Integrity Of Biomedical Research Research Articles

Enhancing quality and integrity in biomedical research in Africa: an international call for greater focus, investment and standardisation in capacity strengthening for frontline staff.

The integrity of biomedical research depends heavily on the quality of research data collected. In turn, data quality depends on processes of data collection, a task undertaken by frontline research staff in many research programmes in Africa and elsewhere. These frontline research staff often have additional responsibilities including translating and communicating research in local languages, seeking informed consent for study participation and maintaining supportive relationships between research institutions and study participants and wider communities. The level of skills that fieldworkers need to undertake these responsibilities clearly affects the quality of data collected, the ethics of research ‘on the ground’ and the short and long term acceptability of research.We organised an international workshop in Kenya in July 2014 to discuss the role of frontline staff in scientific research. A total of 25 field managers from 9 African countries and the UK met for 2.5 days to discuss the relationship between data quality and institutional performance management systems and how they affect career progression and supportive supervision policies of research frontline staff.From this workshop, and supporting an expanding literature on the role of fieldworkers in international health research, participants agreed that fieldworkers’ roles present them with practical and ethical challenges that their routine training does not adequately prepare them for. We argue that the common and complex challenges facing fieldworkers should in part be addressed through increased investment and collaborative agreements across types of research institutions in Africa. We call for standardization of core elements of training for this critically important cadre of research staff who perform similar roles and encounter similar challenges in many African settings. Although many valuable training elements are offered in institutions, there is a need to develop broader, more grounded and innovative strategies to address complex realities for fieldworkers, and support the integrity and ethics of health research in these settings.

FDA SEND in non-US countries responses to the standard for exchange of nonclinical data (SEND) in non-US countries

The Biomedical Research Integrated Domain Group (BRIDG) project is a collaborative initiative between the National Cancer Institute (NCI), the Clinical Data Interchange Standards Consortium (CDISC), the Regulated Clinical Research Information Management Technical Committee (RCRIM TC) of Health Level 7 (HL7), and the Food and Drug Administration (FDA) to develop a model of the shared understanding of the semantics of clinical research.The BRIDG project is based on open-source collaborative principles and an implementation-independent, use-case driven approach to model development. In the BRIDG model, declarative and procedural knowledge are represented using the Unified Modeling Language (UML) class, activity and state diagrams.The BRIDG model currently contains harmonized semantics from four project use cases: the caXchange project and the patient study calendar project from caBIG™; the standard data tabular model (SDTM) from CDISC; and the regulated products submission model (RPS) from HL7. Scalable harmonization processes have been developed to expand the model with content from additional use cases.The first official release of the BRIDG model was published in June 2007. Use of the BRIDG model by the NCI has supported the rapid development of semantic interoperability across applications within the caBIG™ program.The BRIDG project has brought together different standards communities to clarify the semantics of clinical research across pharmaceutical, regulatory, and research organizations. Currently, the NCI uses the BRIDG model to support interoperable application development in the caBIG™, and CDISC and HL7 are using the BRIDG model to support standards development.

Publication planning: promoting an ethics of transparency and integrity in biomedical research.

Biomedical research should include plans to communicate complete and accurate results to the scientific community and the public in a timely manner. All too often, however, such planning is lacking until after data have been generated. We developed a collaborative professional statement following review of the indexed biomedical literature and relevant professional society guidelines. Planning for publications before, during and after biomedical research studies are conducted promotes the timely dissemination of accurate and comprehensive results. Effective publication planning accounts for the work of all contributors, encourages full transparency and contributes to overall scientific integrity. Although the most obvious contribution of publication planning is to result dissemination, the best planning may also help improve the overall quality of research study design and the overall integrity of study conduct by keeping the final audience in the forefront of the investigators' attention. Publication planning can help biomedical researchers achieve and maintain high standards of transparency and integrity. Table 1 below highlights briefly some of the aspects to be included in a publication plan.

Topical Review: Translating Translational Research in Behavioral Science.

To present a model of translational research for behavioral science that communicates the role of behavioral research at each phase of translation. A task force identified gaps in knowledge regarding behavioral translational research processes and made recommendations regarding advancement of knowledge. A comprehensive model of translational behavioral research was developed. This model represents T1, T2, and T3 research activities, as well as Phase 1, 2, 3, and 4 clinical trials. Clinical illustrations of translational processes are also offered as support for the model. Behavioral science has struggled with defining a translational research model that effectively articulates each stage of translation and complements biomedical research. Our model defines key activities at each phase of translation from basic discovery to dissemination/implementation. This should be a starting point for communicating the role of behavioral science in translational research and a catalyst for better integration of biomedical and behavioral research.

