Abstract
Collecting and managing data for clinical and translational research presents significant challenges for clinical and translational researchers, many of whom lack needed access to data management expertise, methods, and tools. At many institutions, funding constraints result in differential levels of research informatics support among investigators. In addition, the lack of widely shared models and ontologies for clinical research informatics and health information technology hampers the accurate assessment of investigators’ needs and complicates the efficient allocation of crucial resources for research projects, ultimately affecting the quality and reliability of research. In this paper, we present a model for providing flexible, cost‐efficient institutional support for clinical and translational research data management and informatics, the research management team, and describe our initial experiences with deploying this model at our institution. Clin Trans Sci 2012; Volume 5: 464–469
Keywords: biostatistics, clinical trials, computers, translational research
Introduction
The tasks of collecting and managing data for clinical and translational research pose significant challenges for researchers who lack access to data management expertise, methods, and tools. These tasks, known collectively as clinical research informatics, 1 include management of information acquired directly from clinical trials as well as secondary use of data gathered in the context of patient care for research purposes. The development of a robust research infrastructure that includes sophisticated approaches to data management and informatics is vital to modern, data‐intensive clinical and translational research. But despite such infrastructure having been identified as a key priority, 2 significant gaps remain and the conceptual frameworks and nomenclature of informatics and health information technology (HIT) are not agreed upon or well understood, either within or outside of academic health and science systems. 3 One key effect of this lack of common models and terminology has been to make the articulation of informatics needs and appropriate direction of resources for informatics support a continual challenge for clinical and translational research.
Levels of informatics support for the diverse array of investigator‐initiated research vary significantly among institutions. 4 Researchers working in well‐funded departments are more likely to have access to a clinical research infrastructure that includes statistical assistance, data management support, and a secure, compliant, and robust application suite than are investigators with limited funding or who rely upon internal departmental funds. In addition, lack of communication concerning available resources can also lead to underutilization of those resources. 5
In this paper, we present a model for providing flexible, cost‐efficient institutional support for clinical and translational research data management and informatics, the research management team (RMT), and describe our initial experiences with this model.
Research Infrastructure at Duke Translational Medicine Institute
The Duke Translational Medicine Institute (DTMI) is the academic home for the clinical and translational research community at Duke University. 6 Initially funded by a Clinical and Translational Science Award (CTSA) from the National Institutes of Health, the DTMI's integrated infrastructure offers resources and training for investigators across a broad array of disciplines. As part of its core mission, the DTMI also facilitates cross‐institutional collaboration by teaming multidisciplinary investigators with industry‐model project management and by providing investigators easy access to emerging technologies and methodologies.
As is the case with many academic institutions, researchers at Duke have traditionally addressed data collection needs in ad hoc fashion, using applications that ranged from simple, off‐the‐shelf software to robust dedicated systems typical of industry‐supported clinical trials. 7 Because most investigator‐initiated projects are modestly funded, low‐cost, consumer‐grade spreadsheet, and database software packages are often used to collect and manage research data. 7 Although these applications are cheap and ubiquitous, they are often implemented without technical controls that would enforce data management best practices such as data security, audit trails, data integrity constraints, and uniform data cleaning; further, they do not provide facilities to automate data processing, such as double data entry, systematic data capture, on‐screen checks for data errors, routing data discrepancies for resolution, integration of external data, and management of metadata, including rules applied for data cleaning. Moreover, these applications are not supported by institutional infrastructure that provides data storage, backup, privacy and security assurance; all can be used in a manner inconsistent with security and privacy controls for health data, or provided to staff who lack expertise in devising and applying appropriate controls.
Taken together, these shortcomings increase the likelihood that research data integrity may be compromised. If discovered early, such compromise may entail onerous manual data processing and other additional work for investigators and statisticians; if such problems are not detected prior to manuscript submission, they may result in publication of erroneous information.
Beginning in 2006, the DTMI sought to identify and provide data collection and management resources for small or single‐site studies across Duke in ways that would be both cost‐efficient and compliant with regulations. Initially, the DTMI Biomedical Informatics Core offered a free 1‐hour consultation to help researchers in match appropriate methods and systems to their study needs. The DTMI also posted a list of all data collection resources available on campus, 8 , 9 including Duke's access‐controlled internal clinical data warehouse. 10
Although these interventions were helpful, they did not constitute a comprehensive, institution‐wide solution. The free self‐service research data collection tool that Duke offered to investigators featured a point‐and‐click interface for configuring data entry screens and single‐value data checks; data were stored in a secured relational database. However, some researchers experienced difficulties in managing their data without expert support for data capture systems. In a few cases, technical expertise had to be recruited to explain and correct referential integrity problems caused by mid‐study changes to data collection screens and by screens that were configured by operators who lacked a sufficient understanding of relational principles. For these reasons, other solutions were sought to provide support during study initiation.
