Evidence generation plan for HTE10065 Digital technologies for applying algorithms to spirometry to support asthma and COPD diagnosis in primary care and community diagnostic centres
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3 Approach to evidence generation
3.1 Evidence gaps and ongoing studies
Table 1 summarises the evidence gaps and ongoing studies that might address them. Information about evidence status is derived from the external assessment report. More information on the studies in the table can be found in the supporting documents.
3.2 Data sources
NICE's real-world evidence framework provides detailed guidance on assessing the suitability of a real-world data source to answer a specific research question.
The National Respiratory Audit Programme (NRAP) is a clinical audit dataset that collects information about people with respiratory disease in England and Wales. It includes some data needed to address the evidence gaps, such as standard care in the NHS, hospital admissions, and exacerbations because of asthma or COPD. NRAP can be linked to other datasets such as NHS Digital's Hospital Episode Statistics (HES) dataset, and this combined dataset could be used to estimate resource use. HES can be accessed through NHS England's Secure Data Environment service. NRAP can be amended to support additional data collection where necessary.
The quality and coverage of real-world data collections are of key importance when used in generating evidence. Active monitoring and follow up through a central coordinating point is an effective and viable approach to ensure good-quality data with broad coverage.
3.3 Evidence collection plan
Real-world prospective comparative cohort study
The study should enrol a representative population and compare people with an asthma or COPD diagnosis made using the technology with a similar group who had standard care. Eligibility for inclusion, and the point of starting follow up, should be clearly defined and consistent across comparison groups to avoid selection bias.
Data should be collected in all groups from the point at which a person would become eligible for standard care. The data from both the intervention and comparison groups should be collected at appropriate time intervals and for a minimum of 12 months.
Data could be collected using a combination of primary data collection, suitable real-world data sources, and data collected through the technology itself (for example, engagement data).
The technology developer could initially do a 'silent evaluation' (see Kwong et al. 2022) before full deployment into services. This approach allows the technology to be used in a real-world setting without any influence on clinical decision making until it is fully deployed. This approach can be used to:
understand whether the technology can be deployed safely (including in subpopulations)
understand how the technology might have influenced decision making (for example, onward referrals and care pathway)
collect some relevant data items (for example, failure rate or number of indeterminate findings).
3.4 Data to be collected
The following information has been identified for collection:
patient demographics, including age, sex and ethnicity
diagnostic accuracy, including false-positive and false-negative results
accuracy when used by different healthcare professionals
health-related quality-of-life data, ideally collected with the EQ-5D questionnaire
resource use, including staff time, band, level of experience and accreditation of healthcare professionals using the technology, and time taken to do and interpret spirometry
details of the technology (cost, software name, version and configuration settings)
impact on the NHS care pathway of using the technology, for example, waiting lists, time to diagnosis, and setting in which diagnosis was made.
Data collection should follow a predefined protocol and quality assurance processes should be put in place to ensure the integrity and consistency of data collection. See NICE's real-world evidence framework, which provides guidance on the planning, conduct, and reporting of real-world evidence studies.
3.5 Evidence generation period
The evidence generation period will be 3 years, to allow for enough time to set up and implement the technology, collect the necessary data, analyse and report it.
3.6 Following best practice in study methodology
Following best practice when conducting studies is paramount to ensuring the reliability and validity of the research findings. Adherence to rigorous guidelines and established standards is crucial for generating credible evidence that can ultimately improve patient care. NICE's real-world evidence framework details some key considerations.
Within the context of an early-use assessment, a key factor to consider as part of the informed consent process is to ensure that patients (and their carers, as appropriate) understand that data will be collected to address the evidence gaps identified in section 2. Where applicable this should take account of NICE guidance about shared decision making.
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