1
|
Kim J, Villarreal M, Arya S, Hernandez A, Moreira A. Bridging the Gap: Exploring Bronchopulmonary Dysplasia through the Lens of Biomedical Informatics. J Clin Med 2024; 13:1077. [PMID: 38398389 PMCID: PMC10889493 DOI: 10.3390/jcm13041077] [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: 12/28/2023] [Revised: 02/07/2024] [Accepted: 02/12/2024] [Indexed: 02/25/2024] Open
Abstract
Bronchopulmonary dysplasia (BPD), a chronic lung disease predominantly affecting premature infants, poses substantial clinical challenges. This review delves into the promise of biomedical informatics (BMI) in reshaping BPD research and care. We commence by highlighting the escalating prevalence and healthcare impact of BPD, emphasizing the necessity for innovative strategies to comprehend its intricate nature. To this end, we introduce BMI as a potent toolset adept at managing and analyzing extensive, diverse biomedical data. The challenges intrinsic to BPD research are addressed, underscoring the inadequacies of conventional approaches and the compelling need for data-driven solutions. We subsequently explore how BMI can revolutionize BPD research, encompassing genomics and personalized medicine to reveal potential biomarkers and individualized treatment strategies. Predictive analytics emerges as a pivotal facet of BMI, enabling early diagnosis and risk assessment for timely interventions. Moreover, we examine how mobile health technologies facilitate real-time monitoring and enhance patient engagement, ultimately refining BPD management. Ethical and legal considerations surrounding BMI implementation in BPD research are discussed, accentuating issues of privacy, data security, and informed consent. In summation, this review highlights BMI's transformative potential in advancing BPD research, addressing challenges, and opening avenues for personalized medicine and predictive analytics.
Collapse
Affiliation(s)
- Jennifer Kim
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Mariela Villarreal
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Shreyas Arya
- Division of Neonatal-Perinatal Medicine, Dayton Children’s Hospital, Dayton, OH 45404, USA
| | - Antonio Hernandez
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| | - Alvaro Moreira
- Division of Neonatology, Department of Pediatrics, University of Texas Health San Antonio, San Antonio, TX 78229, USA; (J.K.); (M.V.); (A.H.)
| |
Collapse
|
2
|
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.
Collapse
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
| |
Collapse
|
3
|
Mendonca EA, Richesson RL, Hochheiser H, Cooper DM, Bruck MN, Berner ES. Informatics education for translational research teams: An unrealized opportunity to strengthen the national research infrastructure. J Clin Transl Sci 2022; 6:e130. [PMID: 36590353 PMCID: PMC9794970 DOI: 10.1017/cts.2022.481] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 10/04/2022] [Accepted: 10/04/2022] [Indexed: 12/28/2022] Open
Abstract
Objective To identify the informatics educational needs of clinical and translational research professionals whose primary focus is not informatics. Introduction Informatics and data science skills are essential for the full spectrum of translational research, and an increased understanding of informatics issues on the part of translational researchers can alleviate the demand for informaticians and enable more productive collaborations when informaticians are involved. Identifying the level of interest in different topics among various types of of translational researchers will help set priorities for development and dissemination of informatics education. Methods We surveyed clinical and translational science researchers in Clinical and Translational Science Award (CTSA) programs about their educational needs and preferences. Results Researchers from 23 out of the 62 CTSA hubs responded to the survey. 67% of respondents across roles and topics expressed interest in learning about informatics topics. There was high interest in all 30 topics included in the survey, with some variation in interest depending on the role of the respondents. Discussion Our data support the need to advance training in clinical and biomedical informatics. As the complexity and use of information technology and data science in research studies grows, informaticians will continue to be a limited resource for research collaboration, education, and training. An increased understanding of informatics issues across translational research teams can alleviate this burden and allow for more productive collaborations. To inform a roadmap for informatics education for research professionals, we suggest strategies to use the results of this needs assessment to develop future informatics education.
Collapse
Affiliation(s)
- Eneida A. Mendonca
- Indiana University/Regenstrief Institute, Indianapolis, IN, USA
- Cincinnati Children’s Hospital and University of Cincinnati, Cincinnati, OH, USA
| | | | | | | | - Meg N. Bruck
- University of Alabama at Birmingham, Birmingham, AL, USA
| | - Eta S. Berner
- University of Alabama at Birmingham, Birmingham, AL, USA
| |
Collapse
|
4
|
Maré IA, Kramer B, Hazelhurst S, Nhlapho MD, Zent R, Harris PA, Klipin M. Electronic Data Capture System (REDCap) for Health Care Research and Training in a Resource-Constrained Environment: Technology Adoption Case Study. JMIR Med Inform 2022; 10:e33402. [PMID: 36040763 PMCID: PMC9472062 DOI: 10.2196/33402] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 03/01/2022] [Accepted: 05/31/2022] [Indexed: 01/04/2023] Open
Abstract
Background Electronic data capture (EDC) in academic health care organizations provides an opportunity for the management, aggregation, and secondary use of research and clinical data. It is especially important in resource-constrained environments such as the South African public health care sector, where paper records are still the main form of clinical record keeping. Objective The aim of this study was to describe the strategies followed by the University of the Witwatersrand Faculty of Health Sciences (Wits FHS) during the period from 2013 to 2021 to overcome resistance to, and encourage the adoption of, the REDCap (Research Electronic Data Capture; Vanderbilt University) system by academic and clinical staff. REDCap has found wide use in varying domains, including clinical studies and research projects as well as administrative, financial, and human resource applications. Given REDCap’s global footprint in >5000 institutions worldwide and potential for future growth, the strategies followed by the Wits FHS to support users and encourage adoption may be of importance to others using the system, particularly in resource-constrained settings. Methods The strategies to support users and encourage adoption included top-down organizational support; secure and reliable application, hosting infrastructure, and systems administration; an enabling and accessible REDCap support team; regular hands-on training workshops covering REDCap project setup and data collection instrument design techniques; annual local symposia to promote networking and awareness of all the latest software features and best practices for using them; participation in REDCap Consortium activities; and regular and ongoing mentorship from members of the Vanderbilt University Medical Center. Results During the period from 2013 to 2021, the use of the REDCap EDC system by individuals at the Wits FHS increased, respectively, from 129 active user accounts to 3447 active user accounts. The number of REDCap projects increased from 149 in 2013 to 12,865 in 2021. REDCap at Wits also supported various publications and research outputs, including journal articles and postgraduate monographs. As of 2020, a total of 233 journal articles and 87 postgraduate monographs acknowledged the use of the Wits REDCap system. Conclusions By providing reliable infrastructure and accessible support resources, we were able to successfully implement and grow the REDCap EDC system at the Wits FHS and its associated academic medical centers. We believe that the increase in the use of REDCap was driven by offering a dependable, secure service with a strong end-user training and support model. This model may be applied by other academic and health care organizations in resource-constrained environments planning to implement EDC technology.
Collapse
Affiliation(s)
- Irma Adele Maré
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa
| | - Beverley Kramer
- School of Anatomical Sciences, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Scott Hazelhurst
- Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical & Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Mapule Dorcus Nhlapho
- Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa.,Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Epidemiology and Biostatistics, School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Roy Zent
- Division of Nephrology and Hypertension, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Paul A Harris
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, United States.,Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States.,Department of Biostatistics, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Michael Klipin
- Department of Surgery, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Biomedical Informatics and Translational Science, Wits Health Consortium, Johannesburg, South Africa
| |
Collapse
|
5
|
Morris SM, Gupta A, Kim S, Foraker RE, Gutmann DH, Payne PRO. Predictive Modeling for Clinical Features Associated With Neurofibromatosis Type 1. Neurol Clin Pract 2022; 11:497-505. [PMID: 34987881 PMCID: PMC8723929 DOI: 10.1212/cpj.0000000000001089] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Accepted: 02/25/2021] [Indexed: 12/23/2022]
Abstract
Objective To perform a longitudinal analysis of clinical features associated with
neurofibromatosis type 1 (NF1) based on demographic and clinical
characteristics and to apply a machine learning strategy to determine
feasibility of developing exploratory predictive models of optic pathway
glioma (OPG) and attention-deficit/hyperactivity disorder (ADHD) in a
pediatric NF1 cohort. Methods Using NF1 as a model system, we perform retrospective data analyses using a
manually curated NF1 clinical registry and electronic health record (EHR)
information and develop machine learning models. Data for 798 individuals
were available, with 578 comprising the pediatric cohort used for
analysis. Results Males and females were evenly represented in the cohort. White children were
more likely to develop OPG (odds ratio [OR]: 2.11, 95% confidence interval
[CI]: 1.11–4.00, p = 0.02) relative to their
non-White peers. Median age at diagnosis of OPG was 6.5 years
(1.7–17.0), irrespective of sex. Males were more likely than females
to have a diagnosis of ADHD (OR: 1.90, 95% CI: 1.33–2.70,
p < 0.001), and earlier diagnosis in males
relative to females was observed. The gradient boosting classification model
predicted diagnosis of ADHD with an area under the receiver operator
characteristic (AUROC) of 0.74 and predicted diagnosis of OPG with an AUROC
of 0.82. Conclusions Using readily available clinical and EHR data, we successfully recapitulated
several important and clinically relevant patterns in NF1 semiology
specifically based on demographic and clinical characteristics. Naive
machine learning techniques can be potentially used to develop and validate
predictive phenotype complexes applicable to risk stratification and disease
management in NF1.
