Shuang Liang

BackgroundImplementation science often involves complex causal relationships shaped by multiple interacting conditions (e.g., barriers, enablers, and implementation strategies) and co-occurring outcomes. Traditional regression-based methods, which typically analyse quantitatively measured implementation constructs, may be limited in identifying such complexity. Configurational comparative methods (CCM), including Qualitative Comparative Analysis (QCA), Coincidence Analysis (CNA), and Combinational Regularity Analysis (CORA), offer alternative approaches, but their comparative performance in implementation science remains unclear.MethodsThis study compared QCA, CNA, and CORA using three published public health datasets and a large-scale simulation, covering a single-outcome dataset on HPV vaccination uptake, and two multi-outcome datasets, one examining diabetes and depression and the other examining traffic accidents and self-inflicted injuries. The simulation generated 1,000 causal structures across three sample sizes and five noise levels, resulting in 15,000 datasets to simulate different implementation scenarios. Method performance was evaluated using structural recovery metrics (error-freeness, correctness, completeness, no-model rate) and solution quality metrics (consistency and coverage).ResultsIn the single-outcome analysis, all three methods produced identical solutions. In multi-outcome settings, CORA uniquely identified shared causal structures (shared combinations of conditions associated with more than one outcome), while QCA and CNA used separate analyses. In the simulation study, CNA achieved the highest error-freeness (up to 1.00) but frequently failed to produce solutions. QCA achieved perfect correctness and completeness (1.00) at n = 1000 under 0% noise. CORA maintained high correctness under low-noise conditions across sample sizes (0.83–0.91) but was limited by computational constraints under high-noise conditions. Consistency and coverage revealed different performance patterns across methods.ConclusionsNo single method is universally superior. Method selection should depend on sample size, data quality, and outcome multiplicity. CORA is advantageous for multi-outcome analysis, QCA is most productive for larger datasets, and CNA conservatively prioritises avoiding incorrect conclusions. These findings provide practical guidance for implementation scientists in selecting appropriate CCM methods based on sample size, data quality, and whether multiple outcomes are being analysed.

Background/Objectives: Effective nutritional care is a critical component of oncology treatment. Behaviour change techniques (BCTs) are key elements that drive individual behaviour change and are commonly identified within clinical interventions and implementation strategies. Despite their relevance, the application of BCTs in oncology nutrition has not been systematically examined. This systematic review aimed to identify and synthesise the use of BCTs in nutrition interventions and their implementation strategies within adult oncology care. Methods: A search across 10 electronic databases was conducted from inception up to December 2024. Data extraction focused on intervention characteristics, plus implementation barriers, strategies, and outcomes, which were coded using relevant established taxonomies, i.e., Theoretical Domains Framework, Behaviour Change Techniques, and Proctor’s outcomes framework. Study quality was assessed using the QuADS tool. Results: After screening 4055 abstracts and 165 full texts, 38 articles representing 31 unique studies were included. Nutrition interventions (n = 31) spanned across risk screening/assessment, care planning/pathways, nutritional monitoring, and support guidelines. Among the 19 interventions that incorporated BCTs targeting patients and/or healthcare professionals (HCPs), 20 unique BCTs were identified across 97 instances. Separately, implementation strategies reported in 25 of the 38 articles included 96 BCT instances (18 unique) targeting HCPs. BCTs were frequently observed alongside positive outcomes across patient, service, and implementation levels. Conclusions: Application of BCTs in oncology nutrition practice remains implicit and inconsistent. For clinical practice, more explicit specifications of BCTs may support better integration of nutrition care in routine oncology settings. Future research is warranted to test the effectiveness of specific BCTs and their combinations. This can be supported by standardised reporting of intervention content and implementation strategies which can enable identification of patterns of success and optimise replicability.

