Amanda Cole

The Congressional Budget Office (CBO) created a model of new drug development with the aim of informing the US Congress about the potential impact of policy changes affecting expected biopharmaceutical revenue on future innovation. While models are by their nature simplifications of reality, biopharmaceutical investment decision-making is particularly complex and poorly understood outside the investment ecosystem, raising questions about the adequacy of such models to inform policymaking. To better understand how CBO's model compares to real-world investment decision processes, we conducted semi-structured interviews with investors representing venture capital, private equity, corporate venture capital, and biopharmaceutical companies. The interviews with investors suggest that the CBO's model does not adequately reflect investment decisions for drug development. These findings highlight the risks of using models to guide policymaking and the need to improve the existing model with the help of stakeholder input before such models are adopted.

Scientific advancements offer significant opportunities for better patient outcomes, but also present new challenges for value assessment, affordability and access. Alternative payment models (APMs) can offer solutions to the ensuing payer challenges. However, a comprehensive framework that matches the spectrum of challenges with the right solution, and places them within a framework for implementation, is currently missing. To fill this gap, we propose evidence-based steps for the effective selection and implementation of APMs. First, contracting challenges should be identified and mapped to potential APM solutions. We developed a decision guide that can serve as a starting point to articulate core problems and map these to APM solutions. The main problem categories identified are: budget impact and uncertainty, value uncertainty, and the scope of value assessment and negotiation. Sub-categories include affordability, uncertainty of effectiveness, and patient heterogeneity, which map onto APM solutions such as outcome-based agreements, instalments, and subscription models. Just as important are the subsequent identification and assessment of the feasibility of potential solutions as well as collaboration to reach agreement on the terms of the APM and lay the groundwork for effective implementation. We adduce recent examples of APM implementation as evidence of how commonly cited implementation barriers can be overcome by applying pragmatic design choices and collaboration. This step-by-step framework can aid payers and manufacturers in the process of effectively identifying, agreeing on, and implementing APMs to advance patient access to cost-effective medicines, while at the same time providing appropriate incentives to support future innovation.

Background: Genomic and molecular alterations are increasingly important in cancer diagnosis, and scientific advances are opening new treatment avenues. Precision oncology (PO) uses a patient’s genomic profile to determine optimal treatment, promising fewer side effects and higher success rates. Within PO, tumor-agnostic (TA) therapies target genomic alterations irrespective of tumor location. However, traditional value frameworks and approval pathways pose challenges which may limit patient access to PO therapies. Objectives: This study describes challenges in assessing PO and TA medicines, explores possible solutions, and provides actionable recommendations to facilitate an iterative life-cycle assessment of these medicines. Methods: After reviewing the published literature, we obtained insights from key stakeholders and European experts across a range of disciplines, through individual interviews and an industry workshop. The research was guided and refined by an international expert committee through 2 sounding board meetings. Results: The current challenges faced by PO and TA medicines are multiple and can be demonstrated through real-world examples of the current barriers and opportunities. A life-cycle approach to assessment should be taken, including key actions at the early stages of evidence generation, regulatory and reimbursement stage, as well as payment and adoption solutions that make use of the evolving evidence base. Working toward these solutions to maximize PO medicine value is a shared responsibility and stands to benefit all stakeholders. Conclusions: Our call to action is to expand access to comprehensive genomic testing, foster a learning health care system, enable fast and equitable access to cost-effective treatments, and ultimately improve health outcomes.

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.

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.

Introduction: The expansion of health data offers exciting opportunities to support better and more efficient drug discovery, development and implementation. Data protection and governance provide the legal framework to balance safeguarding patients’ privacy with the benefits to society of medical research. Our aim is to highlight current legal barriers to the better use of health data and propose ways to address them.Methods: Analysis of the relevant legislative texts was supplemented by interviews with external experts in data protection, health research, informatics and cyber security and a workshop with pharmaceutical industry members. We investigated the legal issues arising for six key activities along the pharmaceutical lifecycle, from identifying unmet need through to health technology assessment and pharmacovigilance.Results: The General Data Protection Regulation (GDPR) was introduced in May 2018 to Harmonise data protection across Europe. However, considerable ambiguity remains, particularly around the appropriate legal bases for data processing in the absence of consent: scientific research, public interest, or provision of health or social care. Other key themes included data subject rights, anonymization, compatibility of primary and secondary (re-)use of data, heterogeneity arising from divergent interpretation, the need for guidance on digital health, and the importance of trust.Conclusions: We speculate which legal bases are most appropriate for the six pharmaceutical activities studied, but clear guidance and consensus is required. The GDPR was not designed to hamper scientific research, and the issues identified arose from uncertainties rather than barriers per se. Industry and academic researchers should therefore deal proactively with the prevailing uncertainties, share good practice, and engender trust by co-creating a code of conduct and outlining principles of responsible use. Engagement with patients will be critical in encouraging a shared understanding of the value to society of health data for research.

Background: Sleepio is an automated digital programme that delivers cognitive behavioural therapy for insomnia. Sleepio has been proven effective in improving sleep difficulties. However, evidence for the possible impact of Sleepio use on health care costs in the United Kingdom has not previously been developed. In this study, we assessed the effect of a population-wide rollout of Sleepio in terms of primary care costs in the National Health Service (NHS) in England. Methods: 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. We use primary care data for people with relevant characteristics from nine general practices in Buckinghamshire. The study relies on a quasi-experimental design, using an interrupted time series to compare the trend in primary care costs before and after the rollout of Sleepio . Primary care costs include general practice contacts and prescriptions. Segmented regression analysis was used to estimate primary and secondary outcomes. Results: For the 10,704 patients included in our sample, the total saving over the 65-week follow-up period was £71,027. This corresponds to £6.64 per person in our sample or around £70.44 per Sleepio user. Secondary analyses suggest that savings may be driven primarily by reductions in prescriptions. 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 .

Background In health state valuation studies, health states are typically presented as a series of sentences, each describing a health dimension and severity ‘level’. Differences in the severity levels can be subtle, and confusion about which is ‘worse’ can lead to logically inconsistent valuation data. A solution could be to mimic the way patients self-report health, where the ordinal structure of levels is clear. We develop and test the feasibility of presenting EQ-5D-5L health states in the ‘context’ of the entire EQ-5D-5L descriptive system. Methods An online two-arm discrete choice experiment was conducted in the UK (n = 993). Respondents were randomly allocated to a control (standard presentation) or ‘context’ arm. Each respondent completed 16 paired comparison tasks and feedback questions about the tasks. Differences across arms were assessed using regression analyses. Results Presenting health states ‘in context’ can significantly reduce the selection of logically dominated health states, particularly for labels ‘severe’ and ‘extreme’ (χ² = 46.02, p < 0.001). Preferences differ significantly between arms (likelihood ratio statistic = 42.00, p < 0.05). Comparing conditional logit modeling results, coefficients are ordered as expected for both arms, but the magnitude of decrements between levels is larger for the context arm. Conclusions Health state presentation is a key consideration in the design of valuation studies. Presenting health states ‘in context’ affects valuation data and reduces logical inconsistencies. Our results could have implications for other valuation tasks such as time trade-off, and for the valuation of other preference-based measures.

Decisions made by the National Institute for Health and Care Excellence (NICE) exert an influence on the allocation of resources within ‘fixed’ National Health Service budgets. Yet guidance for different types of health interventions is handled via different ‘programmes’ within NICE, which follow different methods and processes.