Collapse
Affiliation(s)
- Stephanie M Morris
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Aditi Gupta
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Seunghwan Kim
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Randi E Foraker
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - David H Gutmann
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| | - Philip R O Payne
- Department of Neurology (DHG), Washington University, St. Louis, MO; and Institute for Informatics (SMM, AG, SK, REF, PROP), Washington University, St. Louis, MO
| |
Collapse
|
6
|
Campion TR, Sholle ET, Pathak J, Johnson SB, Leonard JP, Cole CL. An architecture for research computing in health to support clinical and translational investigators with electronic patient data. J Am Med Inform Assoc 2021; 29:677-685. [PMID: 34850911 PMCID: PMC8690260 DOI: 10.1093/jamia/ocab266] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 10/20/2021] [Accepted: 11/15/2021] [Indexed: 12/13/2022] Open
Abstract
Objective Obtaining electronic patient data, especially from electronic health record (EHR) systems, for clinical and translational research is difficult. Multiple research informatics systems exist but navigating the numerous applications can be challenging for scientists. This article describes Architecture for Research Computing in Health (ARCH), our institution’s approach for matching investigators with tools and services for obtaining electronic patient data. Materials and Methods Supporting the spectrum of studies from populations to individuals, ARCH delivers a breadth of scientific functions—including but not limited to cohort discovery, electronic data capture, and multi-institutional data sharing—that manifest in specific systems—such as i2b2, REDCap, and PCORnet. Through a consultative process, ARCH staff align investigators with tools with respect to study design, data sources, and cost. Although most ARCH services are available free of charge, advanced engagements require fee for service. Results Since 2016 at Weill Cornell Medicine, ARCH has supported over 1200 unique investigators through more than 4177 consultations. Notably, ARCH infrastructure enabled critical coronavirus disease 2019 response activities for research and patient care. Discussion ARCH has provided a technical, regulatory, financial, and educational framework to support the biomedical research enterprise with electronic patient data. Collaboration among informaticians, biostatisticians, and clinicians has been critical to rapid generation and analysis of EHR data. Conclusion A suite of tools and services, ARCH helps match investigators with informatics systems to reduce time to science. ARCH has facilitated research at Weill Cornell Medicine and may provide a model for informatics and research leaders to support scientists elsewhere.
Collapse
Affiliation(s)
- Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Department of Pediatrics, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Evan T Sholle
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Jyotishman Pathak
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA
| | - Stephen B Johnson
- Department of Population Health, New York University Grossman School of Medicine, New York, New York, USA
| | - John P Leonard
- Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| | - Curtis L Cole
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA.,Information Technologies & Services Department, Weill Cornell Medicine, New York, New York, USA.,Clinical and Translational Science Center, Weill Cornell Medicine, New York, New York, USA.,Department of Medicine, Weill Cornell Medicine, New York, New York, USA
| |
Collapse
|
7
|
Acosta-López JE, Suárez I, Pineda DA, Cervantes-Henríquez ML, Martínez-Banfi ML, Lozano-Gutiérrez SG, Ahmad M, Pineda-Alhucema W, Noguera-Machacón LM, Hoz MDL, Mejía-Segura E, Jiménez-Figueroa G, Sánchez-Rojas M, Mastronardi CA, Arcos-Burgos M, Vélez JI, Puentes-Rozo PJ. Impulsive and Omission Errors: Potential Temporal Processing Endophenotypes in ADHD. Brain Sci 2021; 11:1218. [PMID: 34573239 PMCID: PMC8467181 DOI: 10.3390/brainsci11091218] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2021] [Revised: 09/01/2021] [Accepted: 09/02/2021] [Indexed: 11/19/2022] Open
Abstract
Temporal processing (TP) is associated with functions such as perception, verbal skills, temporal perspective, and future planning, and is intercorrelated with working memory, attention, and inhibitory control, which are highly impaired in individuals with attention deficit hyperactivity disorder (ADHD). Here we evaluate TP measures as potential endophenotypes in Caribbean families ascertained from probands affected by ADHD. A total of 232 individuals were recruited and clinically evaluated using an extensive battery of neuropsychological tasks and reaction time (RT)-based task paradigms. Further, the heritability (genetic variance underpinning phenotype) was estimated as a measure of the genetics apportionment. A predictive framework for ADHD diagnosis was derived using these tasks. We found that individuals with ADHD differed from controls in neuropsychological tasks assessing mental control, visual-verbal memory, verbal fluency, verbal, and semantic fluency. In addition, TP measures such as RT, errors, and variability were also affected in individuals with ADHD. Moreover, we determined that only omission and commission errors had significant heritability. In conclusion, we have disentangled omission and commission errors as possible TP endophenotypes in ADHD, which can be suitable to assess the neurobiological and genetic basis of ADHD. A predictive model using these endophenotypes led to remarkable sensitivity, specificity, precision and classification rate for ADHD diagnosis, and may be a useful tool for patients' diagnosis, follow-up, and longitudinal assessment in the clinical setting.
Collapse
Affiliation(s)
- Johan E. Acosta-López
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Isabel Suárez
- Universidad del Norte, Barranquilla 081007, Colombia;
| | - David A. Pineda
- Neuropsychology and Conduct Research Group, University of San Buenaventura, Medellín 050010, Colombia;
| | - Martha L. Cervantes-Henríquez
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
- Universidad del Norte, Barranquilla 081007, Colombia;
| | - Martha L. Martínez-Banfi
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Semiramis G. Lozano-Gutiérrez
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Mostapha Ahmad
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Wilmar Pineda-Alhucema
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Luz M. Noguera-Machacón
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Moisés De La Hoz
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Elsy Mejía-Segura
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Giomar Jiménez-Figueroa
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | - Manuel Sánchez-Rojas
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
| | | | - Mauricio Arcos-Burgos
- Grupo de Investigación en Psiquiatría (GIPSI), Departamento de Psiquiatría, Instituto de Investigaciones Médicas, Facultad de Medicina, Universidad de Antioquia, Medellín 050010, Colombia
| | | | - Pedro J. Puentes-Rozo
- Facultad de Ciencias Jurídicas y Sociales, Universidad Simón Bolívar, Barranquilla 080005, Colombia; (M.L.C.-H.); (M.L.M.-B.); (S.G.L.-G.); (M.A.); (W.P.-A.); (L.M.N.-M.); (M.D.L.H.); (E.M.-S.); (G.J.-F.); (M.S.-R.); (P.J.P.-R.)
- Grupo de Neurociencias del Caribe, Universidad del Atlántico, Barranquilla 081007, Colombia
| |
Collapse
|
8
|
He W, Kirchoff KG, Sampson RR, McGhee KK, Cates AM, Obeid JS, Lenert LA. Research Integrated Network of Systems (RINS): a virtual data warehouse for the acceleration of translational research. J Am Med Inform Assoc 2021; 28:1440-1450. [PMID: 33729486 DOI: 10.1093/jamia/ocab023] [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: 10/23/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Integrated, real-time data are crucial to evaluate translational efforts to accelerate innovation into care. Too often, however, needed data are fragmented in disparate systems. The South Carolina Clinical & Translational Research Institute at the Medical University of South Carolina (MUSC) developed and implemented a universal study identifier-the Research Master Identifier (RMID)-for tracking research studies across disparate systems and a data warehouse-inspired model-the Research Integrated Network of Systems (RINS)-for integrating data from those systems. MATERIALS AND METHODS In 2017, MUSC began requiring the use of RMIDs in informatics systems that support human subject studies. We developed a web-based tool to create RMIDs and application programming interfaces to synchronize research records and visualize linkages to protocols across systems. Selected data from these disparate systems were extracted and merged nightly into an enterprise data mart, and performance dashboards were created to monitor key translational processes. RESULTS Within 4 years, 5513 RMIDs were created. Among these were 726 (13%) bridged systems needed to evaluate research study performance, and 982 (18%) linked to the electronic health records, enabling patient-level reporting. DISCUSSION Barriers posed by data fragmentation to assessment of program impact have largely been eliminated at MUSC through the requirement for an RMID, its distribution via RINS to disparate systems, and mapping of system-level data to a single integrated data mart. CONCLUSION By applying data warehousing principles to federate data at the "study" level, the RINS project reduced data fragmentation and promoted research systems integration.