Implementation science aims to bridge the gap between research evidence and routine healthcare practice by understanding and optimizing the integration of evidence-based interventions (EBIs). In this paper, we identify seven persistent challenges limiting implementation progress, including (1) overwhelming volume of implementation materials (e.g., reports, interviews, surveys), (2) contextual variability, (3) complex interactions among contextual factors, interventions, and outcomes, (4) stakeholder engagement constraints, (5) equity and access barriers, (6) insufficient or biased data, and (7) concerns around data security, trust, and ethics. We explore how advances in data science and artificial intelligence (AI) offer promising solutions to these challenges by enhancing evidence extraction and synthesis, contextual analysis, stakeholder engagement, and adaptation throughout the implementation process. Using a live project in precision oncology as a practical example, we demonstrate how AI tools, such as large language models (LLMs), clustering algorithms, and sentiment analysis, can support implementation research and practice, from concept (e.g., barriers, facilitators, strategies) extraction and barrier identification to process mapping. We also introduce ImpleMATE, an AI-enabled platform that integrates implementation science knowledge with dynamic learning health system workflows, enabling continuous knowledge extraction, decision support, and feedback for implementation improvement. While AI offers significant potential to accelerate and scale implementation efforts, we emphasize the need for ethical oversight, transparency, and human collaboration to ensure responsible, equitable, and impactful application in practice.

Non-communicable diseases (NCDs) are a major global health and economic burden. Precision medicine has merged as a promising approach for treating NCDs by tailoring health interventions to individual profiles. Despite rapid advances, its effective implementation remains slow. This systematic review aimed to examine the implementation landscape for delivering precision medicine interventions, services, or programs for NCDs, focusing on barriers, facilitators, strategies, and outcomes. Comprehensive searches were performed in PubMed, Embase, CINAHL, and Web of Science until November 2024. The search strategy comprised of four key concepts, i.e. precision medicine, NCDs, implementation, and public health. Articles published from 2014 onwards were included. Extracted data included study characteristics, populations, precision medicine intervention details, and information related to implementation. Relevant data were coded to implementation science frameworks and taxonomies. These included the updated Consolidated Framework for Implementation Research (CFIR 2.0), Expert Recommendations for Implementing Change (ERIC), and Proctor’s outcomes framework. The QuADs tool was used to assess the risk of bias of included studies. A total of 10,039 records were identified, with 8,684 abstracts and 463 full texts screened. Of these, 68 studies met the inclusion criteria. While 64 studies reported implementation determinants [i.e. barriers (n = 61) and/or facilitators (n = 40)], approximately two-thirds proposed or implemented strategies that were primarily based on intuition. Key barriers identified included ‘access to knowledge and information’ (n = 34) and ‘work infrastructure’ (n = 21) within the Inner Setting, and ‘financing’ (n = 20) within the Outer Setting. While financial burdens were clearly attributed to patients and healthcare settings, there was less clarity and strategic direction regarding potential funding sources to support implementation strategies. Implementation Outcomes were reported in 46 instances, with Adoption being the most frequent. Service Outcomes, particularly Effectiveness, were generally positive, as were patient outcomes, primarily, mortality and morbidity. Despite growing interest in precision medicine for treating NCDs, implementation evidence remains theoretically fragmented, making it difficult to identify implementation success patterns. Future efforts adopting targeted strategies and robust outcome evaluation, guided by implementation science, are needed to support effective integration of precision medicine into routine healthcare.

Introduction Genomic diagnostics have accelerated therapeutic and preventative breakthroughs in oncology and cancer genetics. Despite increased access, the implementation of genomics-based care faces serious fragmentation and scalability issues due to a lack of system support. The Precision Care Initiative aims to develop a novel and scalable Precision Care Clinic (PCC). It is designed to coordinate precision medicine in oncology and streamline decision support for referring oncologists and geneticists. The PCC will enhance quality of care through multifaceted, patient-centred communication. It will also improve translational capacity by integrating team expertise in precision oncology, implementation science, clinical informatics, cancer genetics, health economics and patient-reported measures. Methods and analysis This programme uses a type I and type II hybrid effectiveness-implementation trial design sequentially. The complex clinical intervention is precision oncology—matching the targeted treatment or risk management strategy to the right patient, based on their genomic, cancer staging, environmental, lifestyle and biological characteristics, etc. The service intervention is the PCC, providing centralised multidisciplinary review to facilitate shared decision-making with clinicians for the provision of optimal precision oncology care for their patients. The implementation intervention is the co-designed implementation platform—applying evidence-based implementation approaches and Learning Health System principles to enhance feasibility and sustainability. All adult patients across Australia referred to the PCC (n=est. 100–150/year), and healthcare professional interest holders involved in the delivery of precision oncology services, are eligible to participate. Over the study course, phase I involves using a mixed-methods approach to inform iterative co-design and pilot testing of the first PCC with an accompanying implementation platform, and a suite of outcome measures to assess effectiveness; phase II (hybrid type I) includes the implementation of the PCC and evaluation of the outcome measures designed in phase I; phase III (hybrid type II) involves a co-design of local adaptations and testing the effectiveness of the PCC model nationally. Ethics and dissemination The study received ethical approval from the St Vincent’s Hospital Human Research Ethics Committee (2023/ETH00373). Study results will be presented at relevant conferences and published in peer-reviewed journals. Trial registration number NCT06077110