During this same period, Harris et al. released Research Electronic Data Capture (REDCap), 11 , 12 a freely licensed (but non‐open‐source) product developed by Vanderbilt University with support from the CTSA program. 13 Unlike commercially available products often used for investigator‐initiated studies, REDCap offers a built‐in audit log, individual user assignment rights, a Web‐based interface, and data access groups that enable data collection (including protected health information) across institutions in a secure, compliant system. Since its release, REDCap has continued to evolve and the REDCap Consortium currently includes more than 483 partners worldwide. 12
The DTMI Biomedical Informatics Core adopted REDCap in recognition of a universal need to test, validate, and assure compliance for all research data, regardless of the level of financial support. The validation process was considered critical both to local decisions about the use of REDCap within Duke, as well as to the creation of a validation framework of documentation and templates that would benefit the entire REDCap consortium. In addition, the imperative for assuring data provenance 14 necessitated clear documentation and an audit trail, both of which are embedded functions within REDCap. However, making REDCap widely available to untrained users without a supporting infrastructure was thought likely to result both in misuse of the system and in continued poor data collection and management practices. Instead, the DTMI sought to ensure successful implementation by combining the tool's release with data management services provided through a partnership with the Duke School of Nursing's RMT. The RMT, which was built on a conceptual framework designed to assure data quality, 7 was designated as the central resource for data management and research assistance for REDCap, as it was already providing similar services on a smaller scale to investigators within the School of Nursing. Historically, successful Duke enterprise‐level systems have required accompanying support. By emulating this approach on a smaller scale, the RMT identifies and initiates programs that support various levels of need‐based funding, allowing it to serve a much wider and more diverse group of clinical investigators and studies.
The RMT Model
In 2009, the DTMI and the RMT formed a synergistic partnership that leveraged strengths within each group to create a model for effective clinical and translational research support that could overcome limitations imposed by inflexible organizational structures. 15 Through funding from the Duke CTSA and the Duke School of Medicine, the Biomedical Informatics Core deployed and maintains the REDCap environment, supplements RMT physical space and management overhead, and provides additional support to maintain full‐time effort for RMT staff shortfalls incurred during project transitions.
This model affords a low‐cost, high‐quality solution for researchers. System implementation, support, storage, maintenance, testing, and validation are all supported by the DTMI; researchers using the service pay for direct project costs only, eliminating the need to hire, manage, and train qualified staff for each project, and minimizing the cost of expert statistical support for small or pilot investigations. Since its inception, the RMT has doubled the number of supported studies/investigators while leaving “backstop” funds untouched.
Initially, the RMT consisted of 6.5 full‐time equivalent employees (FTEs): a director, a manager, two data technicians, two data managers, and a portion of a research assistant. The RMT's inaugural project in 2008 was a single cancer‐related quality of life study that was part of the NIH's Patient‐Reported Outcomes Measurement Information (PROMIS) Network. 16 By making unallocated staff time available to other investigators during downtime, the RMT conserved effort on the PROMIS study and was able to assist the investigator in conducting an additional substudy to assess the accuracy of 30‐day recall for components of sexual function. The RMT director and manager also developed methods to assess other projects’ needs within the Duke School of Nursing and track incoming requests to determine optimal rates for RMT growth.
New employees are hired into RMT based on need, appropriate skills, shared vision for the RMT model, and an attitude of imaginative, energetic problem‐solving. With support from the DTMI and the School of Nursing, the RMT devotes substantial administrative time and effort ( Figure 1 ), none of which is charged to investigators. 17
Figure 1.
Schematic representation of REDCap support model.
