The Health Technology Assessment (HTA) process aims to optimize health system funding of technologies. In recent years there has been an increase in what is known as Real-World Evidence (RWE) as a complement to clinical trials. The objective of Health Technology Assessment International’s Latin American Policy Forum 2022 was to explore the utility of incorporating RWE into HTA and decision-making processes in the region.

This article is based on a background document, survey, and the deliberative work of the country representatives who participated in the Forum.

There is a growing interest in the use of Real-World Data / Real-World Evidence in HTA processes in Latin America, although currently there are no specific local guidelines for RWE use by HTA agencies. At present, its use is limited to certain areas such as adding context to HTA reports, the evaluation of adverse events, or cost estimation.

Potential future uses of RWE were identified, including the creation of risk-sharing agreements, the assessment of technology performance in routine practice, providing information on outcomes that are not so easily evaluated in clinical trials (e.g., the identification of specific subpopulations or quality of life), and the estimation of input parameters for economic evaluations.

The participants agreed that there are several areas presenting significant potential to expand the application of RWD/RWE and that the development of normative frameworks for its use could be helpful.

Healthcare disinvestment requires multi-level decision-making, and early stakeholder engagement is essential to facilitate implementation and acceptance. This study aimed to explore the perceptions of Malaysian healthcare stakeholders to disinvestment initiatives as well as identify disinvestment activities in the country.

A cross-sectional online survey was conducted from February to March 2023 among Malaysian healthcare stakeholders involved in resource allocation and decision-making at various levels of governance. Response frequencies were analyzed descriptively and cross-tabulation was performed for specific questions to compare the responses of different groups of stakeholders. For free-text replies, content analysis was used with each verbatim response examined and assigned a theme.

A total of 153 complete responses were analyzed and approximately 37 percent of participants had prior involvement in disinvestment initiatives. Clinical effectiveness and cost-effectiveness ranked as the most important criteria in assessment for disinvestment. Surprisingly, equity was rated the lowest priority despite its crucial role in healthcare decision-making. Almost 90 percent of the respondents concurred that a formal disinvestment framework is necessary and the importance of training for the program’s successful implementation. Key obstacles to the adoption of disinvestment include insufficient stakeholder support and political will as well as a lack of expertise in executing the process.

While disinvestment is perceived as a priority for efficient resource allocation in Malaysian healthcare, there is a lack of a systematic framework for its implementation. Future research should prioritize methodological analysis in healthcare disinvestment and strategies for integrating equity considerations in evaluating disinvestment candidates.

Malaria remains the number one cause of morbidity and mortality in Ghana. Since 1961, several malaria control strategies have been adopted, some of which were discontinued due to funding. In spite of the numerous malaria control strategies in place, its prevalence continues to rise. Priority setting using economic evidence has been proven to ensure efficient use of resources in a cost-effective manner (1). This study, therefore, sought to examine economic evaluation studies conducted on malaria in Ghana and their influence on malaria control policies.

A systematic search was conducted in databases including Medline and Embase to identify relevant Malaria economic evaluation studies conducted in Ghana up to December 2016. Malaria control policies formulated in Ghana over the years were also reviewed. The economic studies were examined alongside the policies to establish their influence on them.

A total of eight studies were identified, all of which were conducted in response to a global directive on malaria control and funded by international agencies. All studies were cost-effective; five evaluating preventive measures and the remaining evaluating treatment. The studies used different methodological approaches, rendering the comparison between alternatives impossible.

Most malaria control initiatives are funded by international agencies, hence its abandonment when funding ceases. Although the majority of economic studies addressed some of these policies, none of them directly influenced their adoption. These policies were rather influenced by global malaria control initiatives. Also, malaria chemoprophylaxis; demonstrated as cost-effective by three studies, is not on the Ghana malaria control policy (2,3).

To ensure sustainability of malaria control strategies and subsequently reduce its prevalence, Ghana must invest financially into economic analysis for formulating and implementation of these policies. Also, the use of economic evidence by policy makers can be promoted, should researchers adopt a methodological guideline for its conduct that ensures comparability of results.

We aimed to develop and validate machine learning (ML) -based algorithms to predict COVID-19 diagnosis as well as to identify new biomarkers associated with the disease.

Initially, 96 blood samples of patients diagnosed with COVID-19 (Thaizhou Hospital, China) were analyzed through liquid chromatography coupled to mass spectrometry. Samples of patients presenting other pneumonias or severe acute respiratory syndrome, but with negative RT-PCR for SARS-CoV-2, were used as positive controls. Samples from healthy volunteers were used as negative controls. The final database included around 1000 metabolites. Exploratory analyses for the development of ML-based models using principal component analysis (PCA) were performed. Leverage plot versus studentized residuals method was used to detect outliers. Three supervised ML-based models were developed: discriminant analysis by partial least squares (PLS-DA), artificial neural networks discriminant analysis (ANNDA) and k-nearest neighbors (KNN). Samples for the training (70%) and testing sets (30%) were randomly selected using the Kenrad Stone algorithm. Models’ performance was evaluated considering accuracy, sensitivity and specificity. Analyses were conducted in SOLO (Eigenvector-Research).

