Real-world Data (RWD) and Real-world Evidence (RWE) are essential tools in modern healthcare, helping to generate valuable clinical insights.

While RWD provides a wealth of information on patient health status, RWE utilizes the data in order to assess the effectiveness and safety of treatments through observational studies and other designs.

The importance of RWD and RWE has grown alongside the expansion of big healthcare databases, especially with RWE-based publications increasing tenfold over the past two decades. 

Health technology assessment (HTA) has an increasingly important role in healthcare decision making globally.

This lecture presents the definition of HTA, the significance of HTA in health sector, and implications of HTA development in China. 

Further development of HTA in China faces potential challenges, including lack of policy arrangements for using HTA to support decision making, insufficient HTA staff and expertise, difficulties with data availability/accessibility, lack of standardised methodology and quality control measures, and challenges of integrating multidimensional assessments into deliberative processes. 

We recommend strengthening the institutionalisation of HTA at national level, establishing more HTA agencies with arm’s length funding, using multi-channel resources and inputs, ensuring local data availability, and developing indigenous HTA meth

This presentation sheds light on the pivotal role of Health Technology Assessment (HTA) within healthcare systems, specifically in the UK context with NICE and NIHR HTA as illustrative examples.

It delves into key areas where HTA significantly contributes, including decision-making, facilitating access, reimbursement processes, expediting access, and enhancing patient and health system outcomes.

Health technology assessment (HTA) critically evaluates a wide range of evidence, including published and unpublished studies, conference proceedings, expert opinions, registry data, and real-world evidence.

While randomized controlled trials (RCTs) are the "gold standard" in HTA due to their advantages in randomization, allocation concealment, and blinding, they have limitations such as high external validity and insufficient long-term evidence.

The evidence pyramid helps to illustrate the hierarchy of evidence, with systematic reviews and meta-analyses at the top. 

The presentation discusses chronic disease trends in Hong Kong until 2030, highlighting Healthcare Big Data and Real-world Evidence (RWE) in HTA. It underscores RWE's role in understanding treatment paths, disease impact, and drug efficacy.

By addressing RCT limitations, RWE supports the evaluation and approval of new health technologies swiftly.

International bodies are embracing RWE in their frameworks, enhancing decision-making and healthcare assessments worldwide, indicating a shift towards optimized resource allocation and informed healthcare choices on a global scale. 

Patients often face a trade-off between the length and quality of life when undergoing treatments like chemotherapy or radiation therapy.

While these treatments can extend life expectancy, their side effects—such as pain, fatigue, and emotional distress—can significantly reduce quality of life (QOL).

The Quality-Adjusted Life Year (QALY) is a key outcome measure that combines both health-related QOL and length of life, providing a comprehensive assessment of a patient’s well-being. QALY is widely used to evaluate and compare the effectiveness of different medical treatments and health interventions, aiding in resource allocation by comparing QALYs associated with various interventions.

However, while QALY is a valuable tool, it has limitations that need to be considered in its application.

To address scarcity in healthcare, three key strategies can be implemented: promoting economic growth, making efficient use of current resources, and reducing the overall need for care.

However, two significant dilemmas often arise in healthcare allocation—the first, is deciding whether to allocate resources to rare conditions or more common ones, where the number of patients benefiting from the resource may be limited.

The second dilemma involves the challenge of dealing with an influx of innovative but increasingly expensive drugs.

Health economics plays a crucial role in comparing alternatives to determine the most effective option.

Markov modeling is a widely used decision-analytic model in economic evaluation, structured around mutually exclusive "health states" that represent all possible stages in a disease's progression.

Individuals transition between these states based on specific "transition probabilities" during each cycle. Each health state is associated with a particular "cost" and "health outcome" for the time spent in that state.

Markov modeling typically compares one scenario to an alternative, adjusting probabilities and costs accordingly.

When evaluating a new treatment with both higher  Quality-Adjusted Life Years (QALY) and cost, the Incremental Cost-Effectiveness Ratio (ICER) is calculated by dividing the difference in costs by the difference in QALYs.

This ratio is then compared to the country's cost-effectiveness threshold to determine if the new treatment is justified.

This presentation explores the use of AI-based forecasting models to project the medium to long-term burden of chronic diseases and healthcare needs in Hong Kong.

It delves into the application of both statistical and deep learning forecasting methods, highlighting their respective advantages and limitations in handling time series data.

By examining these models, it is salient to better understand how they can be used to guide future healthcare planning and resource allocation, ensuring preparedness for the evolving healthcare landscape in Hong Kong.

Clinical trials are essential for assessing the effectiveness and safety of healthcare interventions.

However, they do not address whether these interventions should be financially supported by public funding.

In this lecture, we introduced the fundamentals of cost-effectiveness analysis alongside clinical trials, demonstrating how it can be applied using a practical example, and discussed its limitations.

This presentation discusses the complexities of health technology adoption, particularly focusing on how heterogeneity in demand influences policy decisions. Using the example of meningococcal vaccine adoption, the role of fear-based marketing, the affect heuristic, and the concept of "fear reversals" in shaping consumer behavior are explored.

Through a conjoint analysis study conducted in France and Germany, the presentation highlights the challenges policymakers face when demand varies significantly across populations.

It raises critical questions about how to proceed when half of the population values a health product highly while the other half does not, and whether fear should be used as a tool to influence public health decisions.