Regulatory, health technology assessment and company interactions: the current landscape and future ecosystem for drug development, review and reimbursement.

BACKGROUND: Multi-stakeholder interactions have evolved at product and policy levels. There is a need to assess the current and future landscape of interactions between companies, and regulatory and HTA agencies to address challenges and identify areas for improvement. OBJECTIVES: The aims of this study were to review the current interactions within and across regulatory and HTA agencies, and companies' experiences in engaging in these activities; to assess the added value of interactions as well as limitations; to explore the future ecosystem for stakeholder interactions. METHOD: Three separate questionnaires were developed for companies, regulators and HTA agencies, respectively, to assess their experiences and perceptions. The responses were analyzed using descriptive statistics and discussed at a multi-stakeholder workshop. Key outcomes from the surveys and workshop discussion were reported. RESULTS: All seven regulators and seven HTA agencies in the survey indicated that they had stakeholder interactions. More formal collaboration occurred with regulators compared with HTA agencies. All nine companies have taken early advice but indicated the need for future prioritization. Success indicators can be built at the product and therapy levels, with the added value of faster patient access. Four principles were proposed for the future ecosystem: separate remit and functions between regulators and HTA; align processes; converge evidence requirements where possible; increase transparency. CONCLUSIONS: This research brought together regulators, HTA agencies, companies to examine how they interact with one another. We propose measures of value and make recommendations on future evolution to enable better evidence generation and improve regulatory and HTA decision-making.

Health technology assessment of tests for SARS-CoV-2 and treatments for COVID-19: A proposed approach and best-practice recommendations.

OBJECTIVES: To develop best-practice guidance for health technology assessment (HTA) agencies when appraising diagnostic tests for SARS-CoV-2 and treatments for COVID-19. METHODS: We used a policy sandbox approach to develop best-practice guidance for HTA agencies to approach known challenges associated with assessing tests and treatments for COVID-19. The guidance was developed by a multi-stakeholder workshop of twenty-one participants representing HTA agencies, clinical and patient experts, academia, industry, and a payer, from across Europe and North America. The workshop was supported by extensive background work to identify the key challenges, including: targeted reviews of existing COVID-related methods guidance for assessing interventions and clinical guidelines, engagement with clinical experts, a survey and workshop of HTA agencies, a systematic review of published economic evaluations, and a workshop of health economic modelers. RESULTS: We suggest HTA agencies should consider using other types of evidence (e.g., real world) where high-quality randomized controlled trials may be lacking and healthcare systems would value timely HTA outputs. A "living" HTA approach may be useful, given the context of an evolving disease, scientific understanding and evidence base, allowing for decisions to be efficiently revisited in response to new information; particularly, if supported by a common "disease model" for COVID-19. Innovative ways of engaging with the public and clinicians, and early engagement with regulators and payers, are recommended. CONCLUSIONS: HTA agencies should consider the elements of this guidance that are most suited to their existing processes to enable them to assess the effectiveness and value of interventions for COVID-19.

CODE-EHR best practice framework for the use of structured electronic healthcare records in clinical research.

Big data is central to new developments in global clinical science aiming to improve the lives of patients. Technological advances have led to the routine use of structured electronic healthcare records with the potential to address key gaps in clinical evidence. The covid-19 pandemic has demonstrated the potential of big data and related analytics, but also important pitfalls. Verification, validation, and data privacy, as well as the social mandate to undertake research are key challenges. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including patient representatives, clinicians, scientists, regulators, journal editors and industry. We propose the CODE-EHR Minimum Standards Framework as a means to improve the design of studies, enhance transparency and develop a roadmap towards more robust and effective utilisation of healthcare data for research purposes.

CODE-EHR best-practice framework for the use of structured electronic health-care records in clinical research.

Big data is important to new developments in global clinical science that aim to improve the lives of patients. Technological advances have led to the regular use of structured electronic health-care records with the potential to address key deficits in clinical evidence that could improve patient care. The COVID-19 pandemic has shown this potential in big data and related analytics but has also revealed important limitations. Data verification, data validation, data privacy, and a mandate from the public to conduct research are important challenges to effective use of routine health-care data. The European Society of Cardiology and the BigData@Heart consortium have brought together a range of international stakeholders, including representation from patients, clinicians, scientists, regulators, journal editors, and industry members. In this Review, we propose the CODE-EHR minimum standards framework to be used by researchers and clinicians to improve the design of studies and enhance transparency of study methods. The CODE-EHR framework aims to develop robust and effective utilisation of health-care data for research purposes.