Objectives:The headroom approach to medical device development relies on the estimation of a value-based price ceiling at different stages of the development cycle. Such price-ceilings delineate the commercial opportunities for new products in many healthcare systems. We apply a simple model to obtain critical business information as the product proceeds along a development pathway, and indicate some future directions for the development of the approach.Methods:Health economic modelling in the supply-side development cycle for new products.Results:The headroom can be used: initially as a ‘reality check’ on the viability of the device in the healthcare market; to support product development decisions using a real options approach; and to contribute to a pricing policy which respects uncertainties in the reimbursement outlook.Conclusions:The headroom provides a unifying thread for business decisions along the development cycle for a new product. Over the course of the cycle attitudes to uncertainty will evolve, based on the timing and manner in which new information accrues. Within this framework the developmental value of new information can justify the costs of clinical trials and other evidence-gathering activities. Headroom can function as a simple shared tool to parties in commercial negotiations around individual products or groups of products. The development of similar approaches in other contexts holds promise for more rational planning of service provision.

Background: Sleepio is an automated digital program that delivers digital cognitive behavioural therapy for insomnia (dCBT-I). Sleepio has been proven effective in improving sleep difficulties; however, evidence for the possible impact of Sleepio use on healthcare costs in the UK has not, to the authors’ knowledge, previously been developed.Aim: To identify the effect of a population-wide rollout of Sleepio in terms of primary care costs in the NHS in England.Design & setting: The study was conducted in the Thames Valley region of England, where access to Sleepio was made freely available to all residents between October 2018 and January 2020. The study relies on a quasi-experimental design, using an interrupted time series (ITS) to compare the trend in primary care costs before and after the rollout of Sleepio.Method: Primary care data for people with relevant characteristics from nine general practices in Buckinghamshire was used. Primary care costs include general practice contacts and prescriptions. Segmented regression analysis was used to estimate primary and secondary outcomes.Results: For the 10 705 patients included in the sample, the total saving over the 65-week follow-up period was £71 027. This corresponds to £6.64 per person in the sample or around £70.44 per Sleepio user. Secondary analyses suggest that savings may be driven primarily by reductions in prescribing.Conclusion: Sleepio rollout reduced primary care costs. National adoption of Sleepio may reduce primary care costs by £20 million in the first year. The expected impact on primary care costs in any particular setting will depend on the uptake of Sleepio.

The potential for the use of real-world data (RWD) to generate real-world evidence (RWE) that can inform clinical decision-making and health policy is increasingly recognized, albeit with hesitancy in some circles. If used appropriately, the rapidly expanding wealth of health data could improve healthcare research, delivery of care, and patient outcomes. However, this depends on two key factors: (1) building structures that increase the confidence and willingness of European Union (EU) citizens to permit the collection and use of their data, and (2) development of EU health policy to support and shape data collection infrastructures, methodologies, transmission, and use. The great potential for use of RWE in healthcare improvement merits careful exploration of the drivers of, and challenges preventing, efficient RWD curation. Literature-based research was performed to identify relevant themes and discussion topics for two sets of expert panels, organized by the European Alliance for Personalised Medicine. These expert panels discussed steps that would enable a gradual but steady growth in the quantity, quality, and beneficial deployment of RWE. Participants were selected to provide insight based on their professional medical, economic, patient, industry, or governmental experience. Here, we propose a framework that addresses public trust and access to data, cross-border governance, alignment of evidence frameworks, and demonstrable improvements in healthcare decisions. We also discuss key case studies that support these recommendations, in accordance with the discussions at the expert panels.