronary heart disease.
Collapse
Affiliation(s)
- Elizabeth M Cutting
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Casey L Overby
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Meghan Banchero
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Toni Pollin
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Mark Kelemen
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Alan R Shuldiner
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Amber L Beitelshees
- Program in Personalized and Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| |
Collapse
|
11
|
Overby CL, Devine EB, Abernethy N, McCune JS, Tarczy-Hornoch P. Making pharmacogenomic-based prescribing alerts more effective: A scenario-based pilot study with physicians. J Biomed Inform 2015; 55:249-59. [PMID: 25957826 DOI: 10.1016/j.jbi.2015.04.011] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2014] [Revised: 04/27/2015] [Accepted: 04/28/2015] [Indexed: 01/06/2023]
Abstract
To facilitate personalized drug dosing (PDD), this pilot study explored the communication effectiveness and clinical impact of using a prototype clinical decision support (CDS) system embedded in an electronic health record (EHR) to deliver pharmacogenomic (PGx) information to physicians. We employed a conceptual framework and measurement model to access the impact of physician characteristics (previous experience, awareness, relative advantage, perceived usefulness), technology characteristics (methods of implementation-semi-active/active, actionability-low/high) and a task characteristic (drug prescribed) on communication effectiveness (usefulness, confidence in prescribing decision), and clinical impact (uptake, prescribing intent, change in drug dosing). Physicians performed prescribing tasks using five simulated clinical case scenarios, presented in random order within the prototype PGx-CDS system. Twenty-two physicians completed the study. The proportion of physicians that saw a relative advantage to using PGx-CDS was 83% at the start and 94% at the conclusion of our study. Physicians used semi-active alerts 74-88% of the time. There was no association between previous experience with, awareness of, and belief in a relative advantage of using PGx-CDS and improved uptake. The proportion of physicians reporting confidence in their prescribing decisions decreased significantly after using the prototype PGx-CDS system (p=0.02). Despite decreases in confidence, physicians perceived a relative advantage to using PGx-CDS, viewed semi-active alerts on most occasions, and more frequently changed doses toward doses supported by published evidence. Specifically, sixty-five percent of physicians reduced their dosing, significantly for capecitabine (p=0.002) and mercaptopurine/thioguanine (p=0.03). These findings suggest a need to improve our prototype such that PGx CDS content is more useful and delivered in a way that improves physician's confidence in their prescribing decisions. The greatest increases in communication effectiveness and clinical impact of PGx-CDS are likely to be realized through continued focus on content, content delivery, and tailoring to physician characteristics.
Collapse
Affiliation(s)
- Casey Lynnette Overby
- Program in Personalized & Genomic Medicine, Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, United States.
| | - Emily Beth Devine
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Department of Pharmacy, University of Washington, Seattle, WA, United States; Department of Health Services, University of Washington, Seattle, WA, United States
| | - Neil Abernethy
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Department of Health Services, University of Washington, Seattle, WA, United States
| | - Jeannine S McCune
- Department of Pharmacy, University of Washington, Seattle, WA, United States
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics and Medical Education, University of Washington, Seattle, WA, United States; Department of Pediatrics, University of Washington, Seattle, WA, United States; Department of Computer Science & Engineering, University of Washington, Seattle, WA, United States
| |
Collapse
|
12
|
Formea CM, Nicholson WT, Vitek CR. An inter-professional approach to personalized medicine education: one institution's experience. Per Med 2015; 12:129-138. [PMID: 28413426 PMCID: PMC5391796 DOI: 10.2217/pme.14.63] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Personalized medicine offers the promise of better diagnoses, targeted therapies and individualized treatment plans. Pharmacogenomics is an integral component of personalized medicine; it aids in the prediction of an individual's response to medications. Despite growing public acceptance and emerging clinical evidence, this rapidly expanding field of medicine is slow to be adopted and utilized by healthcare providers, although many believe that they should be knowledgeable and able to apply pharmacogenomics in clinical practice. Institutional infrastructure must be built to support pharmacogenomic implementation. Multidisciplinary education for healthcare providers is a critical component for pharmacogenomics to achieve its full potential to optimize patient care. We describe our recent experience at the Mayo Clinic implementing pharmacogenomics education in a large, academic healthcare system facilitated by the Mayo Clinic Center for Individualized Medicine.
