The Role of HTA in Achieving and Progressing Universal Health Coverage
Universal Health Coverage (UHC) has been acknowledged as a priority goal of many health systems, with the recognition that UHC improves the health of a population and reduces health inequalities. Furthermore, there is general consensus that healthier populations are more productive, take less sick leave and demand less healthcare services resulting in economic benefits on a long-term basis. The importance of achieving UHC is reflected in the consistent calls by the World Health Organization (WHO) for member states to implement systems that promote access to quality health care and provide households with the needed protection from the catastrophic consequences of out-of-pocket (OOP) health-related payments. Progress towards UHC is a continuous process that changes in responses to shifting demographic, epidemiological and technological trends, as well as a population’s expectations.
HTA is recognized as an important approach in setting benefits packages for UHC and in answering other key questions that policy makers are left to grapple with after the commitment to UHC is made. HTA can address central issues: what services should be made available, and under what conditions? In the context of fixed and restricted budgets, where it is necessary to prioritize, should more (priority) services be included? Should coverage of existing (priority) services be expanded or excluded? Should co-payments of existing (priority) services be reduced? When making these decisions, social, legal ethical, and socio-economic issues need to be addressed in a meaningful deliberative manner to enhance public accountability.
The need to perform HTA is higher where resources are limited. For example, in countries with a fragile economic state, HTA can be used to define basic benefit packages; emergency kits and for disaster planning. For low-income countries with low levels of UHC, HTA can be used to define essential medicines and other interventions packages (including vaccinations, prevention and some treatments), for middle-income countries with medium UHC, HTA can then be used to define extensions to benefits packages including treatment of (non-communicable) diseases and for specific populations. For high-income countries with a high level of UHC, HTA can be used to define full-benefits packages (including home care, palliative care and diagnostic services and innovative technologies) and to reassess or disinvest from services.
There are also many hurdles to be overcome in relation to healthcare and human resource capacity and access to health services. These relate to scope of services, quality, cost, and governance of a specific context. HTA is an effective tool to unravel/unveil these barriers by answering questions about a health technology or service, in terms of availability, accessibility, equity, and affordability in that specific context. However, HTA is not the only solution to achieving and progressing UHC in any health system. HTA should be considered as one part of the complex health ecosystem within a political system with local customs, cultural and environmental issues always being considered when prioritizing services.
Based on the above description, the plenary session will consist of:
- a short overview of Universal Health Coverage, what’s in and what’s out and the possible role of HTA (e.g. priority setting, benefits package listings, horizon scanning, reassessment);
- using HTA to inform the development and prioritization of basic and essential healthcare (e.g. looking at the cost effectiveness of integrated clinical pathways and benefit packages specific to local contexts);
- explore the crucial elements that are required to conduct UHC-focused HTA that considers the appropriateness to socio-political factors (e.g. culture adaption to use evidence for decision making and appropriate and meaningful patient/public involvement);
- consider what works well when using HTA in the context of achieving UHC; acknowledging that there is “not one size that fits all”.
How to Adapt HTA to Address Technologies that are ‘Disrupting’ Health Systems
There are an increasing number of emerging innovations that have a high potential to be “disruptive.” That is, technologies that are representing fundamentally, and possibly unexpected, new modes of clinical practice or service improvement that will disrupt or overturn the traditional methods and practices in health systems. They include but are not limited to potentially curative therapies such as, gene therapies, somatic-cell therapies, and tissue-engineered therapies. Examples include Chimeric Antigen Receptor (CAR) T-cell therapies, gene editing using Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR), preventative therapy for Alzheimer’s Disease and genetic diagnostic tests.
It is often challenging to adequately estimate the value of these disruptive technologies using current HTA frameworks and methodologies. As these typically ‘one-shot,’ expensive treatments will most likely be compared to a lifetime of existing treatments, the traditional HTA paradigms will be tested. This may include increased safety concerns (certainty of treatment effect and potential for adverse events); assessing and paying for value, and appropriate incorporation of ethical, legal, social, and patient/consumer issues and concerns. A number of policy issues may impact current HTA frameworks and methodologies in considering these technologies, such as: global differences in regulatory, patenting and pricing mechanisms; the manufacturing and subsequent organization of service delivery; and balance of risk versus access to innovative technologies (particularly in the context of a sustainable health system providing Universal Health Coverage).
