Priority aims and testable hypotheses in implementation science: a scoping review

Despite substantial investment over two decades, implementation science has struggled to produce the comprehensive, empirically supported theories needed to predict which strategies will work in which contexts. To address this gap, we developed the Priority Aims and Testable Hypotheses (PATH) tool, which organizes implementation research around three causal pathways (priority aims) linking implementation strategies, evidence-based measures of implementation (EBMIs), and health and health-related outcomes (HHROs). The tool further categorizes implementation research according to four hypothesis types based on what is evaluated (effectiveness or cost-effectiveness) and how strategies are compared (superiority or non-inferiority). Mapping which relationships and hypotheses have been tested can clarify where evidence is well-developed and where gaps may constrain theory development, limiting the field’s capacity to inform implementation decisions. We conducted a scoping review of articles published through December 2024 in Implementation Science , Implementation Science Communications , and Implementation Research and Practice , searched via PubMed Studies were dual-coded to determine if they tested at least one eligible hypothesis along at least one causal pathway to be included as PATH A (implementation strategies → EBMIs), PATH B (EBMIs → HHROs) or PATH C (implementation strategies → HHROs). Among 1,387 eligible studies, 219 (15.8%) tested at least one PATH relationship. Coverage varied substantially across aims: 187 studies (85.4%) tested PATH A, 76 (34.7%) tested PATH C, and five (2.3%) tested PATH B. Effectiveness superiority hypotheses predominated (98.2%); cost-effectiveness and non-inferiority hypotheses were uncommon (cost-effectiveness superiority: 6.8%; effectiveness non-inferiority: 0.9%; cost-effectiveness non-inferiority: 0.5%). The PATH tool helps identify opportunities to strengthen the explanatory foundation of implementation research. Expanded testing of EBMIs as predictors of HHROs (PATH B) would support assessment of population health impact and help distinguish implementation effects from intervention effects, a prerequisite for causal implementation theory. Greater use of cost-effectiveness and equivalence designs would generate evidence on resource requirements, feasibility, and acceptable tradeoffs—information often essential for policy and practice decisions. Aligning study designs with PATH-informed priorities, including EBMI validation, economic evaluation, and pragmatic non-inferiority trials, may enhance the field’s capacity to inform real-world implementation choices.