Applying a Capability Maturity Model (CMM) to evaluate global health security-related research programmes in under-resourced areas. Issue 1 (1st January 2017) - Yeh, Kenneth B.; Adams, Martin L.; Marshall, Edith S.; Dasgupta, Debanjana; Zhunushov, Asankadyr; Richards, Allen L.; Hay, John
Abstract: Organisations in under-resourced areas that achieve long-term research sustainability by successfully competing for research funding will not only build their reputation for conducting quality science but also develop their human resources in a manner that reduces the risk of becoming a future security threat. Major challenges to these organisations include identifying and prioritising funding opportunities, securing and administering external grant awards and publishing both the outcomes of research and relevant surveillance data. Lack of a standardised evaluation technique to assess institutional research capabilities poses challenges for identifying and targeting specific, repeatable processes that lead to organisational improvements. Short- and long-term goals, which are challenged by research quality, funding and human resources, need to be established in order to achieve complex missions such as reducing global health security threats. Once baseline capabilities are established, a consistent evaluation technique provides an objective view to complement other steps that enhance capabilities. The capability maturity model, which is often used in business and technology sectors for establishing life cycle and planning sustainment, is a technique that enhances performance by defining three levels of capability (initial, managed and optimised). An organisation can assess its current state of capability ('as is') and develop an actionable strategy for its next progression ('to be'). In addition, application of a CMM aids creation of a strategy for realising a more repeatable and optimised process. Research programmes frequently rely on basic metrics such as the number of peer-reviewed publications and grant funding awards to measure their quality. Our analysis suggests an approach that includes references and tools, especially those that are risk-based, which can be used to establish initial best practices, define metrics, measure outputs and rates of success in a stepwise manner. In addition, we provide a pilot example from a survey of research institutes in under-resourced areas.