The RMT Experience at 3 Years
Currently, the RMT has 14 FTEs. The director is responsible for oversight of strategic plans and workflow processes and serves as the liaison to other research entities at Duke (School of Medicine research groups, DTMI Biomedical Informatics Core, Duke Medical Library, Research Support Office, institutional review board [IRB], Contracts Office, etc.). One research manager is responsible for personnel oversight for two arms of the RMT: project support and data support). The former includes research project‐based work (IRB, project management, research assistance, survey administration, chart abstraction, transcription, and data entry) while the latter focuses on data design, construction, management, and delivery. Staff members are provided opportunities for personal and professional growth (e.g., research training, management training, writing skills, statistics and programming courses).
When contacted by an investigator or study team, an RMT manager and analyst schedule a free consultation to evaluate the request and assess project needs. At this meeting, RMT staff work with the study team to assess timelines and funding source(s) and client needs ( Table 1 ). RMT staff also address specific operational questions and make recommendations for data setup based on project needs. When appropriate, they may provide a brief overview and demonstration of the recommended system, including project‐specific features. Within 1 week of the initial meeting, the RMT manager creates a detailed written estimate that includes the percentage effort needed to complete the project, a detailed timeline, lists of individual staff members with current salary and fringe, and a scope‐of‐work description. The investigator then must sign the agreement and specify a funding source.
Table 1.
Consultation checklist.
| Funding source, grant term, ‐budget, timeline | Research aims, protocol or grant |
|---|---|
| Multisite—data access groups | Regulatory issues (data use/transfer) |
| Veterans administration ‐data—FISMA compliance | FDA—need for Part 11 ‐compliance |
| Legacy data cleaning, import, and server location | Data: capture, tracking, ‐scheduling |
| Documentation available for existing system | CRFs created? Validated ‐measures? |
| Technology (iPAD, cell phones, online modules) | Flat or relational data? ‐Longitudinal? |
| Security, audit trail | Variable/coding naming ‐convention |
| Preference for statistical package/setup | Discussion of RMT% effort model |
Investigators with modest funding may need access to a host of skills that rarely reside within a single FTE. For example, staff with good data and programming skills may lack expertise in research regulations or the science they are supporting; conversely, clinical research coordinators may be skilled at creating protocols and recruiting research participants but lack the ability to design and program databases or perform data management activities. Under the flexible RMT model, the quality of the work does not depend on the availability of particular individuals but is instead assured by the application of standards and oversight that provide predictable and consistent quality.
Careful matching of specialized RMT skills to specific research needs is critical to ensuring a cost‐efficient system. For this reason, RMT employs data managers and analyst programmers capable of handling requirements that range from simple prospective data capture to complex multi‐platform tracking and data mining. These personnel are then paired with data technicians who accomplish data abstraction and data entry. Finally, clinical research coordinators with extensive regulatory knowledge provide IRB support, research coordination, and project management. By combining these skill sets in a customized package, the RMT offers the researchers a “composite” FTE capable of fulfilling their research needs simply and economically while minimizing attrition and maintaining skilled, research‐trained employees with extensive institutional knowledge.
The RMT's institutional goal of improving the quality of data capture and analysis reduces the risk that researchers who are accustomed to developing needs‐based data solutions will overlook processes related to standardization, testing, and documentation. RMT data core employees are trained to build outward from a minimal set of data standards for variable naming and coding, developing and following SOPs that are based on existing regulations for database design and include managing user assignments to databases, testing calculated fields and branching logic, flagging fields that contain HIPAA‐protected 18 identifiers, and developing forms appropriate for all user status levels. Data managers also provide codebooks and data dictionaries for statisticians and accomplish work storage according to explicit internal procedures. These SOPs allow other RMT staff to offer seamless support for a project in the event that an employee leaves or becomes unavailable for an extended period. Further, when studies are audited, database documentation is readily available and comprehensible to external parties.
Tracking and managing percentage‐effort projects for employees is time‐intensive and requires careful balancing of workflow and staff availability. Excessive workload can result in turning away requests, thus reducing the probability of repeat business; on the other hand, extended gaps in activity may deplete institutional resources. Thus, the RMT model depends on maintaining the most efficient combination of employees and skillsets. Research managers perform the groundwork with investigators by establishing agreements, managing time, and eliminating “scope‐creep”; meanwhile, RMT staff are able to focus on the work for which they have been trained and investigators are in turn able to concentrate on the science, grant submissions, and publications that will ultimately help to improve the human condition 19 and drive support for funding the clinical research enterprise.