Collapse
Affiliation(s)
- Wenjun He
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA
| | - Katie G Kirchoff
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Royce R Sampson
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Department of Psychiatry & Behavioral Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Kimberly K McGhee
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Academic Affairs Faculty, Medical University of South Carolina, Charleston, SC, USA
| | - Andrew M Cates
- Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA
| | - Jihad S Obeid
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Public Health Sciences, Medical University of South Carolina, Charleston, SC, USA
| | - Leslie A Lenert
- College of Medicine, South Carolina Clinical & Translational Research Institute, Medical University of South Carolina, Charleston, SC, USA.,Biomedical Informatics Center, Medical University of South Carolina, Charleston, SC, USA.,Department of Medicine, Medical University of South Carolina, Charleston, SC, USA
| |
Collapse
|
9
|
Manemann SM, St Sauver JL, Liu H, Larson NB, Moon S, Takahashi PY, Olson JE, Rocca WA, Miller VM, Therneau TM, Ngufor CG, Roger VL, Zhao Y, Decker PA, Killian JM, Bielinski SJ. Longitudinal cohorts for harnessing the electronic health record for disease prediction in a US population. BMJ Open 2021; 11:e044353. [PMID: 34103314 PMCID: PMC8190051 DOI: 10.1136/bmjopen-2020-044353] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022] Open
Abstract
PURPOSE The depth and breadth of clinical data within electronic health record (EHR) systems paired with innovative machine learning methods can be leveraged to identify novel risk factors for complex diseases. However, analysing the EHR is challenging due to complexity and quality of the data. Therefore, we developed large electronic population-based cohorts with comprehensive harmonised and processed EHR data. PARTICIPANTS All individuals 30 years of age or older who resided in Olmsted County, Minnesota on 1 January 2006 were identified for the discovery cohort. Algorithms to define a variety of patient characteristics were developed and validated, thus building a comprehensive risk profile for each patient. Patients are followed for incident diseases and ageing-related outcomes. Using the same methods, an independent validation cohort was assembled by identifying all individuals 30 years of age or older who resided in the largely rural 26-county area of southern Minnesota and western Wisconsin on 1 January 2013. FINDINGS TO DATE For the discovery cohort, 76 255 individuals (median age 49; 53% women) were identified from which a total of 9 644 221 laboratory results; 9 513 840 diagnosis codes; 10 924 291 procedure codes; 1 277 231 outpatient drug prescriptions; 966 136 heart rate measurements and 1 159 836 blood pressure (BP) measurements were retrieved during the baseline time period. The most prevalent conditions in this cohort were hyperlipidaemia, hypertension and arthritis. For the validation cohort, 333 460 individuals (median age 54; 52% women) were identified and to date, a total of 19 926 750 diagnosis codes, 10 527 444 heart rate measurements and 7 356 344 BP measurements were retrieved during baseline. FUTURE PLANS Using advanced machine learning approaches, these electronic cohorts will be used to identify novel sex-specific risk factors for complex diseases. These approaches will allow us to address several challenges with the use of EHR.
Collapse
Affiliation(s)
- Sheila M Manemann
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Jennifer L St Sauver
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Hongfang Liu
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Nicholas B Larson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Sungrim Moon
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul Y Takahashi
- Division of Community Internal Medicine, Department of Medicine, Mayo Clinic, Rochester, Minnesota, USA
| | - Janet E Olson
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Walter A Rocca
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
- Department of Neurology, Mayo Clinic, Rochester, Minnesota, USA, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Women's Health Research Center, Mayo Clinic, Rochester, Minnesota, USA
| | - Virginia M Miller
- Mayo Clinic Women's Health Research Center, Mayo Clinic, Rochester, Minnesota, USA
- Department of Physiology and Biomedical Engineering, Mayo Clinic, Rochester, Minnesota, USA
- Department of Surgery, Mayo Clinic, Rochester, Minnesota, USA
- Mayo Clinic Specialized Center of Research Excellence, Mayo Clinic Rochester, Minnesota, USA, Mayo Clinic, Rochester, Minnesota, USA
| | - Terry M Therneau
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Che G Ngufor
- Department of Artificial Intelligence and Informatics, Mayo Clinic, Rochester, Minnesota, USA
| | - Veronique L Roger
- Division of Cardiovascular Diseases, Department of Internal Medicine, Mayo Clinic, Rochester, Minnesota, USA
- Epidemiology and Community Health Branch National Heart, Lung and Blood Institute, National Institutes of Health, Bethesda, Maryland, USA
| | - Yiqing Zhao
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Paul A Decker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Jill M Killian
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| | - Suzette J Bielinski
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, Minnesota, USA
| |
Collapse
|
10
|
Campion TR, Craven CK, Dorr DA, Knosp BM. Understanding enterprise data warehouses to support clinical and translational research. J Am Med Inform Assoc 2020; 27:1352-1358. [PMID: 32679585 PMCID: PMC7647350 DOI: 10.1093/jamia/ocaa089] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Revised: 04/24/2020] [Accepted: 05/12/2020] [Indexed: 11/13/2022] Open
Abstract
OBJECTIVE Among National Institutes of Health Clinical and Translational Science Award (CTSA) hubs, adoption of electronic data warehouses for research (EDW4R) containing data from electronic health record systems is nearly ubiquitous. Although benefits of EDW4R include more effective, efficient support of scientists, little is known about how CTSA hubs have implemented EDW4R services. The goal of this qualitative study was to understand the ways in which CTSA hubs have operationalized EDW4R to support clinical and translational researchers. MATERIALS AND METHODS After conducting semistructured interviews with informatics leaders from 20 CTSA hubs, we performed a directed content analysis of interview notes informed by naturalistic inquiry. RESULTS We identified 12 themes: organization and data; oversight and governance; data access request process; data access modalities; data access for users with different skill sets; engagement, communication, and literacy; service management coordinated with enterprise information technology; service management coordinated within a CTSA hub; service management coordinated between informatics and biostatistics; funding approaches; performance metrics; and future trends and current technology challenges. DISCUSSION This study is a step in developing an improved understanding and creating a common vocabulary about EDW4R operations across institutions. Findings indicate an opportunity for establishing best practices for EDW4R operations in academic medicine. Such guidance could reduce the costs associated with developing an EDW4R by establishing a clear roadmap and maturity path for institutions to follow. CONCLUSIONS CTSA hubs described varying approaches to EDW4R operations that may assist other institutions in better serving investigators with electronic patient data.
Collapse
Affiliation(s)
- Thomas R Campion
- Department of Population Health Sciences, Weill Cornell Medicine, New York, New York, USA
| | - Catherine K Craven
- Institute for Health Care Delivery Science, Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - David A Dorr
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, Oregon, USA
| | - Boyd M Knosp
- Institute for Clinical and Translational Science, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, Iowa, USA
| |
Collapse
|
11
|
Hulsen T. The ten commandments of translational research informatics. DATA SCIENCE 2019. [DOI: 10.3233/ds-190020] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Affiliation(s)
- Tim Hulsen
- Department of Professional Health Solutions & Services, Philips Research, Eindhoven, The Netherlands. E-mail:
| |
Collapse
|
12
|
Miller JB. Big data and biomedical informatics: Preparing for the modernization of clinical neuropsychology. Clin Neuropsychol 2018; 33:287-304. [PMID: 30513257 DOI: 10.1080/13854046.2018.1523466] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/27/2022]
Abstract
OBJECTIVE Neuropsychology is poised for a fundamental shift as we modernize the ways in which behavior is measured. The amount and complexity of data generated by these new methods will be several orders of magnitude greater than what is currently created by analog measures and will quickly adopt characteristics of "Big Data." Adequate preparation for managing the influx of data will be critical for technology integration and modernization to be successful. Drawing from information technology, mathematics, statistics, computer science, and engineering, as well as, biology, genetics, and medicine, the field of biomedical informatics has rapidly evolved from its early days in computational biology to a burgeoning independent discipline that has much to offer neuropsychology. METHOD Following a critical review of the relevant literature, the present article (1) provides an introductory overview of biomedical informatics and how these concepts are relevant to neuropsychology; (2) describes how biomedical informatics applications can be utilized to leverage existing data sources more effectively; and (3) discusses ideas for future developments designed to facilitate integration of new data derived from novel, technologically driven measurement tools. Within this context, applications intended for use by both the individual neuropsychologist to increase clinical efficiencies, as well as, larger field-wide initiatives intended to generate new information and derive new knowledge are discussed. CONCLUSIONS By no means a comprehensive review of biomedical informatics, the present paper highlights that our approach to data needs to become a multidisciplinary endeavor in order to develop applications capable of effectively utilizing modern data sources.