Recognizing the large number of disruptive technologies under development, consideration needs to be given to whether current HTA frameworks are appropriate for the evaluation of these potentially ground-breaking technologies. Critical assessment of the current HTA frameworks with regards to the specific characteristics of these technologies and continued dialogue will be key to ensuring appropriate market access and for informing coverage decisions.
This plenary session aims to gain insights and generate discussion on the following areas:
- Methodological uncertainties and the associated consequences that are particularly relevant to the assessment of disruptive technologies, including HTA best practices for implementation (or knowledge translation) and evaluation of these technologies
- Collaboration between HTA agencies on sharing experiences, knowledge gained, and lessons learned
- Whether horizon scanning systems can play a more systematic role in fostering cross-stakeholder interactions, encouraging the preparation of health systems in the appropriate uptake of disruptive and potentially costly technologies
- How and when (and if) HTA processes could be adapted specifically for these technologies to align with different regulatory and/or reimbursement systems
- How the sustainability of Universal Health Coverage (UHC) and health systems can be maintained while ensuring appropriate access to these high-cost, yet potentially ground-breaking technologies
- The pressing ethical, legal, social, and policy implications regarding the introduction and adoption of these technologies
- Whether patient, citizen, and broader stakeholder involvement processes should be adapted to help promote the development, assessment, diffusion, and adoption of these technologies.
Incoming Tides and What it Means for HTA; the Rise of Real-World Evidence, ‘Big Data’, and Artificial Intelligence.
In the last few years, real-world evidence (RWE), ‘big data’, and Artificial Intelligence (AI) have been rapidly increasing in terms of prevalence and applications. The following definitions can be considered:
- RWE is evidence derived from the analysis of real-world data (RWD; observational or administrative data) that can provide information on the routine delivery of health care and the health status of a population.
- Big data is an evolving term that describes a large volume of a variety of data (structured or unstructured) that has the potential to be mined for information.
- AI is the simulation of human intelligence processes by machines, including learning, reasoning, and self-correction. In the medical context, AI has the potential to be used to exploit datasets in the diagnosis, treatment, and prediction of outcomes in many clinical scenarios.
The rise in these data collection and analysis modalities is already impacting health care delivery and subsequently HTA, with the potential to reshape health care practice around the world. However, these increased possibilities also bring a number of challenges. The quality and acceptability of the data being collected, the governance and accountability surrounding the data (including consent for data collection and usage) and transference of data securely across stakeholder groups are all key issues. Principles on how and when RWE, big data, and AI can be used to inform decision-making (both at the clinical level and also in the context of HTA) all need to be deliberated and openly discussed.
Applications of AI, based on big data, are already being used for assisting clinical decision making, radiological and pathological analysis, early prediction, monitoring of health risks, and health insurance fraud detection. The possible increase in predictive accuracy is a result of potentially complex algorithms that can sometimes appear to be “black boxes”; it is important that comprehensive evaluations and input from all stakeholders (particularly from the viewpoint of patients and citizens) is undertaken. AI is also being integrated into HTA conduct (including, but not limited to, horizon scanning and systematic literature searches). As a result, the HTA community need to urgently consider how this will affect clinical and HTA workforces, and how to effectively collaborate with technology developers and platforms. It is also imperative that preparation for the influx of big data, RWE, and AI continues; including a deeper understanding of the potential ‘disruption’ to sustainable health systems.
Given the breadth of the full topic, the plenary session will focus on AI including:
- The implication of AI in health systems; how AI is being used with case studies of applications of AI in clinical practice now and potentially in the future (e.g. clinical decision-making and diagnosis in oncology, neurology, diabetic retinopathy, radiology, cardiology).
- Where (big) data comes from to inform and develop AI algorithms, who owns the data, what factors were taken into consideration, experience in developing and testing algorithms.
- How AI can and should be transparently evaluated, the risks of AI getting treatment/diagnostic algorithms wrong or skewed and biased, and the way HTA may need to adapt.
- The use of AI in HTA and the impact on the HTA community and workforce.
- Quality, ethical, and regulatory implication of AI, particularly from the patient perspective.