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Pomann GM, Truong T, Boulos M, Boulware LE, Brouwer RN, Curtis LH, Kapphahn K, Khalatbari S, McKeel J, Messinger S, O’Hara R, Pencina MJ, Samsa GP, Spino C, Zidanyue Yang L, Desai M. Needles in a Haystack: Finding Qualitative and Quantitative Collaborators in Academic Medical Centers. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2023; 98:889-895. [PMID: 36940408 PMCID: PMC10440235 DOI: 10.1097/acm.0000000000005212] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2023]
Abstract
Translational research is a data-driven process that involves transforming scientific laboratory- and clinic-based discoveries into products and activities with real-world impact to improve individual and population health. Successful execution of translational research requires collaboration between clinical and translational science researchers, who have expertise in a wide variety of domains across the field of medicine, and qualitative and quantitative scientists, who have specialized methodologic expertise across diverse methodologic domains. While many institutions are working to build networks of these specialists, a formalized process is needed to help researchers navigate the network to find the best match and to track the navigation process to evaluate an institution's unmet collaborative needs. In 2018, a novel analytic resource navigation process was developed at Duke University to connect potential collaborators, leverage resources, and foster a community of researchers and scientists. This analytic resource navigation process can be readily adopted by other academic medical centers. The process relies on navigators with broad qualitative and quantitative methodologic knowledge, strong communication and leadership skills, and extensive collaborative experience. The essential elements of the analytic resource navigation process are as follows: (1) strong institutional knowledge of methodologic expertise and access to analytic resources, (2) deep understanding of research needs and methodologic expertise, (3) education of researchers on the role of qualitative and quantitative scientists in the research project, and (4) ongoing evaluation of the analytic resource navigation process to inform improvements. Navigators help researchers determine the type of expertise needed, search the institution to find potential collaborators with that expertise, and document the process to evaluate unmet needs. Although the navigation process can create a basis for an effective solution, some challenges remain, such as having resources to train navigators, comprehensively identifying all potential collaborators, and keeping updated information about resources as methodologists join and leave the institution.
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Affiliation(s)
- Gina-Maria Pomann
- Biostatistics, Epidemiology, and Research Design (BERD) Methods Core, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Tracy Truong
- Biostatistics, Epidemiology, and Research Design (BERD) Methods Core, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Mary Boulos
- BERD Core, Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Palo Alto, California
| | - L. Ebony Boulware
- Duke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, North Carolina
| | - Rebecca N. Brouwer
- Duke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, North Carolina
| | - Lesley H. Curtis
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Kristopher Kapphahn
- BERD Core, Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Palo Alto, California
| | - Shokoufeh Khalatbari
- Biostatistics Program, Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, Michigan
| | - Julie McKeel
- Duke Clinical and Translational Science Institute, Duke University School of Medicine, Durham, North Carolina
| | - Shari Messinger
- BERD Program, Miami Clinical and Translational Science Institute, University of Miami, Miami, Florida
| | - Ruth O’Hara
- Stanford University School of Medicine, Palo Alto, California
| | - Michael J. Pencina
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Greg P. Samsa
- Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Cathie Spino
- Biostatistics Program, Michigan Institute for Clinical and Health Research, University of Michigan, Ann Arbor, Michigan
| | - Lexie Zidanyue Yang
- Biostatistics, Epidemiology, and Research Design (BERD) Methods Core, Department of Biostatistics and Bioinformatics, Duke University School of Medicine, Durham, North Carolina
| | - Manisha Desai
- BERD Core, Quantitative Sciences Unit, Department of Medicine, Stanford University School of Medicine, Palo Alto, California
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LeBlanc M, Rueegg CS, Bekiroğlu N, Esterhuizen TM, Fagerland MW, Falk RS, Frøslie KF, Graf E, Heinze G, Held U, Holst R, Lange T, Mazumdar M, Myrberg IH, Posch M, Sergeant JC, Vach W, Vance EA, Weedon-Fekjaer H, Zucknick M. Statistical advising: Professional development opportunities for the biostatistician. Stat Med 2022; 41:847-859. [PMID: 35194815 PMCID: PMC9303234 DOI: 10.1002/sim.9290] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2021] [Revised: 11/25/2021] [Accepted: 12/07/2021] [Indexed: 11/25/2022]
Affiliation(s)
- Marissa LeBlanc
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Corina S Rueegg
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Nural Bekiroğlu
- Department of Biostatistics, Medical School, Marmara University, İstanbul, Turkey
| | - Tonya M Esterhuizen
- Division of Epidemiology and Biostatistics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Morten W Fagerland
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Ragnhild S Falk
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Kathrine F Frøslie
- Faculty of Chemistry, Biotechnology and Food Sciences, Norwegian University of Life Sciences, Ås, Norway
| | - Erika Graf
- Faculty of Medicine and Medical Center, Institute of Medical Biometry and Statistics, University of Freiburg, Freiburg, Germany
| | - Georg Heinze
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Ulrike Held
- Department of Biostatistics at Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Zurich, Switzerland
| | - René Holst
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway.,Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
| | - Theis Lange
- Section of Biostatistics, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.,Centre for Statistical Science, Peking University, Beijing, China
| | - Madhu Mazumdar
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Ida H Myrberg