Collapse
Affiliation(s)
- Justin B Miller
- a Cleveland Clinic Lou Ruvo Center for Brain Health , Las Vegas , Nevada , USA
| |
Collapse
|
13
|
Johnson SB. Clinical Research Informatics: Supporting the Research Study Lifecycle. Yearb Med Inform 2017; 26:193-200. [PMID: 29063565 PMCID: PMC6239240 DOI: 10.15265/iy-2017-022] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/18/2017] [Indexed: 12/27/2022] Open
Abstract
Objectives: The primary goal of this review is to summarize significant developments in the field of Clinical Research Informatics (CRI) over the years 2015-2016. The secondary goal is to contribute to a deeper understanding of CRI as a field, through the development of a strategy for searching and classifying CRI publications. Methods: A search strategy was developed to query the PubMed database, using medical subject headings to both select and exclude articles, and filtering publications by date and other characteristics. A manual review classified publications using stages in the "research study lifecycle", with key stages that include study definition, participant enrollment, data management, data analysis, and results dissemination. Results: The search strategy generated 510 publications. The manual classification identified 125 publications as relevant to CRI, which were classified into seven different stages of the research lifecycle, and one additional class that pertained to multiple stages, referring to general infrastructure or standards. Important cross-cutting themes included new applications of electronic media (Internet, social media, mobile devices), standardization of data and procedures, and increased automation through the use of data mining and big data methods. Conclusions: The review revealed increased interest and support for CRI in large-scale projects across institutions, regionally, nationally, and internationally. A search strategy based on medical subject headings can find many relevant papers, but a large number of non-relevant papers need to be detected using text words which pertain to closely related fields such as computational statistics and clinical informatics. The research lifecycle was useful as a classification scheme by highlighting the relevance to the users of clinical research informatics solutions.
Collapse
Affiliation(s)
- S. B. Johnson
- Healthcare Policy and Research, Weill Cornell Medicine, New York, USA
| |
Collapse
|
14
|
Moreira A, Alonso-Calvo R, Muñoz A, Crespo J. Enhancing Collaborative Case Diagnoses Through Unified Medical Language System-Based Disambiguation: A Case Study of the Zika Virus. Telemed J E Health 2017; 23:608-614. [DOI: 10.1089/tmj.2016.0203] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Affiliation(s)
- Albert Moreira
- Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Raúl Alonso-Calvo
- Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain
| | - Alberto Muñoz
- Faculty of Medicine, Universidad Complutense de Madrid, Madrid, Spain
| | - José Crespo
- Biomedical Informatics Group, DLSIIS & DIA, ETSIINF, Universidad Politécnica de Madrid, Madrid, Spain
| |
Collapse
|
15
|
Fudge N, Sadler E, Fisher HR, Maher J, Wolfe CDA, McKevitt C. Optimising Translational Research Opportunities: A Systematic Review and Narrative Synthesis of Basic and Clinician Scientists' Perspectives of Factors Which Enable or Hinder Translational Research. PLoS One 2016; 11:e0160475. [PMID: 27490373 PMCID: PMC4973909 DOI: 10.1371/journal.pone.0160475] [Citation(s) in RCA: 51] [Impact Index Per Article: 6.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2015] [Accepted: 07/20/2016] [Indexed: 01/07/2023] Open
Abstract
Introduction Translational research is central to international health policy, research and funding initiatives. Despite increasing use of the term, the translation of basic science discoveries into clinical practice is not straightforward. This systematic search and narrative synthesis aimed to examine factors enabling or hindering translational research from the perspective of basic and clinician scientists, a key stakeholder group in translational research, and to draw policy-relevant implications for organisations seeking to optimise translational research opportunities. Methods and Results We searched SCOPUS and Web of Science from inception until April 2015 for papers reporting scientists’ views of the factors they perceive as enabling or hindering the conduct of translational research. We screened 8,295 papers from electronic database searches and 20 papers from hand searches and citation tracking, identifying 26 studies of qualitative, quantitative or mixed method designs. We used a narrative synthesis approach and identified the following themes: 1) differing concepts of translational research 2) research processes as a barrier to translational research; 3) perceived cultural divide between research and clinical care; 4) interdisciplinary collaboration as enabling translation research, but dependent on the quality of prior and current social relationships; 5) translational research as entrepreneurial science. Across all five themes, factors enabling or hindering translational research were largely shaped by wider social, organisational, and structural factors. Conclusion To optimise translational research, policy could consider refining translational research models to better reflect scientists’ experiences, fostering greater collaboration and buy in from all types of scientists. Organisations could foster cultural change, ensuring that organisational practices and systems keep pace with the change in knowledge production brought about by the translational research agenda.
Collapse
Affiliation(s)
- Nina Fudge
- Division of Health and Social Care Research, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.,National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
| | - Euan Sadler
- Centre for Implementation Science, Department of Health Service and Population Research, Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, United Kingdom
| | - Helen R Fisher
- Division of Health and Social Care Research, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.,National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
| | - John Maher
- Department of Research Oncology, King's College London and Guy's Hospital, London, United Kingdom.,Department of Clinical Immunology and Allergy, King's College Hospital, London, United Kingdom
| | - Charles D A Wolfe
- Division of Health and Social Care Research, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.,National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
| | - Christopher McKevitt
- Division of Health and Social Care Research, Faculty of Life Science and Medicine, King's College London, London, United Kingdom.,National Institute for Health Research (NIHR) Comprehensive Biomedical Research Centre, Guy's and St Thomas' NHS Foundation Trust and King's College London, London, United Kingdom
| |
Collapse
|
16
|
Arsoniadis EG, Melton GB. Leveraging the electronic health record for research and quality improvement: Current strengths and future challenges. SEMINARS IN COLON AND RECTAL SURGERY 2016. [DOI: 10.1053/j.scrs.2016.01.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
|
17
|
Johnson SB, Farach FJ, Pelphrey K, Rozenblit L. Data management in clinical research: Synthesizing stakeholder perspectives. J Biomed Inform 2016; 60:286-93. [PMID: 26925516 DOI: 10.1016/j.jbi.2016.02.014] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2015] [Revised: 02/17/2016] [Accepted: 02/22/2016] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This study assesses data management needs in clinical research from the perspectives of researchers, software analysts and developers. MATERIALS AND METHODS This is a mixed-methods study that employs sublanguage analysis in an innovative manner to link the assessments. We performed content analysis using sublanguage theory on transcribed interviews conducted with researchers at four universities. A business analyst independently extracted potential software features from the transcriptions, which were translated into the sublanguage. This common sublanguage was then used to create survey questions for researchers, analysts and developers about the desirability and difficulty of features. Results were synthesized using the common sublanguage to compare stakeholder perceptions with the original content analysis. RESULTS Individual researchers exhibited significant diversity of perspectives that did not correlate by role or site. Researchers had mixed feelings about their technologies, and sought improvements in integration, interoperability and interaction as well as engaging with study participants. Researchers and analysts agreed that data integration has higher desirability and mobile technology has lower desirability but disagreed on the desirability of data validation rules. Developers agreed that data integration and validation are the most difficult to implement. DISCUSSION Researchers perceive tasks related to study execution, analysis and quality control as highly strategic, in contrast with tactical tasks related to data manipulation. Researchers have only partial technologic support for analysis and quality control, and poor support for study execution. CONCLUSION Software for data integration and validation appears critical to support clinical research, but may be expensive to implement. Features to support study workflow, collaboration and engagement have been underappreciated, but may prove to be easy successes. Software developers should consider the strategic goals of researchers with regard to the overall coordination of research projects and teams, workflow connecting data collection with analysis and processes for improving data quality.