Extension of the primary care research object model (PCROM) as clinical research information model (CRIM) for the "learning healthcare system".

BackgroundPatient data from general practices is already used for many types of epidemiological research and increasingly, primary care systems to facilitate randomized clinical trials. The EU funded project TRANSFoRm aims to create a “Learning Healthcare System” at a European level that is able to support all types of research using primary care data, to recruit patients and follow patients in clinical studies and to improve diagnosis and therapy. The implementation of such a Learning Healthcare System needs an information model for clinical research (CRIM), as an informational backbone to integrate aspects of primary care with clinical trials and database searches.MethodsWorkflow descriptions and corresponding data objects of two clinical use cases (Gastro-Oesophageal Reflux Disease and Type 2 Diabetes) were described in UML activity diagrams. The components of activity diagrams were mapped to information objects of PCROM (Primary Care Research Object Model) and BRIDG (Biomedical Research Integrated Domain Group) and evaluated. The class diagram of PCROM was adapted to comply with workflow descriptions.ResultsThe suitability of PCROM, a primary care information model already used for clinical trials, to act as an information model for TRANSFoRm was evaluated and resulted in its extension with 14 new information object types, two extensions of existing objects and the introduction of two new high-ranking concepts (CARE area and ENTRY area). No PCROM component was redundant. Our result illustrates that in primary care based research an important but underestimated portion of research activity takes place in the area of care (e.g. patient consultation, screening, recruitment and response to adverse events). The newly introduced CARE area for care-related research activities accounts for this shift and includes Episode of Care and Encounter as two new basic elements. In the ENTRY area different aspects of data collection were combined, including data semantics for observations, assessment activities, intervention activities and patient reporting to enable case report form (CRF) based data collection combined with decision support.ConclusionsResearch with primary care data needs an extended information model that covers research activities at the care site which are characteristic for primary care based research and the requirements of the complicated data collection processes.

Heterogeneity in European Research Integrity Guidance

Similar forms of misconduct are perceived differently throughout Europe. There are no extensive surveys on the guidance on research integrity in the different countries of Europe. Therefore, we performed a systematic content analysis of (biomedical) research integrity guidance documents from all the countries of the European Economic Area. We show that there is strong heterogeneity concerning research integrity guidance on crucial aspects, for example, the defining of research misconduct, at both an international and a national level. We also sought to explain why the guidance documents differ by distinguishing the approaches that underlie them. We distinguished a value-based and a norm-based approach, as well as different perspectives on trust. The current confusing situation concerning research integrity guidance hampers international research and possibly wastes research funds. We risk talking past each other, if we do not take the distinction between these underlying approaches into account.

Meditación sobre la compleja relación entre medicina y animales: un responsopara un gato fiel

From its basis in the writings of the philosopher Peter Singer and the bioethical shortcomings of animal experimentation and animal husbandry, the animal rights movement has evolved into an important societal movement critical of animal experimentation in biomedical research. A lack of dialogue and transparency, an absence of understanding and an unreasonable radicalization of different positions regarding animal experimentation has frequently resulted in an adversarial relationship between some members of the scientific community and societal groups aggressively protecting animal rights. In response to this problem, both the bioethical regulations pertaining to biomedical experimentation with animals and the powers of animal care committees (IACUCs) have been strengthened. Careful analysis of the relevance of animal models to human conditions, replacement of these models with non-animal models when possible, adequate re-examination of existing knowledge before undertaking new experimental projects involving animals, and the improvement of methods to avoid animal stress and pain have further strengthened the bioethical basis of animal experimentation. To improve the ethical integrity of research conducted with animals, it is also necessary to increase the editorial scrutiny of the bioethical standards of potentially publishable research utilizing animals. Of note is also the recent use of animals in alternative animal associated therapies (AAT) to ameliorate several medical conditions. Education of the biomedical community, including students and professionals, and of societal groups concerned about this issue as well as directness and continuous dialogue among all the stakeholders are essential to insure the wellbeing of animals and the ethical integrity of biomedical research.