Collapse
Affiliation(s)
- Christine M Formea
- Hospital Pharmacy Services, Mary Brigh Building G-722, Mayo Clinic Hospital-St Marys Campus, 200 First Street SW, Rochester, MN 55905, USA
| | - Wayne T Nicholson
- Department of Anesthesiology, Mayo Clinic Hospital-St Marys Campus, 200 First Street, Rochester, MN 55905, USA
| | | |
Collapse
|
13
|
Linares OA, Daly D, Linares AD, Stefanovski D, Boston RC. Personalized Oxycodone Dosing: Using Pharmacogenetic Testing and Clinical Pharmacokinetics to Reduce Toxicity Risk and Increase Effectiveness. PAIN MEDICINE 2014; 15:791-806. [DOI: 10.1111/pme.12380] [Citation(s) in RCA: 29] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
|
14
|
Cimino JJ, Overby CL, Devine EB, Hulse NC, Jing X, Maviglia SM, Del Fiol G. Practical choices for infobutton customization: experience from four sites. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2013; 2013:236-245. [PMID: 24551334 PMCID: PMC3900175] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/03/2023]
Abstract
Context-aware links between electronic health records (EHRs) and online knowledge resources, commonly called "infobuttons" are being used increasingly as part of EHR "meaningful use" requirements. While an HL7 standard exists for specifying how the links should be constructed, there is no guidance on what links to construct. Collectively, the authors manage four infobutton systems that serve 16 institutions. The purpose of this paper is to publish our experience with linking various resources and specifying particular criteria that can be used by infobutton managers to select resources that are most relevant for a given situation. This experience can be used directly by those wishing to customize their own EHRs, for example by using the OpenInfobutton infobutton manager and its configuration tool, the Librarian Infobutton Tailoring Environment.
Collapse
Affiliation(s)
- James J Cimino
- Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD ; Department of Biomedical Informatics, Columbia University, New York, NY
| | - Casey L Overby
- Department of Biomedical Informatics, Columbia University, New York, NY ; Department of Medicine, University of Maryland, Baltimore, MD
| | - Emily B Devine
- Department of Health Services, University of Washington, Seattle, WA
| | | | - Xia Jing
- Laboratory for Informatics Development, NIH Clinical Center, Bethesda, MD
| | | | - Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT
| |
Collapse
|
15
|
Tarczy-Hornoch P, Amendola L, Aronson SJ, Garraway L, Gray S, Grundmeier RW, Hindorff LA, Jarvik G, Karavite D, Lebo M, Plon SE, Van Allen E, Weck KE, White PS, Yang Y. A survey of informatics approaches to whole-exome and whole-genome clinical reporting in the electronic health record. Genet Med 2013; 15:824-32. [PMID: 24071794 PMCID: PMC3951437 DOI: 10.1038/gim.2013.120] [Citation(s) in RCA: 53] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2013] [Accepted: 07/09/2013] [Indexed: 11/10/2022] Open
Abstract
PURPOSE Genome-scale clinical sequencing is being adopted more broadly in medical practice. The National Institutes of Health developed the Clinical Sequencing Exploratory Research (CSER) program to guide implementation and dissemination of best practices for the integration of sequencing into clinical care. This study describes and compares the state of the art of incorporating whole-exome and whole-genome sequencing results into the electronic health record, including approaches to decision support across the six current CSER sites. METHODS The CSER Medical Record Working Group collaboratively developed and completed an in-depth survey to assess the communication of genome-scale data into the electronic health record. We summarized commonalities and divergent approaches. RESULTS Despite common sequencing platform (Illumina) adoptions, there is a great diversity of approaches to annotation tools and workflow, as well as to report generation. At all sites, reports are human-readable structured documents available as passive decision support in the electronic health record. Active decision support is in early implementation at two sites. CONCLUSION The parallel efforts across CSER sites in the creation of systems for report generation and integration of reports into the electronic health record, as well as the lack of standardized approaches to interfacing with variant databases to create active clinical decision support, create opportunities for cross-site and vendor collaborations.