Collapse
Affiliation(s)
- Stephen B Johnson
- Division of Health Informatics, Weill Cornell Medical College, 425 East 61st Street, DV-317, New York, NY 10065, United States.
| | | | | | | |
Collapse
|
18
|
Mo H, Thompson WK, Rasmussen LV, Pacheco JA, Jiang G, Kiefer R, Zhu Q, Xu J, Montague E, Carrell DS, Lingren T, Mentch FD, Ni Y, Wehbe FH, Peissig PL, Tromp G, Larson EB, Chute CG, Pathak J, Denny JC, Speltz P, Kho AN, Jarvik GP, Bejan CA, Williams MS, Borthwick K, Kitchner TE, Roden DM, Harris PA. Desiderata for computable representations of electronic health records-driven phenotype algorithms. J Am Med Inform Assoc 2015; 22:1220-30. [PMID: 26342218 PMCID: PMC4639716 DOI: 10.1093/jamia/ocv112] [Citation(s) in RCA: 80] [Impact Index Per Article: 8.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2015] [Accepted: 06/24/2015] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Electronic health records (EHRs) are increasingly used for clinical and translational research through the creation of phenotype algorithms. Currently, phenotype algorithms are most commonly represented as noncomputable descriptive documents and knowledge artifacts that detail the protocols for querying diagnoses, symptoms, procedures, medications, and/or text-driven medical concepts, and are primarily meant for human comprehension. We present desiderata for developing a computable phenotype representation model (PheRM). METHODS A team of clinicians and informaticians reviewed common features for multisite phenotype algorithms published in PheKB.org and existing phenotype representation platforms. We also evaluated well-known diagnostic criteria and clinical decision-making guidelines to encompass a broader category of algorithms. RESULTS We propose 10 desired characteristics for a flexible, computable PheRM: (1) structure clinical data into queryable forms; (2) recommend use of a common data model, but also support customization for the variability and availability of EHR data among sites; (3) support both human-readable and computable representations of phenotype algorithms; (4) implement set operations and relational algebra for modeling phenotype algorithms; (5) represent phenotype criteria with structured rules; (6) support defining temporal relations between events; (7) use standardized terminologies and ontologies, and facilitate reuse of value sets; (8) define representations for text searching and natural language processing; (9) provide interfaces for external software algorithms; and (10) maintain backward compatibility. CONCLUSION A computable PheRM is needed for true phenotype portability and reliability across different EHR products and healthcare systems. These desiderata are a guide to inform the establishment and evolution of EHR phenotype algorithm authoring platforms and languages.
Collapse
Affiliation(s)
- Huan Mo
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - William K Thompson
- Center for Biomedical Research Informatics, NorthShore University HealthSystem, Evanston, IL, USA
| | - Luke V Rasmussen
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Guoqian Jiang
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Richard Kiefer
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA
| | - Qian Zhu
- Department of Information Systems, University of Maryland, Baltimore County, Baltimore, MD, USA
| | - Jie Xu
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Enid Montague
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | | | - Todd Lingren
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Frank D Mentch
- Center for Applied Genomics, the Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Yizhao Ni
- Division of Biomedical Informatics, Cincinnati Children's Hospital, Cincinnati, OH, USA
| | - Firas H Wehbe
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Peggy L Peissig
- Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA
| | - Gerard Tromp
- Division of Molecular Biology and Human Genetics, Department of Biomedical Sciences, Faculty of Medicine and Health Sciences, University of Stellenbosch, Cape Town, South Africa
| | | | - Christopher G Chute
- Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Jyotishman Pathak
- Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA Center for the Science of Health Care Delivery, Mayo Clinic, Rochester, MN, USA
| | - Joshua C Denny
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA Department of Medicine, Vanderbilt University, Nashville, TN, USA
| | - Peter Speltz
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Abel N Kho
- Department of Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Gail P Jarvik
- Department of Medicine (Medical Genetics), University of Washington, Seattle, WA, USA Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Cosmin A Bejan
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| | - Marc S Williams
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Kenneth Borthwick
- The Sigfried and Janet Weis Center for Research, Geisinger Health System, Danville, PA, USA
| | - Terrie E Kitchner
- Marshfield Clinic Research Foundation, Marshfield Clinic, Marshfield, WI, USA
| | - Dan M Roden
- Department of Medicine, Vanderbilt University, Nashville, TN, USA Department of Pharmacology, Vanderbilt University, Nashville, TN, USA
| | - Paul A Harris
- Department of Biomedical Informatics, Vanderbilt University, Nashville, TN, USA
| |
Collapse
|
19
|
Embi PJ, Payne PRO. Advancing methodologies in Clinical Research Informatics (CRI): foundational work for a maturing field. J Biomed Inform 2015; 52:1-3. [PMID: 25484113 DOI: 10.1016/j.jbi.2014.10.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2014] [Revised: 10/15/2014] [Accepted: 10/18/2014] [Indexed: 10/24/2022]
Affiliation(s)
- Peter J Embi
- 250 Lincoln Tower, 1800 Canon Drive, The Ohio State University, Columbus, OH 43210, USA.
| | - Philip R O Payne
- 250 Lincoln Tower, 1800 Canon Drive, The Ohio State University, Columbus, OH 43210, USA.
| |
Collapse
|
20
|
Linkage of Data from Diverse Data Sources (LDS): A Data Combination Model Provides Clinical Data of Corresponding Specimens in Biobanking Information System. J Med Syst 2013; 37:9975. [DOI: 10.1007/s10916-013-9975-y] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2013] [Accepted: 08/29/2013] [Indexed: 11/26/2022]
|
21
|
Kukafka R, Allegrante JP, Khan S, Bigger JT, Johnson SB. Understanding facilitators and barriers to reengineering the clinical research enterprise in community-based practice settings. Contemp Clin Trials 2013; 36:166-74. [DOI: 10.1016/j.cct.2013.06.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2013] [Revised: 06/12/2013] [Accepted: 06/16/2013] [Indexed: 11/16/2022]
|
22
|
Payne PRO. Advancing user experience research to facilitate and enable patient-centered research: current state and future directions. EGEMS (WASHINGTON, DC) 2013; 1:1026. [PMID: 25848566 PMCID: PMC4371428 DOI: 10.13063/2327-9214.1026] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/24/2022]
Abstract
Human-computer interaction and related areas of user experience (UX) research, such as human factors, workflow evaluation, and data visualization, are thus essential to presenting data in ways that can further the analysis of complex data sets such as those used in patient-centered research. However, a review of available data on the state of UX research as it relates to patient-centered research demonstrates a significant underinvestment and consequently a large gap in knowledge generation. In response, this report explores trends in funding and research productivity focused on UX and patient-centered research and then presents a set of recommendations to advance innovation at this important intersection point. Ultimately, the aim is to catalyze a community-wide dialogue concerning future directions for research and innovation in UX as it applies to patient-centered research.
Collapse
|
23
|
Payne PRO, Pressler TR, Sarkar IN, Lussier Y. People, organizational, and leadership factors impacting informatics support for clinical and translational research. BMC Med Inform Decis Mak 2013; 13:20. [PMID: 23388243 PMCID: PMC3577661 DOI: 10.1186/1472-6947-13-20] [Citation(s) in RCA: 9] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2012] [Accepted: 01/14/2013] [Indexed: 11/29/2022] Open
Abstract
BACKGROUND In recent years, there have been numerous initiatives undertaken to describe critical information needs related to the collection, management, analysis, and dissemination of data in support of biomedical research (J Investig Med 54:327-333, 2006); (J Am Med Inform Assoc 16:316-327, 2009); (Physiol Genomics 39:131-140, 2009); (J Am Med Inform Assoc 18:354-357, 2011). A common theme spanning such reports has been the importance of understanding and optimizing people, organizational, and leadership factors in order to achieve the promise of efficient and timely research (J Am Med Inform Assoc 15:283-289, 2008). With the emergence of clinical and translational science (CTS) as a national priority in the United States, and the corresponding growth in the scale and scope of CTS research programs, the acuity of such information needs continues to increase (JAMA 289:1278-1287, 2003); (N Engl J Med 353:1621-1623, 2005); (Sci Transl Med 3:90, 2011). At the same time, systematic evaluations of optimal people, organizational, and leadership factors that influence the provision of data, information, and knowledge management technologies and methods are notably lacking. METHODS In response to the preceding gap in knowledge, we have conducted both: 1) a structured survey of domain experts at Academic Health Centers (AHCs); and 2) a subsequent thematic analysis of public-domain documentation provided by those same organizations. The results of these approaches were then used to identify critical factors that may influence access to informatics expertise and resources relevant to the CTS domain. RESULTS A total of 31 domain experts, spanning the Biomedical Informatics (BMI), Computer Science (CS), Information Science (IS), and Information Technology (IT) disciplines participated in a structured surveyprocess. At a high level, respondents identified notable differences in theaccess to BMI, CS, and IT expertise and services depending on the establishment of a formal BMI academic unit and the perceived relationship between BMI, CS, IS, and IT leaders. Subsequent thematic analysis of the aforementioned public domain documents demonstrated a discordance between perceived and reported integration across and between BMI, CS, IS, and IT programs and leaders with relevance to the CTS domain. CONCLUSION Differences in people, organization, and leadership factors do influence the effectiveness of CTS programs, particularly with regard to the ability to access and leverage BMI, CS, IS, and IT expertise and resources. Based on this finding, we believe that the development of a better understanding of how optimal BMI, CS, IS, and IT organizational structures and leadership models are designed and implemented is critical to both the advancement of CTS and ultimately, to improvements in the quality, safety, and effectiveness of healthcare.