“The Animal” After Derrida: Interrogating the Bioethics of Geno-Cide

Bioethics tends to be dominated by discourses concerned with the ethical dimension of medical practice, the organization of medical care, and the integrity of biomedical research involving human subjects and animal testing. Jacques Derrida has explored the fundamental question of the “limit” that identifies and differentiates the human animal from the nonhuman animal. However, to date his work has not received any reception in the field of biomedical ethics. In this paper, I examine what Derrida’s thought about this limit might mean for the use/misuse/abuse of animals in contemporary biomedical research. For this, I review Derrida’s analysis and examine what it implies for scientific responsibility, introducing what I have coined the “Incompleteness Theorem of Bioethics.”

Identifying Appropriate Reference Data Models for Comparative Effectiveness Research (CER) Studies Based on Data from Clinical Information Systems

The need for a common format for electronic exchange of clinical data prompted federal endorsement of applicable standards. However, despite obvious similarities, a consensus standard has not yet been selected in the comparative effectiveness research (CER) community. Using qualitative metrics for data retrieval and information loss across a variety of CER topic areas, we compare several existing models from a representative sample of organizations associated with clinical research: the Observational Medical Outcomes Partnership (OMOP), Biomedical Research Integrated Domain Group, the Clinical Data Interchange Standards Consortium, and the US Food and Drug Administration. While the models examined captured a majority of the data elements that are useful for CER studies, data elements related to insurance benefit design and plans were most detailed in OMOP's CDM version 4.0. Standardized vocabularies that facilitate semantic interoperability were included in the OMOP and US Food and Drug Administration Mini-Sentinel data models, but are left to the discretion of the end-user in Biomedical Research Integrated Domain Group and Analysis Data Model, limiting reuse opportunities. Among the challenges we encountered was the need to model data specific to a local setting. This was handled by extending the standard data models. We found that the Common Data Model from the OMOP met the broadest complement of CER objectives. Minimal information loss occurred in mapping data from institution-specific data warehouses onto the data models from the standards we assessed. However, to support certain scenarios, we found a need to enhance existing data dictionaries with local, institution-specific information.

Climate change and infectious diseases of wildlife: Altered interactions between pathogens, vectors and hosts

Abstract Infectious diseases result from the interactions of host, pathogens, and, in the case of vector-borne diseases, also vectors. The interactions involve physiological and ecological mechanisms and they have evolved under a given set of environmental conditions. Environmental change, therefore, will alter host-pathogen-vector interactions and, consequently, the distribution, intensity, and dynamics of infectious diseases. Here, we review how climate change may impact infectious diseases of aquatic and terrestrial wildlife. Climate change can have direct impacts on distribution, life cycle, and physiological status of hosts, pathogens and vectors. While a change in either host, pathogen or vector does not necessarily translate into an alteration of the disease, it is the impact of climate change on the interactions between the disease components which is particularly critical for altered disease risks. Finally, climate factors can modulate disease through modifying the ecological networks host-pathogen-vector systems are belonging to, and climate change can combine with other environmental stressors to induce cumulative effects on infectious diseases. Overall, the influence of climate change on infectious diseases involves different mechanisms, it can be modulated by phenotypic acclimation and/or genotypic adaptation, it depends on the ecological context of the host-pathogen-vector interactions, and it can be modulated by impacts of other stressors. As a consequence of this complexity, non-linear responses of disease systems under climate change are to be expected. To improve predictions on climate change impacts on infectious disease, we suggest that more emphasis should be given to the integration of biomedical and ecological research for studying both the physiological and ecological mechanisms which mediate climate change impacts on disease, and to the development of harmonized methods and approaches to obtain more comparable results, as this would support the discrimination of case-specific versus general mechanisms.

