Protocol Life Cycle Research Articles

4575 Implementing a Workflow Management Tool for Clinical Trials

OBJECTIVES/GOALS: A workflow management tool is essential in order to help support consistent processes with transparency in next steps of the study process. Prior to this tool, staff has relied upon extensive training and coaching on the study process. While resources and guidelines exist, it requires additional time for staff to identify these resources and allows for confusion and rework. Implementation of a systematic workflow management tool was identified as a critical need in order to support streamlined processes, improve transparency and support business continuity, and to accelerate the study process. METHODS/STUDY POPULATION: This effort was undertaken as part of the Protocol Lifecycle Management effort to implement a comprehensive clinical trial management system for clinical research studies. Mayo Clinic has designed a workflow management tool within the Velos eResearch system. The workflow manager is dynamic and will present specific activities based on the study design and responses to data entered on the ad hoc forms. A Workflow Build group contributed to the design of the workflow in order to reflect appropriate, current operational processes. The workflow was vetted and validated with research teams. In addition to designing activities, planned dates and target timelines were established for relevant workflows to help promote transparency in the study start-up timelines and allow study staff to identify overdue activities. Study status controls were designed in the workflow to protect study staff from inadvertently changing the status until appropriate activities are complete. RESULTS/ANTICIPATED RESULTS: A dynamic workflow has been designed and implemented in the Velos eResearch system to support Mayo Clinic research sites. This system will be implemented February 24, 2020 to all consenting studies. DISCUSSION/SIGNIFICANCE OF IMPACT: The implementation of this workflow management tool is critical to help support research operations in a large, academic medical center. Benefits to implementation are expected to include improved transparency in the study status and next steps, reductions in rework due to confusion in next steps, better understanding from new staff in the appropriate study process, and improved timelines for study start-up. As we prepare for the implementation of the Velos eResearch system at Mayo Clinic, the workflow management tool has been identified in training sessions as a positive benefit.

Evaluating protocol lifecycle time intervals in HIV/AIDS clinical trials.

Identifying efficacious interventions for the prevention and treatment of human diseases depends on the efficient development and implementation of controlled clinical trials. Essential to reducing the time and burden of completing the clinical trial lifecycle is determining which aspects take the longest, delay other stages, and may lead to better resource utilization without diminishing scientific quality, safety, or the protection of human subjects. In this study, we modeled time-to-event data to explore relationships between clinical trial protocol development and implementation times, as well as to identify potential correlates of prolonged development and implementation. We obtained time interval and participant accrual data from 111 interventional clinical trials initiated between 2006 and 2011 by National Institutes of Health's HIV/AIDS Clinical Trials Networks. We determined the time (in days) required to complete defined phases of clinical trial protocol development and implementation. Kaplan-Meier estimates were used to assess the rates at which protocols reached specified terminal events, stratified by study purpose (therapeutic, prevention) and phase group (pilot/phase I, phase II, and phase III/IV). We also examined several potential correlates to prolonged development and implementation intervals. Even though phase grouping did not determine development or implementation times of either therapeutic or prevention studies, overall we observed wide variation in protocol development times. Moreover, we detected a trend toward phase III/IV therapeutic protocols exhibiting longer developmental (median 2½ years) and implementation times (>3 years). We also found that protocols exceeding the median number of days for completing the development interval had significantly longer implementation. The use of a relatively small set of protocols may have limited our ability to detect differences across phase groupings. Some timing effects present for a specific study phase may have been masked by combining protocols into phase groupings. Presence of informative censoring, such as withdrawal of some protocols from development if they began showing signs of lost interest among investigators, complicates interpretation of Kaplan-Meier estimates. Because this study constitutes a retrospective examination over an extended period of time, it does not allow for the precise identification of relative factors impacting timing. Delays not only increase the time and cost to complete clinical trials but they also diminish their usefulness by failing to answer research questions in time. We believe that research analyzing the time spent traversing defined intervals across the clinical trial protocol development and implementation continuum can stimulate business process analyses and re-engineering efforts that could lead to reductions in the time from clinical trial concept to results, thereby accelerating progress in clinical research.

Integrating utilization-focused evaluation with business process modeling for clinical research improvement.

New discoveries in basic science are creating extraordinary opportunities to design novel biomedical preventions and therapeutics for human disease. But the clinical evaluation of these new interventions is, in many instances, being hindered by a variety of legal, regulatory, policy and operational factors, few of which enhance research quality, the safety of study participants or research ethics. With the goal of helping increase the efficiency and effectiveness of clinical research, we have examined how the integration of utilization-focused evaluation with elements of business process modeling can reveal opportunities for systematic improvements in clinical research. Using data from the NIH global HIV/AIDS clinical trials networks, we analyzed the absolute and relative times required to traverse defined phases associated with specific activities within the clinical protocol lifecycle. Using simple median duration and Kaplan-Meyer survival analysis, we show how such time-based analyses can provide a rationale for the prioritization of research process analysis and re-engineering, as well as a means for statistically assessing the impact of policy modifications, resource utilization, re-engineered processes and best practices. Successfully applied, this approach can help researchers be more efficient in capitalizing on new science to speed the development of improved interventions for human disease.

Wireless Authentication in chi-Spaces

The chi-Spaces framework provides a set of tools to support every step of the security protocol's life-cycle. The framework includes a simple, yet powerful programming language which is an implementation of the Security Protocol Language (SPL). SPL is a formal calculus designed to model security protocols and prove interesting properties about them. In this paper we take an authentication protocol suited for low-power wireless devices and derive a chi-Spaces implementation from its SPL model. We study the correctness of the resulting implementation using the underlying SPL semantics of chi-Spaces.