Collapse
Affiliation(s)
- Philip RO Payne
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Taylor R Pressler
- Department of Biomedical Informatics, The Ohio State University, Columbus, OH, USA
| | - Indra Neil Sarkar
- Department of Computer Science, Department of Microbiology and Molecular Genetics, University of Vermont, Burlington, VT, USA
| | - Yves Lussier
- Department of Medicine and Engineering, University of Chicago, Chicago, IL, USA
| |
Collapse
|
24
|
Abstract
The modern biomedical research and healthcare delivery domains have seen an unparalleled increase in the rate of innovation and novel technologies over the past several decades. Catalyzed by paradigm-shifting public and private programs focusing upon the formation and delivery of genomic and personalized medicine, the need for high-throughput and integrative approaches to the collection, management, and analysis of heterogeneous data sets has become imperative. This need is particularly pressing in the translational bioinformatics domain, where many fundamental research questions require the integration of large scale, multi-dimensional clinical phenotype and bio-molecular data sets. Modern biomedical informatics theory and practice has demonstrated the distinct benefits associated with the use of knowledge-based systems in such contexts. A knowledge-based system can be defined as an intelligent agent that employs a computationally tractable knowledge base or repository in order to reason upon data in a targeted domain and reproduce expert performance relative to such reasoning operations. The ultimate goal of the design and use of such agents is to increase the reproducibility, scalability, and accessibility of complex reasoning tasks. Examples of the application of knowledge-based systems in biomedicine span a broad spectrum, from the execution of clinical decision support, to epidemiologic surveillance of public data sets for the purposes of detecting emerging infectious diseases, to the discovery of novel hypotheses in large-scale research data sets. In this chapter, we will review the basic theoretical frameworks that define core knowledge types and reasoning operations with particular emphasis on the applicability of such conceptual models within the biomedical domain, and then go on to introduce a number of prototypical data integration requirements and patterns relevant to the conduct of translational bioinformatics that can be addressed via the design and use of knowledge-based systems.
Collapse
Affiliation(s)
- Philip R O Payne
- The Ohio State University, Department of Biomedical Informatics, Columbus, Ohio, United States of America.
| |
Collapse
|
25
|
Payne PRO, Marsh CB. Towards a "4I" approach to personalized healthcare. Clin Transl Med 2012; 1:14. [PMID: 23369359 PMCID: PMC3560982 DOI: 10.1186/2001-1326-1-14] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2012] [Accepted: 07/25/2012] [Indexed: 11/13/2022] Open
Abstract
Personalized healthcare holds the promise of ensuring that every patient receives optimal wellness promotion and clinical care based upon his or her unique and multi-factorial phenotype, informed by the most up-to-date and contextually relevant science. However, achieving this vision requires the management, analysis, and delivery of complex data, information, and knowledge. While there are well-established frameworks that serve to inform the pursuit of basic science, clinical, and translational research in support of the operationalization of the personalized healthcare paradigm, equivalent constructs that may enable biomedical informatics innovation and practice aligned with such objectives are noticeably sparse. In response to this gap in knowledge, we propose such a framework for the advancement of biomedical informatics in order to address the fundamental information needs of the personalized healthcare domain. This framework, which we refer to as a “4I” approach, emphasizes the pursuit of research and practice that is information-centric, integrative, interactive, and innovative.
Collapse
Affiliation(s)
- Philip R O Payne
- The Ohio State University Wexner Medical Center, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH, 43210, USA.
| | | |
Collapse
|
26
|
Payne PR, Embi PJ, Kahn MG. Selected Papers from the 2011 Summit on Clinical Research Informatics. J Biomed Inform 2011; 44 Suppl 1:S54-S55. [DOI: 10.1016/j.jbi.2011.11.009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2011] [Revised: 11/21/2011] [Accepted: 11/21/2011] [Indexed: 12/01/2022]
|
27
|
Payne PRO, Borlawsky TB, Lele O, James S, Greaves AW. The TOKEn project: knowledge synthesis for in silico science. J Am Med Inform Assoc 2011; 18 Suppl 1:i125-31. [PMID: 21984589 DOI: 10.1136/amiajnl-2011-000434] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
OBJECTIVE The conduct of investigational studies that involve large-scale data sets presents significant challenges related to the discovery and testing of novel hypotheses capable of supporting in silico discovery science. The use of what are known as Conceptual Knowledge Discovery in Databases (CKDD) methods provides a potential means of scaling hypothesis discovery and testing approaches for large data sets. Such methods enable the high-throughput generation and evaluation of knowledge-anchored relationships between complexes of variables found in targeted data sets. METHODS The authors have conducted a multipart model formulation and validation process, focusing on the development of a methodological and technical approach to using CKDD to support hypothesis discovery for in silico science. The model the authors have developed is known as the Translational Ontology-anchored Knowledge Discovery Engine (TOKEn). This model utilizes a specific CKDD approach known as Constructive Induction to identify and prioritize potential hypotheses related to the meaningful semantic relationships between variables found in large-scale and heterogeneous biomedical data sets. RESULTS The authors have verified and validated TOKEn in the context of a translational research data repository maintained by the NCI-funded Chronic Lymphocytic Leukemia Research Consortium. Such studies have shown that TOKEn is: (1) computationally tractable; and (2) able to generate valid and potentially useful hypotheses concerning relationships between phenotypic and biomolecular variables in that data collection. CONCLUSIONS The TOKEn model represents a potentially useful and systematic approach to knowledge synthesis for in silico discovery science in the context of large-scale and multidimensional research data sets.
Collapse
Affiliation(s)
- Philip R O Payne
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
| | | | | | | | | |
Collapse
|
28
|
Borlawsky TB, Lele O, Jensen D, Hood NE, Wewers ME. Enabling distributed electronic research data collection for a rural Appalachian tobacco cessation study. J Am Med Inform Assoc 2011; 18 Suppl 1:i140-3. [PMID: 21849332 DOI: 10.1136/amiajnl-2011-000354] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/04/2022] Open
Abstract
Tobacco use is increasingly prevalent among vulnerable populations, such as people living in rural Appalachian communities. Owing to limited access to a reliable internet service in such settings, there is no widespread adoption of electronic data capture tools for conducting community-based research. By integrating the REDCap data collection application with a custom synchronization tool, the authors have enabled a workflow in which field research staff located throughout the Ohio Appalachian region can electronically collect and share research data. In addition to allowing the study data to be exchanged in near-real-time among the geographically distributed study staff and centralized study coordinator, the system architecture also ensures that the data are stored securely on encrypted laptops in the field and centrally behind the Ohio State University Medical Center enterprise firewall. The authors believe that this approach can be easily applied to other analogous study designs and settings.
Collapse
Affiliation(s)
- Tara B Borlawsky
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
| | | | | | | | | |
Collapse
|
29
|
Payne P, Ervin D, Dhaval R, Borlawsky T, Lai A. TRIAD: The Translational Research Informatics and Data Management Grid. Appl Clin Inform 2011; 2:331-44. [PMID: 23616879 DOI: 10.4338/aci-2011-02-ra-0014] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2011] [Accepted: 06/15/2011] [Indexed: 11/23/2022] Open
Abstract
OBJECTIVE Multi-disciplinary and multi-site biomedical research programs frequently require infrastructures capable of enabling the collection, management, analysis, and dissemination of heterogeneous, multi-dimensional, and distributed data and knowledge collections spanning organizational boundaries. We report on the design and initial deployment of an extensible biomedical informatics platform that is intended to address such requirements. METHODS A common approach to distributed data, information, and knowledge management needs in the healthcare and life science settings is the deployment and use of a service-oriented architecture (SOA). Such SOA technologies provide for strongly-typed, semantically annotated, and stateful data and analytical services that can be combined into data and knowledge integration and analysis "pipelines." Using this overall design pattern, we have implemented and evaluated an extensible SOA platform for clinical and translational science applications known as the Translational Research Informatics and Data-management grid (TRIAD). TRIAD is a derivative and extension of the caGrid middleware and has an emphasis on supporting agile "working interoperability" between data, information, and knowledge resources. RESULTS Based upon initial verification and validation studies conducted in the context of a collection of driving clinical and translational research problems, we have been able to demonstrate that TRIAD achieves agile "working interoperability" between distributed data and knowledge sources. CONCLUSION Informed by our initial verification and validation studies, we believe TRIAD provides an example instance of a lightweight and readily adoptable approach to the use of SOA technologies in the clinical and translational research setting. Furthermore, our initial use cases illustrate the importance and efficacy of enabling "working interoperability" in heterogeneous biomedical environments.