The RMT model controls the monetary costs associated with research data management by utilizing experienced, trained staff to build and support consistent database solutions. Another available option would be to provide salary support for researchers to develop solutions as needed for specific projects. This latter approach, although sometimes less expensive initially, may entail additional data management burdens and can result in a poorer product; almost always, it results in equivalent or higher costs. In general, cost is based on the scope and complexity of work, but an average RMT cost for setup, testing, documentation, and implementation of a REDCap database is the equivalent of 5% salary and fringe annually (roughly $2500 per project). Additional costs are incurred only for time that is dedicated to the project (e.g., a data manager who works in tandem with their study team to manage daily data workflow).
Institutional Reception to the RMT Model
The bundled service created by combining REDCap and RMT has been well received across this institution. The principal investigator for PROMIS, Kevin Weinfurt, Ph.D., indicated at the time of project completion that:
“The RMT provided excellent support for this project and greatly reduced the data management burden on my team. We were confident our data were secure and organized, and had full confidence in this team to manage the entire process for us.”
In a customer satisfaction survey sent to 117 RMT users (52% response rate) in 2010–2011, 98% of responders were somewhat satisfied or very satisfied with the cost of allocating effort for RMT staff and 96% of were somewhat satisfied or very satisfied with data integrity, compliance and security. Overall, the majority of faculty and staff who responded to the survey reported that they were satisfied or very satisfied with all areas of service provided by RMT. Many respondent comments emphasized qualities such as the efficiency, cost‐effectiveness, and user‐friendliness of the RMT model.
As noted earlier, the growth of REDCap across the enterprise has resulted in a corresponding growth of the RMT. From 6.5 FTEs servicing 30 individual research projects within the Duke School of Nursing in 2009, the RMT has grown 14 FTEs and is expected to service more than 180 projects in FY 2012 ( Figure 2 ). Although the majority of this work was initially for individual investigators with funded research projects, over the past 2 years a departmental‐based model has begun to develop, especially for groups with significant needs for legacy data cleaning and conversion for internally funded research: for instance, in FY 2012, 66% of the projects served in this model used internal funding mechanisms (departmental/discretionary funds) while 34% used external funding mechanisms (NIH/sponsor/agency grants). This department‐based model has proven especially useful for projects initiated by residents or research fellows.
Figure 2.
Growth of research management team utilization at Duke, FY 2008–FY 2012.
The Duke RMT model and supporting documents have been shared with at least 10 institutions, usually following formal presentation at CTSA consortium meetings (see Supplemental Material). RMT staff have also taken leadership roles in several domains within the national REDCap consortium: (1) Validation/testing; (2) Bug identification and fixes; (3) French translation; (4) Application Programming Interface (API); (5) REDCap Library Oversight Committee (REDLOC) and (6) Data Security. This adoption and sharing of centrally managed SOPs, templates, and documentation procedures has not only benefited Duke investigators, but more importantly, our involvement has benefited external institutions participating in the REDCap consortium.
Investigators often plan and submit grants applications based on promising research ideas with specific, measurable aims. As part of this process, they typically create a budget fitted to the scope of work and, upon obtaining funding, secure personnel capable of completing the work. However, there is often an unrecognized but essential set of skills and knowledge that resides between the scientific expertise of the researcher and the specific operational skills of the study staff. The RMT system, which blends regulatory oversight, expertise in research methodology, database design, and data management, works in tandem with statistical expertise within a percentage effort model to allow flexible, ad hoc use of essential components of individual skillsets.
Benefits of the RMT Model
Strengths of the Duke RMT model include (1) potential cost efficiency, achieved through realized through centralized interviewing, hiring, onboarding, research training, managing and replacing employees, as well as providing coverage for leaves of absence; (2) maintenance of institutional knowledge; (3) improved standardization, which improves consistency, and efficiency, achieved by matching tasks to employee expertise; and (4) efficient, compliant data capture, storage and analysis. The RMT model also expands the availability of sophisticated research logistics knowledge base to all institutional investigators. Further, the RMT model allows for adoption of a data‐oriented framework that includes: (1) identification of data to be collected, (2) defined data elements, (3) measuring and observing values, (4) recording these measurements and observations, (5) processing data electronically in order for analysis to occur, and (6) data analysis. After the completion of analysis, data are disseminated and can be shared. 20 Improvements in the data framework can be associated with all projects with an eye towards increased results dissemination and improved funding potential.
