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A framework to improve the global ecosystem for impact

Published onApr 25, 2024
A framework to improve the global ecosystem for impact

The “evidence ecosystem for impact” framework includes the two domains of evidence creation and evidence application. The evidence creation domain is represented by the evidence creation funnel (Fig. 2.1) and the evidence application domain by the policy/action cycle (Fig. 2.2), which is based on Graham’s knowledge-to-action cycle [1],[63]. The evidence funnel is located at the centre of the policy/action cycle and both domains are enveloped by an outer layer representing the context within which decisions are made. Also featured in the “evidence ecosystem for impact” framework (as shown in Fig. 2.2) are two cross-cutting activities (capacity-building, and continuous communication) and six principles (use of systematic and transparent processes, continuous improvement processes, needs-based approaches, inclusiveness, integration, and equity) that will help to ensure the greatest impact on global health and equity.

Ideally, research producers and users work collaboratively through the processes of evidence creation and the policy/action cycle to create linkages and exchange between the two and facilitate research use.

While the phases in both the evidence creation and policy/action cycle domains appear linear, the process of KT is, in reality, rather iterative, complex and dynamic [63]. The boundaries between the two domains and their phases are permeable, and phases can, at times, be omitted. For example, primary research can directly inform phases of the policy/action cycle, e.g. to determine the magnitude of the problem, assess barriers to implementation and to evaluate outcomes. Furthermore, the policy/action cycle can be used to generate new evidence (e.g. on barriers and facilitators, public perception, and innovations in implementation), and to change or refine the interpretation of evidence. In addition, evidence gaps can be identified that require further research to meet the needs of policy or practice decisions. Also, the evaluation of policies and actions, their impact and consequences, is a major source of evidence for future decision-making about interventions/solutions.

The terms policy cycle and action cycle are not interchangeable. Both terms are included here to explicitly encompass “macro decisions” (e.g. a new national health policy) and “micro decisions” (e.g. a case management recommendation for a clinician), as well as decisions of health-care organizations (such as a hospital) that operate between the micro level of clinical practice and the macro level of health policy [63](p. 212). With these nuances, the domains in the policy/action cycle help to achieve a better understanding of all of these types of decisions.

Ideally, research producers and users work collaboratively through the processes of evidence creation and the policy/action cycle to create linkages and exchange between the two and facilitate research use [48]. Such a co-production approach allows tailoring of the research questions to the problems identified by users and customization of the evidence products, including choosing the right communication channels to reach specific user audiences.

Among the areas of work that are part of the evidence funnel and used in the policy/action cycle, seven workstreams within the three levels of WHO and at country level have been identified as key in view of providing complementary types of evidence and supports for decision-making (see Table 2.1). Two of these work with existing data (data analytics and modelling initiatives), three with synthesized research evidence (guidelines, HTAs and evidence-informed policy-making support such as EBPs), and two with generation of new research (implementation research and evaluation).

At the WHO level, strengthening relations between the different workstreams help to promote exchange of innovations, experiences and resources for the overall advancement of EIDM. Coordinating efforts help equally at the country level, in particular, since these initiatives and programmes, in part, compete for the same limited local resources (financial and technical), in view of the overlap of methods and in the technical knowledge required for the development of many evidence products [69]. For instance, the development and adaptation of both guidelines and HTAs require sound technical skills and a good understanding of systematic reviews and economic modelling.

Evidence workstreams

Workstreams can be thought of as areas of work, rather than as necessarily representing a specific department or unit within WHO. They can also be thought of as types of evidence produced that are useful for EIDM. Some of the areas of work/types of evidence produced may be undertaken by more than one department or unit. See Table 2.1 and glossary for definitions of each workstream.

Table 2.1. Different workstreams involved in producing evidence for decision-making

Source: adapted from El-Jardali et al. 2018, Lavis et al. 2018, and McMaster Health Forum 2021, exhibit 4.2 [65],[66],[67].

* May also rely on existing evidence products (versus evidence products produced de novo).

* Definitions from the Global Commission on Evidence to Address Societal Challenges [67], exhibit 4.2 with minor modifications. Definition for evidence-informed policy-making supports adapted from EVIPNet Europe 2017 [23].


WHO technical products on norms/standards, data and research

Technical products (TPs) are part of WHO’s public health goods, relevant to multiple countries to drive impact and the achievement of the GPW13 “Triple Billion” targets. TPs fall into the areas of standards, data and research, innovation and horizon scanning and are developed based on rigorous processes. Examples of TPs include WHO guidelines, WHO guidance on research areas, frameworks, repositories and platforms. The selection phase of TPs takes place prior to every new biennium and is informed by the priorities included in the strategic planning for the programme budget. The related life-cycle consists of five phases: (i) selection, (ii) development, (iii) quality assurance, (iv) implementation, and (v) measurement and monitoring. The Department of Quality, Norms and Standards (QNS), Science Division, serves as focal point for all quality assurance issues. Moreover, QNS is currently working on developing a typology for these types of products and supporting better use of evidence in their development [68].

While optimizing resources, integrating complementary types of support for EIDM is also likely to offer opportunities for the different workstreams to learn from each other to fine-tune their respective products.

Given the overlap in methods, technical knowledge and resources required, bringing the different workstreams closer together at a national level could, depending on the context, help to optimize resources. One way of doing this is by applying an “integrated multiconcept approach” to create synergistic effects to build national institutional capacity for EIDM [69]. For example, in several European countries, HTA agencies have traditionally also been involved in developing clinical practice guidelines [70].

The nascent trend to integrate different and complementary types of support for decision-making could also be further promoted, in particular, in countries embarking on setting up institutional mechanisms in this field. For example, a country embarking on HTA could reconsider establishing separate agencies for HTA, guideline development and evidence-informed policy-making [70]. While optimizing resources, this integration is also likely to offer opportunities for the different workstreams to learn from each other to fine-tune their respective products [70],[71]. For example, the use of policy dialogues to integrate the evidence with the views, experiences, and tacit knowledge of those who will be affected by future decisions may also offer advantages for HTAs [71].

While optimizing resources, integrating complementary types of support for EIDM is also likely to offer opportunities for the different workstreams to learn from each other to fine-tune their respective products.

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