Collapse
Affiliation(s)
- P Payne
- The Ohio State University, Department of Biomedical Informatics, Center for IT Innovations in Healthcare, and Center for Clinical and Translational Science , Columbus, OH
| | | | | | | | | |
Collapse
|
30
|
Borlawsky TB, Lele O, Payne PRO. Research-IQ: development and evaluation of an ontology-anchored integrative query tool. J Biomed Inform 2011; 44 Suppl 1:S56-S62. [PMID: 21821150 DOI: 10.1016/j.jbi.2011.07.006] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2011] [Revised: 07/19/2011] [Accepted: 07/22/2011] [Indexed: 10/17/2022]
Abstract
Investigators in the translational research and systems medicine domains require highly usable, efficient and integrative tools and methods that allow for the navigation of and reasoning over emerging large-scale data sets. Such resources must cover a spectrum of granularity from bio-molecules to population phenotypes. Given such information needs, we report upon the initial design and evaluation of an ontology-anchored integrative query tool, Research-IQ, which employs a combination of conceptual knowledge engineering and information retrieval techniques to enable the intuitive and rapid construction of queries, in terms of semi-structured textual propositions, that can subsequently be applied to integrative data sets. Our initial results, based upon both quantitative and qualitative evaluations of the efficacy and usability of Research-IQ, demonstrate its potential to increase clinical and translational research throughput.
Collapse
Affiliation(s)
- Tara B Borlawsky
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
| | - Omkar Lele
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
| | - Philip R O Payne
- The Ohio State University, Department of Biomedical Informatics, 3190 Graves Hall, 333 West 10th Avenue, Columbus, OH 43210, USA.
| |
Collapse
|
31
|
Sarkar IN. Biomedical informatics and translational medicine. J Transl Med 2010; 8:22. [PMID: 20187952 PMCID: PMC2837642 DOI: 10.1186/1479-5876-8-22] [Citation(s) in RCA: 73] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2009] [Accepted: 02/26/2010] [Indexed: 11/23/2022] Open
Abstract
Biomedical informatics involves a core set of methodologies that can provide a foundation for crossing the "translational barriers" associated with translational medicine. To this end, the fundamental aspects of biomedical informatics (e.g., bioinformatics, imaging informatics, clinical informatics, and public health informatics) may be essential in helping improve the ability to bring basic research findings to the bedside, evaluate the efficacy of interventions across communities, and enable the assessment of the eventual impact of translational medicine innovations on health policies. Here, a brief description is provided for a selection of key biomedical informatics topics (Decision Support, Natural Language Processing, Standards, Information Retrieval, and Electronic Health Records) and their relevance to translational medicine. Based on contributions and advancements in each of these topic areas, the article proposes that biomedical informatics practitioners ("biomedical informaticians") can be essential members of translational medicine teams.
Collapse
Affiliation(s)
- Indra Neil Sarkar
- Center for Clinical and Translational Science, Department of Microbiology and Molecular Genetics, University of Vermont, College of Medicine, 89 Beaumont Ave, Given Courtyard N309, Burlington, VT 05405, USA.
| |
Collapse
|
32
|
Affiliation(s)
- Peter J Embi
- Center for Health Informatics, University of Cincinnati Academic Health Center, 231 Albert Sabin Way, Cincinnati, OH 45267-0840, USA.
| | | | | |
Collapse
|
33
|
Validating pathophysiological models of aging using clinical electronic medical records. J Biomed Inform 2009; 43:358-64. [PMID: 19958842 DOI: 10.1016/j.jbi.2009.11.007] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2009] [Revised: 11/24/2009] [Accepted: 11/24/2009] [Indexed: 01/31/2023]
Abstract
Bioinformatics methods that leverage the vast amounts of clinical data promises to provide insights into underlying molecular mechanisms that help explain human physiological processes. One of these processes is adolescent development. The utility of predictive aging models generated from cross-sectional cohorts and their applicability to separate populations, including the clinical population, has yet to be completely explored. In order to address this, we built regression models predictive of adolescent chronological age from 2001 to 2002 National Health and Nutrition Examination Survey (NHANES) data and validated them against independent 2003-2004 NHANES data and clinical data from an academic tertiary-care pediatric hospital. The results indicate distinct differences between male and female models with both alkaline phosphatase and creatinine as predictive biomarkers for both genders, hematocrit and mean cell volume for males, and total serum globulin for females. We also suggest that the models are generalizable, are clinically relevant, and imply underlying molecular and clinical differences between males and females that may affect prediction accuracy. The integration of both epidemiological and clinical data promises to create more robust models that shed new light on physiological processes.
Collapse
|
34
|
Grossman AD, Cohen MJ, Manley GT, Butte AJ. Infection in the intensive care unit alters physiological networks. BMC Bioinformatics 2009; 10 Suppl 9:S4. [PMID: 19761574 PMCID: PMC2745691 DOI: 10.1186/1471-2105-10-s9-s4] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
BACKGROUND Physicians use clinical and physiological data to treat patients every day, and it is essential for treating a patient appropriately. However, medical sources of clinical physiological data are only now starting to find use in bioinformatics research. RESULTS We collected 29 types of physiological and clinical data on a minute-by-minute basis from trauma patients in the intensive care unit along with whether they contracted an infection during their stay. Dividing the patients into two groups based on this criterion, we determined that the correlational network amongst pairs of physiological variables changes based on whether the patient contracted an infection. CONCLUSION Examining the variable pairs with the largest change in correlation across groups reveals potential changes in the way our treatments affect the patient's physiology and in how our bodies react to physiological insults. These findings highlight the usefulness of physiological informatics and suggest new relationships to study while also validating previously reported relationships.
Collapse
Affiliation(s)
- Adam D Grossman
- Department of Bioengineering, Stanford University, Stanford, California 94305, USA.
| | | | | | | |
Collapse
|
35
|
Payne PRO, Embi PJ, Sen CK. Translational informatics: enabling high-throughput research paradigms. Physiol Genomics 2009; 39:131-40. [PMID: 19737991 DOI: 10.1152/physiolgenomics.00050.2009] [Citation(s) in RCA: 25] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022] Open
Abstract
A common thread throughout the clinical and translational research domains is the need to collect, manage, integrate, analyze, and disseminate large-scale, heterogeneous biomedical data sets. However, well-established and broadly adopted theoretical and practical frameworks and models intended to address such needs are conspicuously absent in the published literature or other reputable knowledge sources. Instead, the development and execution of multidisciplinary, clinical, or translational studies are significantly limited by the propagation of "silos" of both data and expertise. Motivated by this fundamental challenge, we report upon the current state and evolution of biomedical informatics as it pertains to the conduct of high-throughput clinical and translational research and will present both a conceptual and practical framework for the design and execution of informatics-enabled studies. The objective of presenting such findings and constructs is to provide the clinical and translational research community with a common frame of reference for discussing and expanding upon such models and methodologies.
Collapse
Affiliation(s)
- Philip R O Payne
- Department of Biomedical Informatics, The Ohio State University, Columbus, Ohio 43210, USA.
| | | | | |
Collapse
|
36
|
Bernstam EV, Hersh WR, Johnson SB, Chute CG, Nguyen H, Sim I, Nahm M, Weiner MG, Miller P, DiLaura RP, Overcash M, Lehmann HP, Eichmann D, Athey BD, Scheuermann RH, Anderson N, Starren J, Harris PA, Smith JW, Barbour E, Silverstein JC, Krusch DA, Nagarajan R, Becich MJ. Synergies and distinctions between computational disciplines in biomedical research: perspective from the Clinical andTranslational Science Award programs. ACADEMIC MEDICINE : JOURNAL OF THE ASSOCIATION OF AMERICAN MEDICAL COLLEGES 2009; 84:964-70. [PMID: 19550198 PMCID: PMC2884382 DOI: 10.1097/acm.0b013e3181a8144d] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2023]
Abstract
Clinical and translational research increasingly requires computation. Projects may involve multiple computationally oriented groups including information technology (IT) professionals, computer scientists, and biomedical informaticians. However, many biomedical researchers are not aware of the distinctions among these complementary groups, leading to confusion, delays, and suboptimal results. Although written from the perspective of Clinical and Translational Science Award (CTSA) programs within academic medical centers, this article addresses issues that extend beyond clinical and translational research. The authors describe the complementary but distinct roles of operational IT, research IT, computer science, and biomedical informatics using a clinical data warehouse as a running example. In general, IT professionals focus on technology. The authors distinguish between two types of IT groups within academic medical centers: central or administrative IT (supporting the administrative computing needs of large organizations) and research IT (supporting the computing needs of researchers). Computer scientists focus on general issues of computation such as designing faster computers or more efficient algorithms, rather than specific applications. In contrast, informaticians are concerned with data, information, and knowledge. Biomedical informaticians draw on a variety of tools, including but not limited to computers, to solve information problems in health care and biomedicine. The paper concludes with recommendations regarding administrative structures that can help to maximize the benefit of computation to biomedical research within academic health centers.