- Division of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Martin Posch
- Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria
| | - Jamie C Sergeant
- Centre for Biostatistics, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK.,Centre for Epidemiology Versus Arthritis, Centre for Musculoskeletal Research, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Werner Vach
- Basel Academy for Quality and Research in Medicine, Basel, Switzerland.,Department of Environmental Sciences, University of Basel, Basel, Switzerland
| | - Eric A Vance
- Laboratory for Interdisciplinary Statistical Analysis, Department of Applied Mathematics, University of Colorado Boulder, Boulder, Colorado, USA
| | - Harald Weedon-Fekjaer
- Oslo Centre for Biostatistics and Epidemiology, Research Support Services, Oslo University Hospital, Oslo, Norway
| | - Manuela Zucknick
- Oslo Centre for Biostatistics and Epidemiology, University of Oslo, Oslo, Norway
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Mazumdar M, Poeran JV, Ferket BS, Zubizarreta N, Agarwal P, Gorbenko K, Craven CK, Zhong XT, Moskowitz AJ, Gelijns AC, Reich DL. Developing an Institute for Health Care Delivery Science: successes, challenges, and solutions in the first five years. Health Care Manag Sci 2020; 24:234-243. [PMID: 33161511 DOI: 10.1007/s10729-020-09521-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 09/17/2020] [Indexed: 10/23/2022]
Abstract
Medical knowledge is increasing at an exponential rate. At the same time, unexplained variations in practice and patient outcomes and unacceptable rates of medical errors and inefficiencies in health care delivery have emerged. Our Institute for Health Care Delivery Science (I-HDS) began in 2014 as a novel platform to conduct multidisciplinary healthcare delivery research. We followed ten strategies to develop a successful institute with excellence in methodology and strong understanding of the value of team science. Our work was organized around five hubs: 1) Quality/Process Improvement and Systematic Review, 2) Comparative Effectiveness Research, Pragmatic Clinical Trials, and Predictive Analytics, 3) Health Economics and Decision Modeling, 4) Qualitative, Survey, and Mixed Methods, and 5) Training and Mentoring. In the first 5 years of the I-HDS, we have identified opportunities for change in clinical practice through research using our health system's electronic health record (EHR) data, and designed programs to educate clinicians in the value of research to improve patient care and recognize efficiencies in processes. Testing the value of several model interventions has guided prioritization of evidence-based quality improvements. Some of the changes in practice have already been embedded in the EHR workflow successfully. Development and sustainability of the I-HDS has been fostered by a mix of internal and external funding, including philanthropic foundations. Challenges remain due to the highly competitive funding environment and changes needed to adapt the EHR to healthcare delivery research. Further stakeholder engagement and culture change working with hospital leadership and I-HDS core and affiliate members continues.
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Affiliation(s)
- Madhu Mazumdar
- Institute for Health Care Delivery Science, Center for Biostatistics, Department of Population Health Science and Policy, Tisch Cancer Institute, Icahn School of Medicine at Mount Sinai, 1 Gustave L. Levy Pl, New York, NY, 10029, USA.
| | - Jashvant V Poeran
- Institute for Health Care Delivery Science, Departments of Population Health Science and Policy, Medicine, and Orthopedics, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bart S Ferket
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole Zubizarreta
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Parul Agarwal
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ksenia Gorbenko
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Catherine K Craven
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Clinical Informatics Group, Information Technology, Mount Sinai Health System, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Xiaobo Tony Zhong
- Institute for Health Care Delivery Science, Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alan J Moskowitz
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Annetine C Gelijns
- Department of Population Health Science and Policy, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - David L Reich
- Mount Sinai Hospital, Mount Sinai Queens, New York, NY, USA
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Incorporating professional recommendations into a graduate-level statistical consulting laboratory: A case study. J Clin Transl Sci 2020; 5:e62. [PMID: 33948282 PMCID: PMC8057384 DOI: 10.1017/cts.2020.527] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
Abstract
Introduction: There has been a recent trend in medical research towards a more collaborative relationship between statisticians and clinical investigators. This has led to an increased focus on the most efficient and effective ways to structure, conduct, and measure the impact of organizations that provide statistical services to clinical investigators. Several recent guidelines and recommendations on the conduct of statistical consulting services(SCSs) have been made in response to this need, focusing on larger SCSs consisting primarily of faculty and staff statisticians. However, the application of these recommendations to consulting services primarily staffed by graduate students, which have the dual role of providing a professional service and training, remains unclear. Methods: Guidelines and recommendations, primarily from the Clinical and Translational Science (CTSA) consortium, were applied to a SCS staffed primarily by graduate students in an academic health center. A description of the organizational structure and outcomes after 3 years of operation is presented. Results: The guidelines recommended by the CTSA consortium and other groups were successfully incorporated into the graduate consulting laboratory. At almost one new project request per week, the consulting laboratory demonstrated a large bandwidth and had an excellent feedback from investigators. Conclusions: Guidelines developed for larger statistical consulting organizations are able to be applied in student-led consultation organizations. Outcomes and recommendations from 3.5 years of operation are used to describe the successes and challenges we have encountered.
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