The PCA model was able to distinguish the three classes of patients’ samples (positive for COVID-19, negative controls, positive controls) with an overall accumulated variance of 94.27 percent. The PLS-DA model presented the best performance (accuracy, sensitivity, and specificity of 93%, 98% and 88%, respectively). Increased levels of the biomarkers uridine (linked to glucose homeostasis, lipid, and amino acid metabolisms), 4-hydroxyphenylacetoylcarnitine (metabolite from the tyrosine metabolism; probably associated with anorexia) and ribothymidine (resulting from oral and fecal microbiota alterations) were significantly associated with COVID-19.

Three different and updated ML-based algorithms were developed to predict COVID-19 diagnosis; PLS-DA led to the most accurate results. High levels of some metabolites were found as potentially predictors of the disease. These biomarkers should be further evaluated as potential therapeutic targets in well-designed clinical trials. These ML-based models can help the early diagnosis of COVID-19 and guide the development of tailored interventions.

The experience of implementing a hospital-based health technology assessment (HB-HTA) system in Kazakhstan is currently represented by only one organization, an independent HB-HTA unit established in 2015 in the Medical Center Hospital of the President's Affairs Administration (the Hospital). Despite the demonstrated positive experience of the Hospital, the widespread implementation of the HB-HTA system in Kazakhstan has experienced some barriers that must be considered before further development can occur.

To determine the barriers to developing and implementing HB-HTA in Kazakhstani hospitals, data from the Hospital's experience were obtained through a survey of Kazakhstan hospitals, conducted on behalf of the Ministry of Health Care. An official response was received from 29 hospitals. During the survey and discussions with hospital staff using the “brainstorming” method, several barriers to the development of HB-HTA in Kazakhstan were identified.

Barriers at the system level included the lack of monitoring of the HB-HTA system at the national and regional levels and a lack of methodological support. Organizational barriers included a critically small number of HTA experts and the need for additional logistical support and funding from hospitals. The subjective factors we attributed to the rejection of the HB-HTA system by hospital management were the underestimation of lost profits and that HTA is a tool for promoting a transparent and open system for making managerial decisions.

Despite some barriers, the development of HB-HTA in Kazakhstan is a promising area. The heads of key hospitals in Kazakhstan demonstrated a readiness and understanding of the need to use the principles of health technology assessment and clinical and economic analysis to promote the active transfer and implementation of innovative medical technologies.

Differences between healthcare datasets in structure, content, and coding systems are widely recognized as significant barriers to generating robust evidence for regulatory and medical decision making. As a result, there is a growing interest in using common data models embedded within large data networks. By standardizing the structure, contents, and semantics of disparate healthcare databases, common data models like the Observational and Medical Outcomes Partnerships common data model (OMOP-CDM) enable multidatabase studies to be undertaken at speed and in a transparent way. To date, little attention has been given to their potential role in health technology assessment (HTA).

We identify the uses of observational data in generating evidence in HTA, some common analytical challenges faced in their estimation, and the infrastructural, technical, and data reusability constraints that limit its wider use. We discuss where and how the OMOP-CDM could overcome these barriers in relation to different types of evidence requirements.

The OMOP-CDM increases the interoperability of otherwise disparate datasets, allowing reliable evidence to be generated from multidatabase studies at speed and transparently. The current analytical tools are best suited for clinical characterization and population-level effect estimation. Further developments to these tools are required to support analyses common in HTA like parametric survival modeling. Differences in costing methods as well as the structure of healthcare delivery between countries may limit the feasibility and value of standardization.

The OMOP-CDM has the potential to support reliable and timely evidence generation in HTA. The analytical tools should be further developed to support common HTA use cases.

Since the creation of the National Committee for Health Technology Incorporation in the Brazilian Health System (CONITEC), a new phase started in the public Brazilian Health System (SUS): a continuous updating of the system based on Health Technology Assessment (HTA). CONITEC was created by federal law in 2012 and is responsible for advising the Ministry of Health regarding the incorporation or disinvestment of health technologies. The whole process involves a strong interaction with society, including the composition of the committee, which has the participation of the National Health Council. The objective of this study was to describe the results of CONITEC in five years of operation.

This is a retrospective descriptive study, based on information from the database (period 2012–2016) and CONITEC's website.

Since 2012, CONITEC assessed 541 technologies, including drugs (360), health products (71) and procedures (110); 303 assessment requests came from SUS agencies and institutions and the other 238 requests from pharmaceutical companies, medical societies, patient associations and the judiciary bodies. In this period, there were 190 public consultations, during which more than 24,000 feedback from society were received. The average time for evaluation was 146 days. The committee recommended the incorporation of 186 technologies into SUS, the disinvestment of 43 and was unfavorable to the incorporation of 88, generating a budgetary impact of approximately BRL2.5 billion (USD764 million).

From 2012–2016, CONITEC tripled the average annual incorporation of new technologies compared to the period 2006–2011. In this process, it was necessary to assess efficacy, safety and cost-effectiveness of technologies, generating positive results for the expansion of access, health gains for patients and sustainability for the system. It should be considered that the use of evidence for decision making strengthens transparency in public management and the development of active processes of information, communication and social participation.