Thyroid Cancer Cell Line Misidentification: An Update

Human cancer cell lines remain valuable tools to study the biology of cancer and to test new therapies. Thus, the integrity of cell lines derived from tumors is critical to accurately study tumor-specific mechanisms and response to therapy. Despite the importance of cell lines, approximately 18–36% of cell lines are cross-contaminated or misidentified (1). It is well known that the cell line misidentification problem started after the establishment of the HeLa cell line, which is a common cross-contaminant due to its aggressive properties in culture. Despite this long history, cell line cross-contamination and misidentification remains a significant problem and continues to affect the integrity of biomedical research. In 2008, our group reported that approximately 20 out of 40 thyroid cancer cell lines were either redundant or misidentified with other tumor types (2). These results were quite shocking, given that many of these cell lines had been widely used for the past 20 years. Of note, our studies did not include cell lines derived from medullary thyroid cancer (MTC). In this issue of the JCEM, Nelkin and colleagues (3) report the misidentification of two presumed MTC cell lines, RO-H85-1 and RO-D81-1. Specifically, Nelkin's group used short tandem repeat (STR) profiling, which is an internationally recognized method for the genetic profiling of cell lines (4), and showed that the presumed MTC cell line, RO-H85-1, is genetically identical to the bladder cancer cell line, 647-V, and the presumed MTC cell line, RO-D81-1, is genetically identical to the HT-29 colon cancer cell line. The only observed differences in the genetic profiling between identical cell lines were likely due to genetic instability or loss of heterozygosity, which commonly occurs in culture (2, 5). Based on the timeline of establishment, the RO-H85-1 and RO-D81-1 cell lines are likely derivatives of these more aggressive bladder and colon cancer cell lines, respectively. Consistent with the aggressive nature of the HT-29 cell lines, we previously found that the HT-29 colon cancer cell line was a cross-contaminant of what were thought to be 5 different thyroid cancer cell lines (2). Although STR profiling represents a powerful approach for the genetic authentication of cell lines, it does not provide information on tissue of origin. Therefore, the analysis of phenotypic markers can be useful to further characterize the tissue of origin. For the RO-H85-1 and RO-D81-1 cell lines, little has been reported regarding the expression of MTC-specific genes, except, as noted by Nelkin and colleagues, that the RO-H85-1 cell line was originally reported to produce calcitonin (6). Thus, it is possible that the original RO-H85-1 cell line was indeed of MTC origin and later became cross-contaminated, or that the detection of calcitonin production in this cell line was a false-positive result. Nonetheless, the STR results show that the RO-H85-1 and RO-D81-1 “MTC” cell lines are genetically identical to the HT-29 and 647-V cell lines, respectively, and are likely misidentified with these nonthyroid cell lines. Although the RO-H85-1 and RO-D81-1 cell lines were not widely used MTC models, the loss of these two cell lines nonetheless leaves the MTC field with a limited number of valid cell culture models. Nelkin's group (3) went on to validate two of the most widely used MTC-derived cell lines, the TT and MZ-CRC-1, and found that they are genetically unique. Thus, the TT and MZ-CRC-1 cell lines, along with the future development and validation of additional MTC cell line models, which genetically match their tissue of origin, will be critical models to study MTC biology and test new therapeutic strategies. In conclusion, the misidentification of two additional thyroid cancer cell lines once again emphasizes the importance of authenticating cell lines. STR profiling is a straightforward and economically feasible approach for the genetic profiling of cells. The key advantage to STR profiling is that the number of allelic repeats is converted into a genetic “barcode,” which can be used to search databases of cell line STR profiles. All major cell line repositories, including the American Type Culture Collection (ATCC), German Collection of Microorganisms and Cell Cultures (DSMZ), European Collection of Cell Cultures (ECACC), and Japanese Collection of Research Bioresources Cell Bank (JCRB), maintain STR databases for their cell lines. To maintain the genetic identity of cell lines, it is recommended that laboratories routinely perform STR profiling on cell line stocks. Although many journals and funding agencies recommend cell line authentication, an increasing number of journals now require cell line authentication due to the importance of cell line models in translational research and ultimately future therapies for patients.

Life sciences domain analysis model

ObjectiveMeaningful exchange of information is a fundamental challenge in collaborative biomedical research. To help address this, the authors developed the Life Sciences Domain Analysis Model (LS DAM), an information model that provides a framework for communication among domain experts and technical teams developing information systems to support biomedical research. The LS DAM is harmonized with the Biomedical Research Integrated Domain Group (BRIDG) model of protocol-driven clinical research. Together, these models can facilitate data exchange for translational research.Materials and methodsThe content of the LS DAM was driven by analysis of life sciences and translational research scenarios and the concepts in the model are derived from existing information models, reference models and data exchange formats. The model is represented in the Unified Modeling Language and uses ISO 21090 data types.ResultsThe LS DAM v2.2.1 is comprised of 130 classes and covers several core areas including Experiment, Molecular Biology, Molecular Databases and Specimen. Nearly half of these classes originate from the BRIDG model, emphasizing the semantic harmonization between these models. Validation of the LS DAM against independently derived information models, research scenarios and reference databases supports its general applicability to represent life sciences research.DiscussionThe LS DAM provides unambiguous definitions for concepts required to describe life sciences research. The processes established to achieve consensus among domain experts will be applied in future iterations and may be broadly applicable to other standardization efforts.ConclusionsThe LS DAM provides common semantics for life sciences research. Through harmonization with BRIDG, it promotes interoperability in translational science.

