Background 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. Purpose In this study we modeled time-to-event data to explore relationships between clinical trial protocol development and implementation times, as well as identify potential correlates of prolonged development and implementation. Methods We obtained time interval and participant accrual data from 111 interventional clinical trials initiated between 2006 and 2011 by NIH’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. Results 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 2 ½ years) and implementation times (>3years). We also found that protocols exceeding the median number of days for completing the development interval had significantly longer implementation. Limitations 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. Conclusions 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 reengineering efforts that could lead to reductions in the time from clinical trial concept to results, thereby accelerating progress in clinical research.

The National Institute of Allergy and Infectious Diseases (NIAID) Division of AIDS (DAIDS) Enterprise Information System (DAIDS-ES) is a web-based system that supports NIAID in the scientific, strategic, and tactical management of its global clinical research programs for HIV/AIDS vaccines, prevention, and therapeutics. Different from most commercial clinical trials information systems, which are typically protocol-driven, the DAIDS-ES was built to exchange information with those types of systems and integrate it in ways that help scientific program directors lead the research effort and keep pace with the complex and ever-changing global HIV/AIDS pandemic. Whereas commercially available clinical trials support systems are not usually disease-focused, DAIDS-ES was specifically designed to capture and incorporate unique scientific, demographic, and logistical aspects of HIV/AIDS treatment, prevention, and vaccine research in order to provide a rich source of information to guide informed decision-making. Sharing data across its internal components and with external systems, using defined vocabularies, open standards and flexible interfaces, the DAIDS-ES enables NIAID, its global collaborators and stakeholders, access to timely, quality information about NIAID-supported clinical trials which is utilized to: (1) analyze the research portfolio, assess capacity, identify opportunities, and avoid redundancies; (2) help support study safety, quality, ethics, and regulatory compliance; (3) conduct evidence-based policy analysis and business process re-engineering for improved efficiency. This report summarizes how the DAIDS-ES was conceptualized, how it differs from typical clinical trial support systems, the rationale for key design choices, and examples of how it is being used to advance the efficiency and effectiveness of NIAID's HIV/AIDS clinical research programs.

Community-based active TB case finding (ACF) has become an essential part of TB elimination efforts in high-burden settings. In settings such as the state of Kerala in India, which has reported an annual decline of 7.5% in the estimated TB incidence since 2015, if ACF is not well targeted, it may end up with a less-than-desired yield, the wastage of scarce resources, and the burdening of health systems. Program managers have recognized the need to optimize resources and workloads, while maximizing the yield, when implementing ACF. We developed and implemented the concept of ‘individuals’-vulnerability-based active surveillance’ as a substitute for the blanket approach for population/geography-based ACF for TB. Weighted scores, based on an estimate of relative risk, were assigned to reflect the TB vulnerabilities of individuals. Vulnerability data for 22,042,168 individuals were available to the primary healthcare team. Individuals with higher cumulative vulnerability scores were targeted for serial ACF from 2019 onwards. In 2018, when a population-based ACF was conducted, the number needed to screen to diagnose one microbiologically confirmed pulmonary TB case was 3772 and the number needed to test to obtain one microbiologically confirmed pulmonary TB case was 112. The corresponding figures in 2019 for individuals’-vulnerability-based ACF were 881 and 39, respectively. Individuals’-vulnerability-based active surveillance is proposed here as a practical solution to improve health system efficiency in settings where the population is relatively stationary, the TB disease burden is low, and the health system is strong.