Introduction Cardiac rehabilitation is known to reduce morbidity and improve quality of life in people living with heart disease, however, adherence, access and completion of these programmes is suboptimal. Peer support may offer an opportunity to close this service gap. The aim of the study is to determine whether the effectiveness of a digital peer support programme for people living with heart disease is effective in improving social connectedness, clinical and patient-reported outcomes and experience measures. Methods and analysis Heart2Heart is a community-based randomised controlled trial with 6 months follow-up for the primary outcome and 6 and 12 months for secondary outcomes. Approximately 752 adults with a diagnosis of heart disease in the past 12 months will be recruited from the general community and Australian cardiac rehabilitation programmes. Control group will participate in usual care, while intervention group will have access to a 6 months intervention that enables peer support via an interactive mobile application, in addition to usual care. The intervention includes online discussion groups, access to resources and facilitated conversations with health professionals. Primary outcome is social connectedness at 6 months follow-up. Secondary outcomes (6 and 12 months) will be all-cause/cardiovascular disease hospital admissions, all-cause mortality, lifestyle (sufficiently physically active, not smoking, sufficient fruit and vegetable consumption), proportion taking prescribed medications and health service utilisation (medical appointments, cardiac rehabilitation, participation in any other in-person peer support activities). Patient-reported outcome and experience measures including self-efficacy, quality of life, satisfaction and programme engagement will be analysed at 6 months. Process measures will include application analytics, barriers and facilitators to engagement with the intervention from participant’s perspective. An intention-to-treat analysis will be used. Ethics and dissemination Ethical clearance was obtained from Western Sydney Local Health District Ethics Committee. Heart2Heart has potential to improve social connectedness and provide a valuable addition to traditional cardiac rehabilitation. Trial registration number ACTRN12624000386538.

Quantitative rankings of multiple dietary patterns for their effects on non-communicable disease (NCD) biomarkers is lacking and would inform primary prevention strategies. Accordingly, a network meta-analysis (NMA) was conducted to compare and rank the effects of different dietary patterns on NCD biomarkers, and associations of dietary patterns’ underlying macronutrient composition with NCD biomarkers were determined by a nutritional geometry approach. Randomised controlled trials (RCTs) were eligible for inclusion if they enrolled healthy participants, employed food-based dietary pattern interventions without energy restriction, and reported NCD biomarker outcomes. NCD biomarkers were included as an outcome if ≥10 trials were available. A systematic search of five electronic databases identified 4008 records. Sixty-eight articles from 59 RCTs reporting lipids, glycemic, and inflammatory biomarkers were included for quantitative syntheses. Risk-of-bias was predominantly categorized as low or having some concerns, and confidence-of-evidence low. Relative to western habitual diet, the Mediterranean, Dietary Approaches to Stop Hypertension (DASH), dietary guidelines-based, plant-based, and low-fat diets reduced low-density lipoprotein cholesterol (mean difference range: −0.29 to −0.17 mmol/L), total cholesterol (−0.36 to −0.24 mmol/L), and apolipoprotein B (−0.11 to −0.07 g/L) (all p < 0.05); the Paleo, plant-based and dietary guidelines-based diets reduced homeostasis model assessment of insulin resistance (−0.95 to −0.35, all p < 0.05). No dietary pattern ranked consistently highest. The Paleo diet received the highest all-outcomes-combined average Surface Under the Cumulative Ranking Curve value (67%), followed by DASH (62%) and Mediterranean diets (57%), whereas western habitual diet was lowest (36%). Our findings were independent of macronutrient composition, highlighting the significance of dietary pattern-level analysis.