Providing Semantic Interoperability Between Clinical Care and Clinical Research Domains

Improving the efficiency with which clinical research studies are conducted can lead to faster medication innovation and decreased time to market for new drugs. To increase this efficiency, the parties involved in a regulated clinical research study, namely, the sponsor, the clinical investigator and the regulatory body, each with their own software applications, need to exchange data seamlessly. However, currently, the clinical research and the clinical care domains are quite disconnected because each use different standards and terminology systems. In this article, we describe an initial implementation of the Semantic Framework developed within the scope of SALUS project to achieve interoperability between the clinical research and the clinical care domains. In our Semantic Framework, the core ontology developed for semantic mediation is based on the shared conceptual model of both of these domains provided by the BRIDG initiative. The core ontology is then aligned with the extracted semantic models of the existing clinical care and research standards as well as with the ontological representations of the terminology systems to create a model of meaning for enabling semantic mediation. Although SALUS is a research and development effort rather than a product, the current SALUS knowledge base contains around 4.7 million triples representing BRIDG DAM, HL7 CDA model, CDISC standards and several terminology ontologies. In order to keep the reasoning process within acceptable limits without sacrificing the quality of mediation, we took an engineering approach by developing a number of heuristic mechanisms. The results indicate that it is possible to build a robust and scalable semantic framework with a solid theoretical foundation for achieving interoperability between the clinical research and clinical care domains.

Ethics and Scientific Integrity in Public Health, Epidemiological and Clinical Research.

The ethics and scientific integrity of biomedical and public health research requires that researchers behave in appropriate ways. However, this requires more than following of published research guidelines that seek to prevent scientific misconduct relating to serious deviations from widely accepted scientific norms for proposing, conducting, and reporting research (e.g., fabrication or falsification of research data or failures to report potential conflicts of interest). In this paper we argue for a broader account of scientific integrity, one consistent with that defended by the United States Institute of Medicine, involving a commitment to intellectual honesty and personal responsibility for one's actions as a researcher and to practices consistent with the responsible conduct of research and protection of the research participants. Maintaining high standards of ethical and scientific integrity helps to maintain public trust in the research enterprise. An increasing number of authors have pointed to the importance of mentoring and education in relation to the responsible conduct of science in preventing transgressions of scientific integrity. Just like in clinical research and biomedicine, epidemiologists and other public health researchers have the responsibility to exhibit and foster the very highest standards of scientific integrity.

Swedish Medical Nanoscience Center at Karolinska Institutet

The Swedish Medical Nanoscience Center is an integrative research hub located at Karolinska Institutet in Stockholm, Sweden. In this highly reputed medical research institute, our mission is to establish the area of nanomedicine as a means to advance this new research area to meet the demands of future innovative health care solutions. Using interdisciplinary basic research, often referred to as ‘curiosity-driven’ research, as our driving force, we promote the integration of cutting-edge nanotechnology and biomedical research. As new concepts and technologies mature, focus of our projects is gradually shifted towards clinically relevant problems. Advancement of the area of nanomedicine requires engagement of a new breed of scientist with the enthusiasm, devotion and competence to communicate and work across many disciplines. Together, they help generate a highly multidisciplinary scientific environment, producing new knowledge that ultimately becomes the stepping-stone towards novel therapies for the benefit of the patient.

HL7 CDA R2 Based Document Sharing in Biomedical Research: Design of a Severe Adverse Event (SAE) Report Document