Collapse
Affiliation(s)
- Elmer V Bernstam
- University of Texas Health Science Center at Houston, Texas 77030, USA.
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
37
|
Erdal S, Catalyurek UV, Payne PRO, Saltz J, Kamal J, Gurcan MN. A knowledge-anchored integrative image search and retrieval system. J Digit Imaging 2009; 22:166-82. [PMID: 18040742 PMCID: PMC3043680 DOI: 10.1007/s10278-007-9086-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2007] [Revised: 10/03/2007] [Accepted: 10/17/2007] [Indexed: 10/22/2022] Open
Abstract
Clinical data that may be used in a secondary capacity to support research activities are regularly stored in three significantly different formats: (1) structured, codified data elements; (2) semi-structured or unstructured narrative text; and (3) multi-modal images. In this manuscript, we will describe the design of a computational system that is intended to support the ontology-anchored query and integration of such data types from multiple source systems. Additional features of the described system include (1) the use of Grid services-based electronic data interchange models to enable the use of our system in multi-site settings and (2) the use of a software framework intended to address both potential security and patient confidentiality concerns that arise when transmitting or otherwise manipulating potentially privileged personal health information. We will frame our discussion within the specific experimental context of the concept-oriented query and integration of correlated structured data, narrative text, and images for cancer research.
Collapse
Affiliation(s)
- Selnur Erdal
- Information Warehouse, The Ohio State University Medical Center, 640 Ackerman Road, P.O. Box 183111, Columbus, OH 43218, USA.
| | | | | | | | | | | |
Collapse
|
38
|
Embi PJ, Payne PRO. Clinical research informatics: challenges, opportunities and definition for an emerging domain. J Am Med Inform Assoc 2009; 16:316-27. [PMID: 19261934 DOI: 10.1197/jamia.m3005] [Citation(s) in RCA: 100] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022] Open
Abstract
OBJECTIVES Clinical Research Informatics, an emerging sub-domain of Biomedical Informatics, is currently not well defined. A formal description of CRI including major challenges and opportunities is needed to direct progress in the field. DESIGN Given the early stage of CRI knowledge and activity, we engaged in a series of qualitative studies with key stakeholders and opinion leaders to determine the range of challenges and opportunities facing CRI. These phases employed complimentary methods to triangulate upon our findings. MEASUREMENTS Study phases included: 1) a group interview with key stakeholders, 2) an email follow-up survey with a larger group of self-identified CRI professionals, and 3) validation of our results via electronic peer-debriefing and member-checking with a group of CRI-related opinion leaders. Data were collected, transcribed, and organized for formal, independent content analyses by experienced qualitative investigators, followed by an iterative process to identify emergent categorizations and thematic descriptions of the data. RESULTS We identified a range of challenges and opportunities facing the CRI domain. These included 13 distinct themes spanning academic, practical, and organizational aspects of CRI. These findings also informed the development of a formal definition of CRI and supported further representations that illustrate areas of emphasis critical to advancing the domain. CONCLUSIONS CRI has emerged as a distinct discipline that faces multiple challenges and opportunities. The findings presented summarize those challenges and opportunities and provide a framework that should help inform next steps to advance this important new discipline.
Collapse
Affiliation(s)
- Peter J Embi
- Center for Health Informatics, University of Cincinnati Academic Health Center, 231 Albert Sabin Way, PO Box 670840, Cincinnati, OH, 45267-0840, USA.
| | | |
Collapse
|
39
|
Wang X, Liu L, Fackenthal J, Cummings S, Cook M, Hope K, Silverstein JC, Olopade OI. Translational integrity and continuity: personalized biomedical data integration. J Biomed Inform 2009; 42:100-12. [PMID: 18760382 PMCID: PMC2675887 DOI: 10.1016/j.jbi.2008.08.002] [Citation(s) in RCA: 22] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2008] [Revised: 08/04/2008] [Accepted: 08/05/2008] [Indexed: 12/18/2022]
Abstract
Translational research data are generated in multiple research domains from the bedside to experimental laboratories. These data are typically stored in heterogeneous databases, held by segregated research domains, and described with inconsistent terminologies. Such inconsistency and fragmentation of data significantly impedes the efficiency of tracking and analyzing human-centered records. To address this problem, we have developed a data repository and management system named TraM (http://tram.uchicago.edu), based on a domain ontology integrated entity relationship model. The TraM system has the flexibility to recruit dynamically evolving domain concepts and the ability to support data integration for a broad range of translational research. The web-based application interfaces of TraM allow curators to improve data quality and provide robust and user-friendly cross-domain query functions. In its current stage, TraM relies on a semi-automated mechanism to standardize and restructure source data for data integration and thus does not support real-time data application.
Collapse
Affiliation(s)
- Xiaoming Wang
- Biomedical Informatics Core, Computation Institute, University of Chicago
- Computation Institute, University of Chicago
| | - Lili Liu
- Biomedical Informatics Core, Computation Institute, University of Chicago
- Computation Institute, University of Chicago
| | - James Fackenthal
- Center for Clinical Cancer Genetics, Department of Medicine, University of Chicago
| | - Shelly Cummings
- Center for Clinical Cancer Genetics, Department of Medicine, University of Chicago
| | - Maggie Cook
- Center for Clinical Cancer Genetics, Department of Medicine, University of Chicago
| | - Kisha Hope
- Center for Clinical Cancer Genetics, Department of Medicine, University of Chicago
| | - Jonathan C. Silverstein
- Biomedical Informatics Core, Computation Institute, University of Chicago
- Computation Institute, University of Chicago
| | | |
Collapse
|
40
|
Abstract
The American Medical Informatics Association (AMIA) recently augmented the scope of its activities to encompass translational bioinformatics as a third major domain of informatics. The AMIA has defined translational bioinformatics as "... the development of storage, analytic, and interpretive methods to optimize the transformation of increasingly voluminous biomedical data into proactive, predictive, preventative, and participatory health." In this perspective, I will list eight reasons why this is an excellent time to be studying translational bioinformatics, including the significant increase in funding opportunities available for informatics from the United States National Institutes of Health, and the explosion of publicly-available data sets of molecular measurements. I end with the significant challenges we face in building a community of future investigators in Translational Bioinformatics.
Collapse
Affiliation(s)
- Atul J Butte
- Stanford Center for Biomedical Informatics, Department of Medicine and Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| |
Collapse
|
41
|
Ash JS, Anderson NR, Tarczy-Hornoch P. People and organizational issues in research systems implementation. J Am Med Inform Assoc 2008; 15:283-9. [PMID: 18308986 PMCID: PMC2410012 DOI: 10.1197/jamia.m2582] [Citation(s) in RCA: 44] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2007] [Accepted: 02/13/2008] [Indexed: 11/10/2022] Open
Abstract
Knowledge about people and organizational issues pertinent to implementation and maintenance of clinical systems has grown steadily over the past fifteen years. Less is known about implementation of systems used for clinical and biomedical research. In conjunction with current National Institutes of Health Roadmap efforts that promote translational research, these issues should now be identified and addressed. During the 2007 American College of Medical Informatics Symposium, members discussed behavioral aspects of translational informatics. This article summarizes that discussion, which covered organizational issues, implications of how knowledge about clinical systems implementation can inform research systems implementation, and those issues unique to each kind of system.
Collapse
Affiliation(s)
- Joan S Ash
- Department of Medical Informatics and Clinical Epidemiology, School of Medicine, Oregon Health & Science University, 3181 SW Sam Jackson Park Road, Portland, OR 97239-3098, USA.
| | | | | |
Collapse
|
42
|
Abstract
A deeper understanding of disease requires a database of human traits and disease states that is integrated with molecular information.
Collapse
Affiliation(s)
- Atul J Butte
- Stanford Center for Biomedical Informatics Research, Department of Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA.
| |
Collapse
|
43
|
Abstract
The future success of the translational research spectrum depends on the clinical research enterprise's ability to break through the barriers that constrain its productivity. As more basic science discoveries emerge, our ability to effectively translate this knowledge into improved patient care rests squarely on the manner in which we answer clinical questions. Informatics--the science of effective information use--is poised to help advance the conduct of science. However, incorporating informatics into the enterprise comes with its own set of challenges. To harness the benefits of improved information use, it is important to first establish how information flows within research. A thoughtful implementation of informatics--one that factors in social and organizational nuances--will undoubtedly lead to a more efficient and effective clinical research enterprise.
Collapse
Affiliation(s)
- Thomas K Chung
- Department of Biomedical Informatics, Columbia University, College of Physicians and Surgeons, New York, NY 10032, USA
| | | | | |
Collapse
|