A model-based study to estimate the health and economic impact of health technology assessment in Thailand

Abstract

ObjectivesHealth technology assessment (HTA) plays a central role in the coverage and reimbursement decision-making process for public health expenditure in many countries, including Thailand. However, there have been few attempts to quantitatively understand the benefits of using HTA to inform resource allocation decisions. The objective of this research was to simulate the expected net monetary benefit (NMB) from using HTA-based decision criteria compared to a first-come, first-served (FCFS) approach using data from Thailand.MethodsA previously published simulation model was adapted to the Thai context which aimed to simulate the impact of using different decision-making criteria to adopt or reject health technologies for public reimbursement. Specifically, the simulation model provides a quantitative comparison between an HTA-based funding rule and a counterfactual (FCFS) funding rule to make decisions on which health technologies should be funded. The primary output of the model was the NMB of using HTA-based decision criteria compared to the counterfactual approach. The HTA-based decision rule in the model involved measuring incremental cost-effectiveness ratios against a cost-effectiveness threshold. The counterfactual decision rule was a FCFS (random) selection of health technologies.ResultsThe HTA-based decision rule was associated with a greater NMB compared to the counterfactual. In the investigated analyses, the NMB ranged from THB24,238 million (USD725 million) to THB759,328 million (USD22,719 million). HTA-based decisions led to fewer costs, superior health outcomes (more quality-adjusted life-years).ConclusionsThe results support the hypothesis that HTA can provide health and economic benefits by improving the efficiency of resource allocation decision making.

How to strengthen societal impact of research and innovation? Lessons learned from assessing participatory knowledge infrastructures funded by the Netherlands Organization for Health Research and Development

“…Several frameworks have been developed (e.g., payback model, (hybrid) Research Impact Framework, health economics models, contribution mapping) [4,5,6,7,8] and several studies have been conducted to assess the societal impact of research and innovation, e.g., [9,10,11]. From these studies it is known that successful knowledge utilisation involves so-called productive interactions [12].…”

Modeling for policy and technology assessment: Challenges from computerbased simulations and artificial intelligence

“…In particular, in Health Technology Assessment (HTA) models have been used to better understand and predict the outcome of policy changes. Again, sustainability -here the UN's Sustainable Development Goals (SDGs) calling for achieving a universal health coverage -is the main driver for the use of prognostic methods (Kingkaew et al 2022). Interestingly, HTA is also leading in the application of AI methods, although this trend is nascent.…”

Measuring progress in institutionalising evidence-informed priority-setting in the Indian healthcare system: an application using the iProSE scale

“…Using evidence to inform healthcare priority-setting has long been recognised as central to making progress towards universal health coverage,3 and seems more important than ever today. Evidence-informed priority-setting (EIPS), understood as a form of systematic priority-setting that involves the explicit consideration of evidence to determine the healthcare interventions to be provided,4 is not just sensible in theory; recent empirical evidence suggests that EIPS represents good value for money, for example, in Thailand, EIPS delivered an estimated 8:1 return on investment (ROI) 5…”

“…Evidenceinformed priority-setting (EIPS), understood as a form of systematic priority-setting that involves the explicit consideration of evidence to determine the healthcare interventions to be provided, 4 is not just sensible in theory; recent empirical evidence suggests that EIPS represents good value for money, for example, in Thailand, EIPS delivered an estimated 8:1 return on investment (ROI). 5 However, institutionalising EIPS in LMICs is not straightforward. A 2019 report by the WHO provides guidance on institutionalisation, 6 however, there is currently no agreed way to understand and measure progress, plan improvements or enable cross-country comparisons.…”