Collapse
Affiliation(s)
- Peter Tarczy-Hornoch
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- University of Washington, Seattle, Washington, USA
| | - Laura Amendola
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- University of Washington, Seattle, Washington, USA
| | - Samuel J. Aronson
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Partners HealthCare Center for Personalized Genetic Medicine, Boston, Massachusetts, USA
| | - Levi Garraway
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Stacy Gray
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Robert W. Grundmeier
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Center for Biomedical Informatics, The Children's Hospital Philadelphia, Philadelphia, Pennsylvania, USA
| | - Lucia A. Hindorff
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- National Human Genome Research Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Gail Jarvik
- University of Washington, Seattle, Washington, USA
| | - Dean Karavite
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Center for Biomedical Informatics, The Children's Hospital Philadelphia, Philadelphia, Pennsylvania, USA
| | - Matthew Lebo
- Partners HealthCare Center for Personalized Genetic Medicine, Boston, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Brigham and Women's Hospital, Boston, Massachusetts, USA
| | - Sharon E. Plon
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Baylor College of Medicine, Houston, Texas, USA
| | - Eliezer Van Allen
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Dana-Farber Cancer Institute, Boston, Massachusetts, USA
- The Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Karen E. Weck
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Peter S. White
- National Institutes of Health National Human Genome Research Institute Clinical Sequencing Exploratory Research Electronic Medical Records Working Group, Bethesda, Maryland, USA
- Harvard Medical School, Boston, Massachusetts, USA
- Center for Biomedical Informatics, The Children's Hospital Philadelphia, Philadelphia, Pennsylvania, USA
| | - Yaping Yang
- Baylor College of Medicine, Houston, Texas, USA
| |
Collapse
|
16
|
Overby CL, Kohane I, Kannry JL, Williams MS, Starren J, Bottinger E, Gottesman O, Denny JC, Weng C, Tarczy-Hornoch P, Hripcsak G. Opportunities for genomic clinical decision support interventions. Genet Med 2013; 15:817-23. [PMID: 24051479 DOI: 10.1038/gim.2013.128] [Citation(s) in RCA: 56] [Impact Index Per Article: 5.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2013] [Accepted: 07/15/2013] [Indexed: 12/12/2022] Open
Affiliation(s)
- Casey Lynnette Overby
- 1] Department of Biomedical Informatics, Columbia University, New York, New York, USA [2] Program for Personalized & Genomic Medicine and Center for Health-Related Informatics and Bioimaging, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | | | | | | | | | | | | | | | | | | | | |
Collapse
|
17
|
Overby CL, Tarczy-Hornoch P. Personalized medicine: challenges and opportunities for translational bioinformatics. Per Med 2013; 10:453-462. [PMID: 24039624 PMCID: PMC3770190 DOI: 10.2217/pme.13.30] [Citation(s) in RCA: 37] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/30/2022]
Abstract
Personalized medicine can be defined broadly as a model of healthcare that is predictive, personalized, preventive and participatory. Two US President's Council of Advisors on Science and Technology reports illustrate challenges in personalized medicine (in a 2008 report) and in use of health information technology (in a 2010 report). Translational bioinformatics is a field that can help address these challenges and is defined by the American Medical Informatics Association as "the development of storage, analytic and interpretive methods to optimize the transformation of increasing voluminous biomedical data into proactive, predictive, preventative and participatory health." This article discusses barriers to implementing genomics applications and current progress toward overcoming barriers, describes lessons learned from early experiences of institutions engaged in personalized medicine and provides example areas for translational bioinformatics research inquiry.
Collapse
Affiliation(s)
- Casey Lynnette Overby
- Program in Personalized & Genomic Medicine and Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Peter Tarczy-Hornoch
- Department of Biomedical Informatics & Medical Education, University of Washington, Seattle, WA, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Computer Science & Engineering, University of Washington, Seattle, WA, USA
| |
Collapse
|
18
|
Fiol GD, Curtis C, Cimino JJ, Iskander A, Kalluri AS, Jing X, Hulse NC, Long J, Overby CL, Schardt C, Douglas DM. Disseminating context-specific access to online knowledge resources within electronic health record systems. Stud Health Technol Inform 2013; 192:672-676. [PMID: 23920641 PMCID: PMC3870015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/02/2023]
Abstract
Clinicians' patient care information needs are frequent and largely unmet. Online knowledge resources are available that can help clinicians meet these information needs. Yet, significant barriers limit the use of these resources within the clinical workflow. Infobuttons are clinical decision support tools that use the clinical context (e.g., institution, user, patient) within electronic health record (EHR) systems to anticipate clinicians' questions and provide automated links to relevant information in knowledge resources. This paper describes OpenInfobutton (www.openinfobutton.org): a standards-based, open source Web service that was designed to disseminate infobutton capabilities in multiple EHR systems and healthcare organizations. OpenInfobutton has been successfully integrated with 38 knowledge resources at 5 large healthcare organizations in the United States. We describe the OpenInfobutton architecture, knowledge resource integration, and experiences at five large healthcare organizations.
Collapse
Affiliation(s)
- Guilherme Del Fiol
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | | | - James J. Cimino
- Laboratory for Informatics Development, National Institutes of Health (NIH) Clinicial Center, Bethesda, MD, USA
| | - Andrew Iskander
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Aditya S.D. Kalluri
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
| | - Xia Jing
- Laboratory for Informatics Development, National Institutes of Health (NIH) Clinicial Center, Bethesda, MD, USA
| | - Nathan C. Hulse
- Department of Biomedical Informatics, University of Utah, Salt Lake City, UT, USA
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, UT, USA
| | - Jie Long
- Homer Warner Center for Informatics Research, Intermountain Healthcare, Salt Lake City, UT, USA
| | - Casey L. Overby
- Department of Biomedical Informatics, Columbia University, New York, NY, USA
| | | | | |
Collapse
|