Limitations of the RMT Model
The RMT model has a number of limitations that should be noted. Managing percentage effort among multiple employees and across multiple projects on a monthly basis is complex, and some “backstop” funding must be in place so that investigators do not incur costs for overhead expenses related to unallocated effort during any given month. Likewise, many investigators strongly prefer the appearance of complete supervisory control of employees and may resist the concept of contracting employee time from a person managed by another group. The management cost of the model, both in employee oversight and in educating investigators may not be sustained in this model, and requires greater focus on future funding on the part of principal investigators. Further, the RMT approach to data management requires institutional support and investment. Finally, the percentage effort model described in this manuscript has been tested only in smaller operations (up to14 FTEs) and the administrative cost has been about 25–30% of the total effort across employees. There may be a threshold at which this model is no longer efficient and a core resource with employees sustained by budgeted resources may be necessary.
Next Steps
The RMT will continue to expand services at Duke using REDCap and other research tools. This effort model, with its emphasis on sustainable support, carefully selected staff matched to the needs of study teams, and a strategy of careful, deliberate growth, has the potential to augment institutional efficiency both through cost savings and by retaining skilled and talented personnel. Furthermore, the additional opportunity to provide mentorship and a fully supportive research environment for Duke trainees (such as those completing fellowships) and young investigators provides unique opportunities for career development in operationalizing and managing research that are not found elsewhere. Such mentorship would include advising from senior faculty clinicians and development of measurable research questions, training in data management best practices, dissemination of publications, and grant development. Ultimately, this may result in a generation of clinicians who are better prepared to develop, manage, and lead clinical and translational research.
Conclusions
By combining REDCap, a validated, low‐cost data capture tool with built‐in compliance components, with the existing RMT model of comprehensive, flexible research support infrastructure, we have decreased disparities in research support across funding mechanisms and achieved high initial rates of customer satisfaction across our institution. The RMT model has the potential for adoption throughout the CTSA consortium and other academic health and science systems, and may serve as a vehicle for providing cost‐effective improvements to the quality and security of clinical and translational research conducted at these institutions.
Sources of Funding
The Research Management Team is partially supported by the Duke School of Nursing and the Duke Translational Medicine Institute, made possible through CTSA Grant Number UL1RR024128‐01 from the National Center for Research Resources (NCRR), a component of the National Institutes of Health (NIH), and NIH Roadmap for Medical Research. The contents of this paper are solely the responsibility of the authors and do not necessarily represent the official views of the CTSA or the NIH.
Conflict of Interest
Dr. Tcheng reports working relationships (i.e., consulting, research, and/or educational services) with the following companies and organizations: the American Board of Internal Medicine, the American College of Cardiology Foundation, Faculty Connection, Ischemix, Inc., and Philips Medical Systems. None of the remaining authors has any potential conflicts of interest to disclose.
Supporting information
Figure S1. Screenshot of access database switchboard. Some text is hidden.
Figure S2. Screenshot of access database table relationship diagram.
Supporting info item
Acknowledgments
The authors wish to thank Jonathan McCall, M.S., for editorial assistance with this manuscript; Mr. McCall is an employee of the Duke Clinical Research Institute, Durham, NC and received no compensation other than his salary for work performed on this manuscript. We also thank David Bowersox, M.B.A., Robert Califf, M.D., Leslie Fife, Catherine Gilliss, D.N.Sc., R.N., F.A.A.N., Jane Halpin, Paul Harris, Ph.D., Diane Holditch‐Davis, Ph.D., R.N., F.A.A.N., Angie House, M.B.A., Aenoch Lynn, M.S., Rebbecca Moen, M.B.A., Deborah Roth, M.P.H., Glenn Setliff, M.S., Jessica Tenenbaum, Ph.D., Robert Taylor, Megan Tirpak, Robbin Thomas, Mitchell Vann, Selnatta Vereen and Megan Williams, M.S.P.H., M.S.W., for supporting the vision and infrastructure for establishing a successful RMT. Finally, we thank RMT members Teresa Baker, Alana Bennett, Mark Bettger, M.B.A., John Boling, M.S., Ceci Chamorro, Nancy Hassell, Jeffrey Hawley, Jessica Houlihan, Mindy Kash, Phyllis Kennel, Carol Pereira, Rick Sloane, M.P.H., Jessica Warren, and Carolyne Whiting.
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Supplementary Materials
Figure S1. Screenshot of access database switchboard. Some text is hidden.
Figure S2. Screenshot of access database table relationship diagram.
Supporting info item