Background: The IT infrastructure requirements of Medical Research Networks (MRNs) are increasing with time and technological progress. The demand for modularity, interoperability and the support of standards calls for new strategies. In the healthcare domain, the application of Integrating the Healthcare Enterprise (IHE) profiles is a widely established approach. Hence, pursuing this approach may lead to a future-oriented system architecture enabling the integration of biomedical research with the healthcare domain. Objectives: The present paper focuses on the document sharing aspect based on HL7 Clinical Document Architecture (CD A), especially on the CDA document design of a Severe Adverse Events (SAE) report document taken from a clinical trial in the field of paediatric oncology. Methods: The CDA document design for the pre-existing SAE form was based on the CDA R2 specification and state-of-the-art implementation guidelines issued by the ELGA GmbH and IHE. To facilitate interoperability with third party information systems the focus was on using external vocabulary and code lists as well as predefined CDA structure templates. Results: A CDA document design for an SAE report was developed. The document was at least Level 2 and to a large extent Level 3 coded. Conclusions: eCRFs can b e designed on the basis of CDA R2 while interoperability in large MRNs has to b e ensured through validation against strict business rules. Further questions regarding IT infrastructure which result from particular requirements of MRNs have to b e dealt with in the future.

Book Review: Trust and Integrity in Biomedical Research: The Case of Financial Conflicts of Interest

Book Review: Trust and Integrity in Biomedical Research: The Case of Financial Conflicts of Interest

Trust and Integrity in Biomedical Research: The Case of Financial Conflicts of Interest

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Quantifying the variability of financial disclosure information reported by authors presenting at annual spine conferences

Background context In recent years, greater attention has been directed toward determining how potential financial conflicts of interest may affect the integrity of biomedical research. To address this issue, various disclosure policies have been adopted in an attempt to increase the transparency of this process. However, the consistency of such reporting among spine surgeons remains unknown. This study quantifies the variability in the self-reported disclosures of individual authors presenting at multiple spine conferences during the same year. Methods The author disclosure information published for the 2008 North American Spine Society (NASS), Cervical Spine Research Society (CSRS), and Scoliosis Research Society (SRS), conferences were compiled into a database. We evaluated the disclosure policy for each society and compared the disclosure listings of authors who presented at more than one of these meetings. Results Disclosure records were available for 1,231 authors at NASS, 550 at CSRS, and 642 at SRS. Of these individuals, 278 (NASS), 129 (CSRS), and 181 (SRS) presented at one of the other conferences and 40 presented at all three conferences. North American Spine Society and CSRS required disclosure of all financial relationships, whereas SRS only requested disclosures pertinent to authors’ presentations. Of the 153 authors who presented at the NASS and CSRS meetings, 51% exhibited discrepancies in their disclosure information. In contrast, only 9% of the 205 individuals whose data was listed at both the NASS and SRS conferences demonstrated irregularities. Similarly, 18% of the 56 authors who had provided information to both CSRS and SRS were inconsistent in their reporting. Conclusions These findings emphasize the significant variability that currently exists in the reporting of financial conflicts of interest by authors who presented at three major spine conferences within the past year. We believe these discrepancies are likely because of confusion regarding what relationships should be acknowledged in certain situations and the clear lack of uniformity among the disclosure policies established by these various associations. Clinical relevance This study evaluates financial conflicts of interests in clinical research.

Biomedical informatics and outcomes research: enabling knowledge-driven health care.

At its core, the practice of medicine is an information-intensive endeavor. Most of what physicians do involves the collection, review, and management of information. Examples of such activities include obtaining and recording patient information, consulting colleagues, reading the scientific literature, planning diagnostic procedures, devising strategies for patient care, interpreting tests, and conducting research. The ever-increasing biomedical knowledge base that must be considered to deliver optimal patient care only adds to the challenges facing medicine today. Successfully addressing these challenges to deliver the best health care possible requires not only the existence of valid and generalizable data sets derived from systematic basic, clinical, and epidemiological research efforts but also the ability to apply the knowledge derived from these research efforts at the point of care. It is easy to understand, therefore, why the field of biomedical informatics, a field that is concerned with collecting, managing, and optimally using information in health care and biomedicine, is critical to the current and future practice of medicine and the study of healthcare outcomes that result from such practice.1,2 Biomedical informatics approaches and related health information technology (health IT) platforms are key to enabling knowledge-driven healthcare and practice improvement initiatives based on a solid research foundation. Similar biomedical informatics approaches and resources are also critical to advancing outcomes research. Indeed, such technologies such as electronic health records (EHRs), clinical data repositories, and research-specific data management systems are already transforming the way we practice medicine and conduct research. This transformation is being further advanced by federally directed funding and research infrastructure development efforts.3,4 In the sections that follow, we provide an overview of how biomedical informatics and health IT processes and tools can affect the conduct of research and the delivery of evidence-based health care from our perspective. Given the current state of development …