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IntroductionThe National Institute for Health and Care Excellence (NICE) is determined to make better use of health and social care data in the development of its guidance. Real-world data (RWD) has the potential to significantly improve our understanding of the value of new and existing health and social care interventions. RWD is already widely used to characterize populations, interventions, and outcomes and to populate economic models, but its use in estimating the effects of interventions remains limited, especially for medicines. Key barriers to its greater use in this context include limited transparency around studies, sometimes a lack of confidence in their integrity, and methodological concerns around how studies have addressed major sources of potential bias.MethodsThis abstract focuses on the real-world evidence (RWE) framework developed by NICE to support its ambitions. The framework was developed in an iterative way based on: reviews of best practice approaches to the conduct and assessment of real-world evidence studies; case studies; and workshops with key external stakeholders. The initial version of this living framework focuses on de novo RWE studies using individual patient data.ResultsThe RWE framework consists of an overarching research governance framework which describes expectations around the planning, conduct, and reporting of RWE studies across uses of real-world data. Uses are categorized by risk according to their importance to decision-making, the impact of decisions on patient and system outcomes, and their complexity as proxied by risk of bias. Studies of the effects of interventions on patient health and system outcomes are considered the highest risk. The research governance framework is supported by a tool to aid assessment of data suitability for its intended application, and detailed guidance on the conduct and reporting of comparative effect studies using RWD, following the target trial approach.ConclusionsThe RWE Framework underpins NICE’s ambitions to make better use of RWD in its guidance and is intended to improve the quality and utility of RWE studies submitted to NICE enabling more consistent and appropriate evaluation.

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IntroductionThe National Institute for Health and Care Excellence (NICE) intends to increasingly use real-world evidence in developing guidance. To increase trust in such evidence, NICE has developed a framework for developing and assessing real-world evidence studies, including understanding the value of the selected data source for the decision problem.MethodsStarting with published high-quality studies about data quality, we developed a conceptual model of the elements needed to understand the quality of a data source. Results from a literature search were then mapped to the model. We used this to design a structured reporting tool, the data Suitability Assessment Tool (dataSAT), and tested it in several cases studies. Additionally, we engaged with internal and external stakeholders to obtain feedback on the tool and revised it accordingly.ResultsDataSAT covers provenance of the data, assessment of data quality, and the data’s relevance to the research question. For data provenance, information is requested about the data source independent of the study’s interests, including the purpose, setting, dates of operation, funding, data specification, and management and quality assurance plans for the data sources. Data quality is covered by quantitively assessing the completeness and accuracy of the following key study elements to inform critical appraisal of the study: population inclusion and exclusion criteria; intervention; comparator; and outcomes and key covariates. The findings on data sources and data quality are then interpreted in terms of relevance to the decision problem. This includes relevance to the population in the United Kingdom, the treatment pathway and care setting, the availability of key study elements, time-related factors such as length of follow up, and the effects of sample size and missing data on the validity of findings.ConclusionsDataSAT allows summary information on source data, including quality and relevance, to be reported in a structured manner, enabling decision makers to better understand how the data influence the robustness of analyses used in health technology assessment. This helps increase trust in the use of real-world evidence.